CDC is considering a shift to its Covid-19 isolation guidance to say that people no longer need to isolate once they have been fever-free for 24 hours and their symptoms are mild or improving.

CDC isolation guidelines for Covid-19 haven’t changed yet. Here’s what you need to know

We recently shared an article by The Washington Post reporting that the Centers for Disease Control and Prevention (CDC) "plans to drop five-day covid isolation guidelines." The WaPo piece stated: Americans who test positive…

CDC says there are "no updates to COVID guidelines to announce at this time" - Boing Boing

Guideline

guideline

One of the preparatory stages of design development for any project is always the stage where the basic rules are laid down. Depending on the scale of the project, these may be some guidelines (if it is a small project involving one designer, such as a landing page), or large-scale design systems that combine several interfaces, in the work with which a group of designers participates. Regardless of the project, implementing these rules allows you to achieve consistency throughout the project, making it easier for the user to understand the interface. Also, a system of stable rules organizes all design elements and significantly speeds up the process at advanced stages; you will not have to design each individual element during scaling. It will be enough to select the necessary component and its state. Next, I will describe the basic set for a small project and the implementation process. Okay, let's start with the task. First, let's see what we have. Do we (or the design customer) have any guidelines? This could be just a logo drawn with a pen on paper. For some, it is a link to some color palette; for someone else, it is already their own corporate guideline, which already contains information about some accepted design elements. That is, there is always some starting point. If there is nothing at all, no problem, this is also a starting point. Often, even more advantageous. Let's assume that we are exactly in it. This means that we are currently limited exclusively by our imagination. Great! The main thing is not to spoil anything. To do this, you need to tune in, anything will do, the main thing is to be well inspired by something. Let's assume, however, that we already have a logo and such processes as benchmarking and elaboration of the central idea of ​​the design have been completed. Then, we boldly create our new cover and can even announce the launch of our latest project in all social networks. Choose a font if it is not regulated by the brand book. Do not underestimate this task. The font must match the brand. Plus, it is not always possible to painlessly change the font at the layout stage. In short, there are a lot of resources on the Internet for selecting a font and even a font pair. So, it is worth collecting several options and evaluating how it will look in the environment, on several screens. Then it is worth choosing a more suitable option, taking a break, and looking again the next morning. If there are no doubts, then this is a good option, if something confuses you, it is worth digging around at this stage. Having chosen the appropriate font, we form the text styles, thus, laying the foundation of the design system. There are several principles here. Everything should always be a multiple of 8 px. There is no way around it, especially if the indentation system and grid are also a multiple of 8 px. Then the logic is as follows - select several font sizes for the first screen. For this, again, we use a beautiful screen, a slogan, and see how it works. Then we determine the font sizes for the h1-h4 headings and at least two font sizes for the t1-t2 text, or better four t1-t4. We understand that the projects have the main text, various descriptions in cards, decorative elements, information about rights, etc., and with all this, we need to achieve a good level of hierarchy so that the user does not get tired when scanning the text and quickly finds important information for himself. Well, and then all the same is repeated for different styles. I have six of them in the project from L to EB. This whole process takes some time, especially at first, and I agree that there are plugins that allow you to save this time. But there are also advantages if you do everything yourself. Firstly, you will better understand the library of your styles, and it will be easier for you to choose the right style. Secondly, your library will be customized as much as possible. Working with a color palette. I already had a color palette, but to make it easier to work with the project, it is worth preparing shades for this palette in advance. In this particular project, I decided to use Radix, because this resource helps achieve an even greater level of consistency and reduces your time. Here, you can get not just a set of shades, but 12 different shades at once that are tied to individual design elements (text, buttons in different states, dividers, etc.). Plus, in one click, you can get a palette for the light and dark theme of your interface. Next, we load the resulting palette into Local variables. You can make several mods right here (light, dark, multi-colored, as many as you like). So, that in the future, by setting a certain color to different elements of the frame, you can change the color scheme of this frame in one click. This operation significantly saves time at later stages of working with the design. Also, in Local variables, it is worth immediately entering the values ​​of the rounding radii and indents. As usual, everything is a multiple of 8 px. For the radii, I make 5 values ​​from XS to XL; more is possible. Smaller values ​​are used for the smallest elements, such as buttons, and larger ones are used for graphics. This is worth doing so that, during the work process, you do not have to go back and look for the logic of decision-making at an earlier stage. With indents, the principle is the same, but there are more values, I set 13. It looks like this. When working with auto layouts, you do not need to remember the values, for example, of indents or paddings that were used earlier; they are simply pulled from the library. Let's move on to icons. For my project, I decided to use the following types of icons: special and basic. I drew the special icons, but they needed to be made more detailed. Basic icons can be found in the community. Think in advance about where these icons can be used and choose a suitable set. I planned to use them in buttons, cards, forms, etc. There is no point in overloading the library with irrelevant elements. At the moment, we have ready-made basic elements of our design guidelines: typography;
color palette;
rounding system;
indentation system;
set of icons;
so there were logos for various formats and color schemes; Plus I prepared grid styles. Now, it's time to move on to more complex components, consisting of basic ones - these are buttons, various forms, cards, header, footer, etc. But this is a topic for the next part of the tutorial. One of the preparatory stages of design development for any project is always the stage where the basic rules are laid down. Depending on the scale of the project, these may be some guidelines (if it is a small project involving one designer, such as a landing page), or large-scale design systems that combine several interfaces, in the work with which a group of designers participates. Regardless of the project, implementing these rules allows you to achieve consistency throughout the project, making it easier for the user to understand the interface. Also, a system of stable rules organizes all design elements and significantly speeds up the process at advanced stages; you will not have to design each individual element during scaling. It will be enough to select the necessary component and its state. Next, I will describe the basic set for a small project and the implementation process. Okay, let's start with the task. First, let's see what we have. Do we (or the design customer) have any guidelines? This could be just a logo drawn with a pen on paper. For some, it is a link to some color palette; for someone else, it is already their own corporate guideline, which already contains information about some accepted design elements. That is, there is always some starting point. If there is nothing at all, no problem, this is also a starting point. Often, even more advantageous. Let's assume that we are exactly in it. This means that we are currently limited exclusively by our imagination. Great! The main thing is not to spoil anything. To do this, you need to tune in, anything will do, the main thing is to be well inspired by something. Let's assume, however, that we already have a logo and such processes as benchmarking and elaboration of the central idea of ​​the design have been completed. Then, we boldly create our new cover and can even announce the launch of our latest project in all social networks. Choose a font if it is not regulated by the brand book. Do not underestimate this task. The font must match the brand. Plus, it is not always possible to painlessly change the font at the layout stage. In short, there are a lot of resources on the Internet for selecting a font and even a font pair. So, it is worth collecting several options and evaluating how it will look in the environment, on several screens. Then it is worth choosing a more suitable option, taking a break, and looking again the next morning. If there are no doubts, then this is a good option, if something confuses you, it is worth digging around at this stage. Having chosen the appropriate font, we form the text styles, thus, laying the foundation of the design system. There are several principles here. Everything should always be a multiple of 8 px. There is no way around it, especially if the indentation system and grid are also a multiple of 8 px. Then the logic is as follows - select several font sizes for the first screen. For this, again, we use a beautiful screen, a slogan, and see how it works. Then we determine the font sizes for the h1-h4 headings and at least two font sizes for the t1-t2 text, or better four t1-t4. We understand that the projects have the main text, various descriptions in cards, decorative elements, information about rights, etc., and with all this, we need to achieve a good level of hierarchy so that the user does not get tired when scanning the text and quickly finds important information for himself. Well, and then all the same is repeated for different styles. I have six of them in the project from L to EB. This whole process takes some time, especially at first, and I agree that there are plugins that allow you to save this time. But there are also advantages if you do everything yourself. Firstly, you will better understand the library of your styles, and it will be easier for you to choose the right style. Secondly, your library will be customized as much as possible. Working with a color palette. I already had a color palette, but to make it easier to work with the project, it is worth preparing shades for this palette in advance. In this particular project, I decided to use Radix, because this resource helps achieve an even greater level of consistency and reduces your time. Here, you can get not just a set of shades, but 12 different shades at once that are tied to individual design elements (text, buttons in different states, dividers, etc.). Plus, in one click, you can get a palette for the light and dark theme of your interface. Next, we load the resulting palette into Local variables. You can make several mods right here (light, dark, multi-colored, as many as you like). So, that in the future, by setting a certain color to different elements of the frame, you can change the color scheme of this frame in one click. This operation significantly saves time at later stages of working with the design. Also, in Local variables, it is worth immediately entering the values ​​of the rounding radii and indents. As usual, everything is a multiple of 8 px. For the radii, I make 5 values ​​from XS to XL; more is possible. Smaller values ​​are used for the smallest elements, such as buttons, and larger ones are used for graphics. This is worth doing so that, during the work process, you do not have to go back and look for the logic of decision-making at an earlier stage. With indents, the principle is the same, but there are more values, I set 13. It looks like this. When working with auto layouts, you do not need to remember the values, for example, of indents or paddings that were used earlier; they are simply pulled from the library. Let's move on to icons. For my project, I decided to use the following types of icons: special and basic. I drew the special icons, but they needed to be made more detailed. Basic icons can be found in the community. Think in advance about where these icons can be used and choose a suitable set. I planned to use them in buttons, cards, forms, etc. There is no point in overloading the library with irrelevant elements. At the moment, we have ready-made basic elements of our design guidelines: typography; color palette; rounding system; indentation system; set of icons; so there were logos for various formats and color schemes; typography; color palette; rounding system; indentation system; set of icons; so there were logos for various formats and color schemes; Plus I prepared grid styles. Now, it's time to move on to more complex components, consisting of basic ones - these are buttons, various forms, cards, header, footer, etc. But this is a topic for the next part of the tutorial.

The Implementation of Design Guidelines for Small and Medium Projects

Authors:
(1) TIMNIT GEBRU, Black in AI;
(2) JAMIE MORGENSTERN, University of Washington;
(3) BRIANA VECCHIONE, Cornell University;
(4) JENNIFER WORTMAN VAUGHAN, Microsoft Research;
(5) HANNA WALLACH, Microsoft Research;
(6) HAL DAUMÉ III, Microsoft Research; University of Maryland;
(7) KATE CRAWFORD, Microsoft Research. Table of Links 1 Introduction 1.1 Objectives 2 Development Process 3 Questions and Workflow 3.1 Motivation 3.2 Composition 3.3 Collection Process 3.4 Preprocessing/cleaning/labeling 3.5 Uses 3.6 Distribution 3.7 Maintenance 4 Impact and Challenges Acknowledgments and References Appendix 3.6 Distribution Dataset creators should provide answers to these questions prior to distributing the dataset either internally within the entity on behalf of which the dataset was created or externally to third parties. • Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? If so, please provide a description. • How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? Does the dataset have a digital object identifier (DOI)? • When will the dataset be distributed? • Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? If so, please describe this license and/or ToU, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions. • Have any third parties imposed IP-based or other restrictions on the data associated with the instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions. • Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation. • Any other comments? This paper is available on arxiv under CC 4.0 license. Authors: (1) TIMNIT GEBRU, Black in AI; (2) JAMIE MORGENSTERN, University of Washington; (3) BRIANA VECCHIONE, Cornell University; (4) JENNIFER WORTMAN VAUGHAN, Microsoft Research; (5) HANNA WALLACH, Microsoft Research; (6) HAL DAUMÉ III, Microsoft Research; University of Maryland; (7) KATE CRAWFORD, Microsoft Research. Authors: Authors: (1) TIMNIT GEBRU, Black in AI; (2) JAMIE MORGENSTERN, University of Washington; (3) BRIANA VECCHIONE, Cornell University; (4) JENNIFER WORTMAN VAUGHAN, Microsoft Research; (5) HANNA WALLACH, Microsoft Research; (6) HAL DAUMÉ III, Microsoft Research; University of Maryland; (7) KATE CRAWFORD, Microsoft Research. Table of Links 1 Introduction 1 Introduction 1.1 Objectives 1.1 Objectives 2 Development Process 2 Development Process 3 Questions and Workflow 3 Questions and Workflow 3.1 Motivation 3.1 Motivation 3.2 Composition 3.2 Composition 3.3 Collection Process 3.3 Collection Process 3.4 Preprocessing/cleaning/labeling 3.4 Preprocessing/cleaning/labeling 3.5 Uses 3.5 Uses 3.6 Distribution 3.6 Distribution 3.7 Maintenance 3.7 Maintenance 4 Impact and Challenges 4 Impact and Challenges Acknowledgments and References Acknowledgments and References Appendix Appendix 3.6 Distribution Dataset creators should provide answers to these questions prior to distributing the dataset either internally within the entity on behalf of which the dataset was created or externally to third parties. • Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? If so, please provide a description. • Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? • How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? Does the dataset have a digital object identifier (DOI)? • How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? Does the dataset have a digital object identifier (DOI)? • When will the dataset be distributed? • When will the dataset be distributed? • Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? If so, please describe this license and/or ToU, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions. • Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? • Have any third parties imposed IP-based or other restrictions on the data associated with the instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions. • Have any third parties imposed IP-based or other restrictions on the data associated with the instances? • Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation. • Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? • Any other comments? • Any other comments? This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Guidelines for Sharing AI Datasets Responsibly

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Jakob Vogel, M.A. Digital Humanities, Institute for Digital Humanities, Faculty of Philosophy, Georg August University of G¨ottingen. Table of Links Abstract and Intro
Diverse cross-document coreference and media bias analysis
Annotation tool
Annotation guidelines
Conclusion and future work
Acknowledgements
Bibliographical References 5. Conclusion and future work Our proposed annotation scheme covers a multitude of coreferential relations. It gives a detailed explanation of how to mark coreferential mentions across documents, assign entity-types and names to them, connect them with each other, and link them to the Wikidata knowledge graph. The scheme thus represents a significant step toward more accurately capturing the complexities of coreference use. It furthermore provides a valuable resource for researchers both in the field of coreference resolution and media bias by word-choice and labelling. Having said that, our scheme leaves room for possible extensions to further advance research in those domains. First, the annotation of events could be included in our scheme. An interesting question that arises is whether the relationtypes as outlined here could be applied not only to entities, but to events all the same. A second possible extension would be to include a layer of media bias annotation to the scheme, enabling a direct comparison of diverse coreference usage and media bias by word-choice and labelling. Both proposed extensions could be easily added on top of our scheme. Having said that, the present form of our scheme already addresses many of the complexities of diverse cross-document coreference and offers a roadmap for capturing nuanced linguistic relationships, ultimately advancing our understanding of language and discourse in digital print media. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jakob Vogel, M.A. Digital Humanities, Institute for Digital Humanities, Faculty of Philosophy, Georg August University of G¨ottingen. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Jakob Vogel, M.A. Digital Humanities, Institute for Digital Humanities, Faculty of Philosophy, Georg August University of G¨ottingen. Table of Links Abstract and Intro Diverse cross-document coreference and media bias analysis Annotation tool Annotation guidelines Conclusion and future work Acknowledgements Bibliographical References Abstract and Intro Abstract and Intro Diverse cross-document coreference and media bias analysis Diverse cross-document coreference and media bias analysis Annotation tool Annotation tool Annotation guidelines Annotation guidelines Conclusion and future work Conclusion and future work Acknowledgements Acknowledgements Bibliographical References Bibliographical References 5. Conclusion and future work Our proposed annotation scheme covers a multitude of coreferential relations. It gives a detailed explanation of how to mark coreferential mentions across documents, assign entity-types and names to them, connect them with each other, and link them to the Wikidata knowledge graph. The scheme thus represents a significant step toward more accurately capturing the complexities of coreference use. It furthermore provides a valuable resource for researchers both in the field of coreference resolution and media bias by word-choice and labelling. Having said that, our scheme leaves room for possible extensions to further advance research in those domains. First, the annotation of events could be included in our scheme. An interesting question that arises is whether the relationtypes as outlined here could be applied not only to entities, but to events all the same. A second possible extension would be to include a layer of media bias annotation to the scheme, enabling a direct comparison of diverse coreference usage and media bias by word-choice and labelling. Both proposed extensions could be easily added on top of our scheme. Having said that, the present form of our scheme already addresses many of the complexities of diverse cross-document coreference and offers a roadmap for capturing nuanced linguistic relationships, ultimately advancing our understanding of language and discourse in digital print media.

Code Book for Annotation of Diverse Cross-Document Coreference: Annotation Guidelines

USA v. Samuel Bankman-Fried Court Filing, retrieved on March 15, 2024 is part of . You can jump to any part in this filing . This part is 28 of 33. HackerNoon’s Legal PDF Series here V. A Sentence of 40 to 50 Years Is Necessary to Avoid Sentencing Disparities Among the factors a sentencing court must consider in imposing sentence is “the need to avoid unwarranted sentence disparities among defendants with similar records who have been found guilty of similar conduct.” 18 U.S.C. § 3553(a)(6). Congress adopted Section 3553(a)(6) “to eliminate unwarranted disparities nationwide.” United States v. Williams, 524 F.3d 209, 215 (2d Cir. 2008). No scheme to defraud since Madoff can be compared to this one in terms of its size, scope, and amount of loss. The PSR notes that sentencing data from the last five years shows that the average sentence for a defendant sentenced under the fraud Guidelines with offense level (43) is approximately 24 years’ imprisonment. The United States Sentencing Commission’s Judiciary Sentencing Information database does not indicate, however, what conduct resulted in these sentences. Nor does it indicate what the largest sentences were. Because of the range of potential white collar crimes that fall under the Guidelines in Section 2B1.1, the Government submits that a more focused approach is necessary to avoid unwanted sentencing disparities. Specifically, the Government submits that other fraud cases where the loss amounts were at the top of the loss table—and in particular, those involving embezzlement—is the relevant body of cases to evaluate. Below is a table of sentences imposed since Booker on defendants where the loss amount exceeded $100 million, and the money was stolen through a Ponzi scheme or some other form of misappropriation or embezzlement. As the table makes clear, most defendants who steal their clients’ or customers’ money with $100 million dollars in losses or more receive sentences ranging from 40 to 50 years’ imprisonment. Indeed, in cases with $200 million or more in losses, courts have typically imposed a sentence of 50 years imprisonment or more. For instance, in a case with similar facts to those here, Russell Wasendorf, the CEO of a futures commission merchant, embezzled more than $215 million in customers’ funds, and was sentenced to 50 years’ imprisonment. There are exceptions, of course, but it also bears noting that the defendant’s fraud would rank second, right below Madoff, in terms of total losses. Accordingly, to avoid unwarranted sentencing disparities amongst like defendants, a sentence of 40 to 50 years’ imprisonment is necessary. The defendant’s submission cites and then distinguishes several other fraud prosecutions involving large losses. Starting with Madoff, the defendant says it is not an apt comparison because Madoff embezzled more money, engaged in fraud from the beginning, was older, and used millions in stolen funds for himself. (Def. Mem. at 75). Bankman-Fried is right that there are differences between his and Madoff’s case, but those do not justify disregarding the Madoff sentence as a benchmark. Like Madoff, the defendant misappropriated billions of dollars of customer money over a multi-year period, spending much of it on his own ventures, and causing massive harm to his victims. United States v. Madoff, Nio. 09 Cr. 213 (DC), Dkt. 92 (Gov’t Sentencing Mem.). And unlike Madoff, Bankman-Fried has not accepted responsibility; rather, he obstructed justice, he perjured himself, and he committed other significant crimes. The defendants’ ages are certainly a distinguishing factor. Age, of course, cuts both ways. A shorter sentence because the defendant is relatively young means he will have an opportunity to commit fraud again when released. And a shorter sentence because of his age also undermines general deterrence, as it could signal to would be young fraudsters that it is worthwhile to attempt a massive fraud, because either they will get away with it, or the prison sentence will allow them a second chance to make money. Even if age were a reason why this Court should impose a shorter sentence than the one imposed on Madoff— a sentence that gives Bankman-Fried the possibility of release someday—it is not a reason to disregard the Madoff sentence given the similarities to the case. Next, the defendant distinguishes Judge Ramos’s recent 20-year sentence of Karl Greenwood, who was involved in the fake cryptocurrency scheme OneCoin. (Def. Mem. at 76- 77). That sentence is distinguishable, but not for the reasons in the defendant’s brief. Greenwood received a sentence shorter than what Bankman-Fried deserves, not a longer one. Greenwood pled guilty and was remorseful for his conduct. At sentencing, Judge Ramos stated that while the defendant’s criminal conduct “compelled” a substantial sentence, there were “substantial reasons” for granting “some measure of lenity, including “horrific conditions” that the defendant endured in a Thailand jail. United States v. Greenwood, No. 17 Cr. 630 (ER) (S.D.N.Y. Sept. 12, 2023), Sent. Tr. at 39. Those conditions included being “stripped naked,” “shaved down,” “chained to other inmates for 24 hours,” “forced to sleep on the floor full of cockroaches and rodents,” “food filled with magots,” “forced to sit outside in the heat for hours,” and some particular circumstances, which are under seal, that “resulted in lasting health effects and health consequences.” (Id. at 18- 19).[13] The particular circumstances of Greenwood’s case distinguish it, and make it an imperfect precedent for the sentence here. Bankman-Fried argues that the sentences imposed on defendants from the public company scandals from the early 2000s are poor comparisons. (Def. Mem. at 77). The Government agrees. Bernard Ebbers of WorldCom and John Rigas of Adelphia Communications were convicted of perpetrating large accounting frauds that resulted in losses to the companies’ investors. These were securities fraud cases, but the nature of the losses was different: the losses did not involve the theft of customers’ or clients’ funds that were entrusted to the defendant. Rather, in both cases, there was ambiguity as to whether the companies’ losses were fully attributable to the fraud. United States v. Ebbers, 458 F.3d 110, 127 (2d Cir. 2006) (“The loss to investors who hold during the period of an ongoing fraud is not easily quantifiable because we cannot accurately assess what their conduct would have been had they known the truth,” and recognizing that “the dot-com bubble burst and its likely negative future effect on WorldCom’s business was public knowledge,” which caused “a downward pressure on share price not attributable to the defendant.”); United States v. Rigas, 583 F.3d 108, 119–20 (2d Cir. 2009) (recognizing that “[t]he loss must be the result of the fraud, as opposed to other, non-fraudulent occurrences”) (citation omitted). That distinguishing feature is critical because, as the above table indicates, courts have routinely imposed lengthier sentences on defendants who have stolen from people who trusted them. Bankman-Fried acknowledges similarities to Elizabeth Holmes. (Def. Mem. at 78-79). But the similarities are superficial. Bankman-Fried stole billions from customers who entrusted him with money. The loss amount in Holmes’s case was less than $150 million, and those losses were borne by private investors, not the general public. United States v. Holmes, 18 Cr. 258 (N.D. Cal.), Dkt. 1712 (Court Order on Sentencing), at 1 (“the loss in this case can be reasonably found to be $120,146,247”), 6 (list of investors). The defendant claims that Holmes’s conduct was worse because her scheme resulted in parties receiving inaccurate medical information. But BankmanFried caused a similar type of trauma to his victims, but at a grander scale. He led victims to believe their money was safe and in accounts, to be used as needed. As the witness statements above make clear, people put their life savings in the defendant’s hands. So like the Theranos patient who got inaccurate medical results and was traumatized, the FTX customers have experienced devastating emotional and psychological trauma from the defendant’s fraud. The difference is, however, that the Theranos patients ultimately learned that the tests were junk and were able to right their lives. The same is not true for FTX’s customers, who have not gotten their money back. Finally, the defendant settles on Michael Milken as the “best comparison” for this case. There is very little about the defendant’s fraud that is any way like the crimes for which Milken was prosecuted. Although Milken was associated with Drexel Burnham’s leveraged buyouts and “junk bonds,” the charges to which Milken pled guilty had nothing to do with those practices; nor did they involve insider trading associated with Ivan Boesky. While Milken pled guilty to six offenses, they related to the following: Milken guaranteed Boesky against loss and helped him evade the net-capital rules in some transactions, he made adjustments in the price of securities bought and sold for another client without disclosing this to the fund’s customers, and he helped the same client lower his takes by arranging unprofitable trades. Milken’s convictions neither involved theft of customers’ funds, nor did they involve anything close to the amount of losses sustained here. Milken’s sentence is not a fair comparison for the conduct here. Arguing otherwise, Bankman-Fried cites to personal similarities to Milken—they were both young, had a “meteoric rise” in their industry, were “geniuses,” and had an interest in charity. (Def. Mem. at 81-82). But Section 3553 requires consideration of whether a sentence will create an unwarranted sentence disparity among defendants “who have been found guilty of similar conduct,” not defendants who may or may not be similar. Milken did not steal customers’ money. He did not steal over $10 billion dollars. He did not perpetrate multiple frauds with losses in the billions. He did not refuse to accept responsibility, obstruct justice, and perjure himself. A sentence equivalent to the one imposed on Milken of 10 years (let alone the two-year sentence he later received based on his cooperation) would be unreasonable here given the facts. At bottom, defendants who are in a position of trust and steal $100 million or more appropriately are sentenced to 40 to 50 years, or more. Such a sentence is necessary here and would be in line with the sentences meted out to other defendants who engaged in similar conduct. Continue Reading . Here [13] The specifics of these circumstances are sealed. If the Court believes the details of those circumstances are necessary in determining how, if at all, to consider the sentence in Greenwood, the Government will provide the information to the Court on a sealed basis. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 15, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener

Understanding Sentencing Guidelines in White Collar Crimes

USA v. Samuel Bankman-Fried Court Filing, retrieved on March 15, 2024 is part of . You can jump to any part in this filing . This part is 22 of 33. HackerNoon’s Legal PDF Series here III. The Guidelines Do Not Overstate the Seriousness of the Offense Bankman-Fried argues that the fraud Guidelines are not based on empirical evidence and “substantially overstate the seriousness of the offense.” (Def. Mem. at 28). This argument is misguided for several reasons: the current fraud Guideline is the result of extensive and iterative work by the Sentencing Commission, and is based on sound policy choices, and even if the fraud Guidelines were overly reliant on loss as a general matter—a point the Government does not concede—they are an appropriate measure of the seriousness of the offense in this case. “One of the central reasons for creating the sentencing guidelines was to ensure stiffer penalties for white-collar crimes and to eliminate disparities between white-collar sentences and sentences for other crimes.” United States v. Davis, 537 F.3d 611, 617 (6th Cir. 2008). “[T]he sentencing guidelines sought to address the inequities of prior sentencing practices that tended to punish white collar economic crimes less severely than other comparable blue collar offenses.” Fishman, 631 F. Supp. 2d at 403. Over a five-year period between 1996 and 2001, the Commission engaged in a deliberative process to address the Guidelines’ treatment of white-collar offenses, with the involvement of relevant stakeholders including the defense bar, the Department of Justice, probation officers, and the U.S. Judicial Conference. See Federal Register Notice BAC2210-40, 62 Fed. Reg. 152, 171-74 (1997) (proposals by the Commission for comment regarding economic crime sentencing reform). The Commission explained that the resulting amendments, which became effective on November 1, 2001, were based on the determination that “loss serves as a measure of the seriousness of the offense and the defendant’s relative culpability.” Sentencing Guidelines for the United States Courts, 66 Fed. Reg. 30,512, 30,533 (June 6, 2001). Thus, the 2001 amendments to Section 2B1.1 reflected the considered view of the Commission, following a collaborative process with relevant stakeholders, that loss amount should be a central consideration in determining the seriousness of an offense to which that Guideline applies. Indeed, “[e]ven if the enhancements may lack robust empirical support related to deterrence, they have foundations in empirical data and national experience related to the goals of fair sentencing and retribution.” United States v. Moose, 893 F.3d 951, 958 (7th Cir. 2018). In 2002, following major corporate fraud at Enron, WorldCom, and Adelphia, Congress instructed the Commission to amend the Guidelines for white-collar crime again, this time as part of the Sarbanes-Oxley Act, and in response the Commission amended the modified loss-amount enhancement in Section 2B1.1 for high-loss cases — that is, cases involving loss amounts in excess of $200 million. See U.S. Sentencing Comm’n, Emergency Guidelines Amendments, 15 Fed. Sentencing Reporter 281, 283 (Apr. 1, 2003) (“[T]he amendment expands the loss table at §2B1.1(b)(1) to punish adequately offenses that cause catastrophic losses of magnitudes previously unforeseen, such as the serious corporate scandals that gave rise to several portions of the Act.”), available at 2003 WL 22016909. Thus, to the extent Section 2B1.1 emphasizes loss amount to the degree it does, that was an intentional decision by the Commission based on years of consideration and guidance from Congress. See Ebbers, 458 F.3d at 129 (“[T]he Guidelines reflect Congress’s judgment as to the appropriate national policy for [white collar] crimes.”). The few district court opinions cited by Bankman-Fried seem not to have considered the Commission and legislative history. See, e.g., United States v. Johnson, No. 16 Cr. 457 (NGG), 2018 WL 1997975, at *3 (E.D.N.Y. Apr. 27, 2018) (cited by Bankman-Fried for the incorrect proposition that “as far as [that] court can tell” the loss guidelines were not the “result from any reasoned determination”). The defendant’s argument otherwise draws on language from Judge Rakoff’s decision in United States v. Adelson, 441 F. Supp. 2d 506, 509 (S.D.N.Y. 2006), Judge Block’s decision in United States v. Parris, 573 F. Supp. 2d 744, 753 (E.D.N.Y. 2008), and Judge Underhill’s concurrence in United States v. Corsey, 723 F.3d 366, 379 (2d Cir. 2013), which quotes from Adelson. All of those cases involved extremely lengthy sentencing ranges recommended by the Sentencing Guidelines for defendants who personally had no participation in causing any actual loss. For example, “Adelson was not the originator of the fraud, and, as the jury found in effect, Adelson did not participate in the fraudulent conspiracy until its final months,” during which time the company’s “stock was not further inflated.” Adelson, 441 F. Supp. 2d at 513. In other words, “Adelson was closer (though not identical) to an accessory after the fact.” Id. As a result, Judge Rakoff recognized that the Guidelines range of 85 years in prison could not account for the nuanced way in which the defendant participated in the scheme and resulted in a patently unjust advisory sentencing range. Id. Even after taking that factor into account, though, Judge Rakoff noted the importance of considering general deterrence in financial-fraud cases and imposed a three-and-ahalf-year sentence: “notwithstanding all the mitigating factors outlined above, meaningful prison time was necessary to achieve retribution and general deterrence.” Id. at 514. Adelson thus advocates implementing a meaningful custodial sentence in fraud cases, even when “it was undisputed at the time of sentencing that [a defendant’s] past history was exemplary.” Id. at 513. In Parris, the defendants were engaged in a two-month “pump and dump” scheme in the penny stock market that resulted in approximately $2,500,000 in losses among hundreds of victims who traded the stock. 573 F. Supp. 2d at 746-49. As a result of their brief penny-stock fraud, even with no criminal history, the defendants faced an advisory Guidelines range starting at 30 years’ imprisonment. Judge Block found this crime with that Guidelines range “not of the same character and magnitude as the securities-fraud prosecutions of those who have been responsible for wreaking unimaginable losses on major corporations.” Id. at 746. The district court asked the government and the defendants to provide the court with a compendium of factually analogous cases nationally. Id. at 751-52. After considering those cases, the court ultimately sentenced the defendants to 60 months’ imprisonment. Corsey presented a similarly unusual situation warranting a variance. In Corsey, the defendants were prosecuted for trying to carry out an outlandish scheme against, what turned out to be, an FBI informant. “This was a clumsy, almost comical, conspiracy to defraud a nonexistent investor of three billion dollars. That scheme never came close to fruition.” Corsey, 723 F.3d at 378. “This scheme amounted to a series of absurd lies piled on top of even more absurd lies.” Id. at 379. However, the defendants in Corsey immediately faced a Guidelines calculation that was above the statutory maximum of 20 years and close to life in prison. It is in this context that Judge Underhill, in a concurring opinion, found the Guidelines to be of “low marginal value” for the case at hand. But he did not say the guidelines were always of such little import. In fact, Underhill quoted Adelson in holding that in other instances, the Guidelines are “of considerable help to any judge in fashioning a sentence that is fair, just, and reasonable.” Id. at 380. Whatever merit there is to the argument that the Guidelines overstate the seriousness of the offense generally, this case is a particularly poor vehicle through which to make it. While reasonable minds can disagree about the precise workings of the fraud Guidelines and whether they are unduly driven by loss, no one would argue that the size and scope of a financial fraud should be unrelated to the resulting sentence. It stands to reason that a defendant who engages in a longer, bigger fraud involving more victims should be punished more harshly than a defendant who engages in a short-term scheme (as in Parris), only joins the scheme late (as in Adelson), or does not harm real victims (as in Corsey). Here, by any measure, the defendant’s crime was historic. By dollars, the loss amount was at least twenty times the top of the Guidelines range. And this was not merely the “loss amount,” it was the amount of money actually stolen from customers and lenders. Thus, this case is unlike the fraud in Adelson and other securities fraud cases, where the “ordinary measure of loss” is “the decline in the price of stock when the fraud is revealed.” 441 F. Supp. 2d at 506. Here, by contrast, the loss is simply the amount of money that the defendant stole from his victims as of the time that the fraud collapsed. In other words, Bankman-Fried fixed his own Guidelines range by misappropriating billions of dollars. Indeed, the Guidelines commentary suggests that in a case like this one, an upwards departure may be appropriate (although that is not what the Government seeks here). See U.S.S.G. § 2B1.1, cmt. n. 21(A) (upwards departure may be appropriate where the “offense caused or risked substantial non-monetary harm,” such as “psychological harm, or severe emotional trauma,” or where the “offense created a risk of substantial loss beyond the loss determined for purposes of subsection (b)(1), such as a risk of significant disruption of a national financial market”). Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 15, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener

Sam Bankman-Fried vs. Fraud Sentencing Guidelines

USA v. Samuel Bankman-Fried Court Filing, retrieved on March 15, 2024 is part of . You can jump to any part in this filing . This part is 14 of 33. HackerNoon’s Legal PDF Series here II. The Defendant’s Challenges to the Guidelines Calculation Lack Merit In his sentencing submission, the defendant makes several challenges to the Guidelines calculation set forth in the PSR. Specifically, he challenges the calculation of the loss amount, the enhancement for fraudulent action in the course of a bankruptcy proceeding, the enhancement for conduct outside of the United States, the gross receipts from a financial institution enhancement, the abuse of trust enhancement, and the obstruction of justice enhancement. While all of these objections lack merit, it bears noting that with the exception of his challenge to the loss amount, none of his other objections will affect the calculation of the total offense level, which would be 43 with or without those enhancements. However, even though the objections are immaterial to the calculation of the Guidelines, the defendant is wrong on the facts and the law, and in any event the Court should consider the conduct underlying the enhancement in imposing a sentence. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 15, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener

Sam Bankman-Fried Challenges Court's Sentencing Guidelines

USA v. Samuel Bankman-Fried Court Filing, retrieved on March 15, 2024 is part of . You can jump to any part in this filing . This part is 13 of 33. HackerNoon’s Legal PDF Series here I. Calculation of the Applicable Sentencing Guidelines The Probation Department calculated that the adjusted offense level subtotal is 56, which is reduced to a total offense level of 43 pursuant to Chapter 5, Part A, comment n.2, since the total offense level is in excess of level 43. (PSR ¶¶ 70-89). The Government agrees with the Probation Department’s conclusion that the total offense level is 43. The Government, however, submits that the adjusted offense level subtotal is 60, not 56: six levels should be added under U.S.S.G. § 2B1.1(b)(2) because the offense resulted in substantial financial hardship to 25 or more victims (rather than the 2-level enhancement proposed by Probation for at least one victim experiencing financial hardship). Besides that modification, the Government agrees with the Probation Department’s calculation, and submits that the Court should calculate the applicable Sentencing Guidelines as follows: • Counts One through Six are grouped for guideline calculation purposes because the offense level is determined largely on the basis of the total amount of harm or loss. U.S.S.G. § 3D1.2(d). These counts are grouped with Count Seven, the money laundering conspiracy, pursuant to U.S.S.G. § 3D1.2(c) and Application Note 6 to U.S.S.G. § 2S1.1, because the defendant was convicted of conspiring to launder the funds derived from the underlying wire fraud offense charged in Count One. Pursuant to U.S.S.G. § 3D1.3(a), since the counts are grouped, the offense level applicable to the group is the highest offense level of the counts in the groups, which is the offense level for money laundering conspiracy. The applicable guideline section for a violation of 18 U.S.C. § 1956(h) is U.S.S.G. § 2S1.1. Pursuant to U.S.S.G. § 2S1.1(a)(1), the guideline set forth in U.S.S.G. § 2B1.1 is used. Pursuant to U.S.S.G. § 2B1.1(a)(1), the base offense level is 6.[1] • Base Offense Level: The total loss to FTX’s customers was in excess of $8 billion; the total loss to FTX’s equity investors was in excess of $1 billion; and the total loss to Alameda’s lenders was in excess of $1 billion. Because the loss involved was $10 billion or more, which exceeds $550 million, an increase of 30 levels is required by U.S.S.G. § 2B1.1(b)(1)(P). • Loss Amount: The offense involved at least one million victims, and resulted in substantial financial hardship to 25 or more victims. First, as the PSR correctly indicates, the defendant’s offense involved over one million victims, and the trial evidence indicated that at least one victim, BlockFi, experienced substantial financial hardship as a result of the defendant’s conduct, declaring bankruptcy soon after FTX.com’s bankruptcy was revealed. (Tr. 1228). BlockFi’s bankruptcy is a sufficient basis to find that at least one victim experienced substantial hardship. See U.S.S.G. § 2B1.1, Application Note 4(F)(i)-(ii) (“substantial financial hardship” includes insolvency and filing for bankruptcy under Chapter 11). Accordingly, at a minimum, an increase of 2 levels is required by U.S.S.G. § 2B1.1(b)(2)(A). However, as described below, more than 25 individual customers experienced substantial hardship, losing their life savings on FTX. See U.S.S.G. § 2B1.1, Application Note 4(F)(i), (iii) (“substantial financial hardship” includes insolvency and suffering substantial loss of a retirement, education, or other savings or investment fund).[2] Accordingly, an increase of 6 levels is required by U.S.S.G. § 2B1.1(b)(2)(C). • Number of Victims: Because the offense involved a misrepresentation or other fraudulent action during the course of a bankruptcy proceeding, a 2-level increase is required by U.S.S.G. § 2B1.1(b)(9). • Fraudulent Action During Bankruptcy: Because the defendant deliberately located FTX outside of the United States to evade law enforcement or regulatory officials, because a substantial part of the fraudulent scheme was committed from outside of the United States, and because the offense otherwise involved sophisticated means, a 2-level increase is required by U.S.S.G. § 2B1.1(b)(10). • Conduct Outside of the United States and Sophisticated Means: Because the defendant derived more than $1 million in gross receipts from one or more financial institutions as a result of the offense, a 2-level increase is required by U.S.S.G. § 2B1.1(b)(17). • Gross Receipts from a Financial Institution: Because the defendant was convicted under 18 U.S.C. § 1956, a 2-level increase is required by U.S.S.G. § 2S1.1(b)(2)(B). • Conviction Under Section 1956: Because the defendant was involved in sophisticated money laundering, specifically through the use of offshore accounts and multiple layers of transactions, a 2-level increase is required by U.S.S.G. § 2S1.1(b)(2)(C). • Sophisticated Money Laundering: The defendant was the CEO of FTX, and through his decision-making authority, directed and led his employees to commit criminal activities as part of the fraud and money laundering conspiracies. As such, he was an organizer and leader of criminal activity that involved five or more participants or was otherwise extensive. Accordingly, a 4-level increase is required by U.S.S.G. § 3B1.1(a). • Role Adjustment: The defendant misappropriated customer deposits that had been entrusted to him and FTX as part of the wire fraud scheme. Therefore, he abused a position of private trust in a manner that significantly facilitated the commission of the offense; a 2-level increase is required by U.S.S.G. § 3B1.3. • Abuse of Trust Adjustment: The defendant willfully obstructed and impeded, and attempted to obstruct and impede, the administration of justice with respect to the investigation, this prosecution, and sentencing in several manners. First, as the PSR notes and as discussed above, the defendant instructed employees to set Signal messages to auto-delete to evade law enforcement scrutiny. (PSR ¶ 85). Second, as discussed above, on two occasions the defendant attempted to tamper with potential trial witnesses. Third, the defendant perjured himself when he testified which, as discussed herein, is a basis to impose the adjustment for obstruction of justice. Accordingly, a 2-level increase is required by U.S.S.G. § 3C1.1. • Obstruction of Justice Adjustment: Because at no time has the defendant clearly demonstrated acceptance of responsibility for the offense, there is no offense level decrease under U.S.S.G. § 3E1.1. • No Acceptance of Responsibility: Based on the foregoing, the adjusted offense subtotal is 60. Because any offense level in excess of 43 is treated as an offense level of 43, 43 is the total applicable offense level. (PSR ¶ 89). The defendant’s criminal history score is zero, which puts him in Criminal History Category I. (PSR ¶ 92). Based upon these calculations, Bankman-Fried’s advisory Guidelines imprisonment range is life. (PSR ¶ 129). However, because the statutorily authorized maximum sentence is 110 years’ imprisonment, which is less than life imprisonment, the applicable Guidelines sentence is 110 years’ (1,320 months) imprisonment. U.S.S.G. §§ 5G1.1(a), 5G1.2(d). Continue Reading . Here [1] The base offense level is not 7 because money laundering is not an offense referenced to U.S.S.G. § 2B1.1. [2] At the time the PSR was drafted, the Probation Department did not have the victim impact information that is the basis for an enhancement under this section of the Guidelines. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 15, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener Lead image . source

A Complete Breakdown of Sam Bankman-Fried's Criminal Offenses and Sentencing Guidelines

The Conventional Commits specification is a lightweight convention that provides a standardized format for writing commit messages. It defines a simple set of rules to structure commit messages in a consistent manner, making it easier to understand the purpose and context of each commit. It is based on the above anatomies of git. By addressing numerous “why”, “what” and “how” questions, this specification can enable an efficient understanding of past decisions and changes made in the codebase, even after months or years. They serve as an indirect wiki, offering valuable insights into the reasons behind code changes. According to this specification, a commit message should be structured as: <commit type>[optional scope]: <description>
[optional body]
[optional footer(s)] Categorization of Commit Types 1. Build This type is used for changes that affect the build system or external dependencies. For example, if you upgrade a library or modify build configurations, it falls under this category. build: update webpack configuration
build: update Hive Maven dependency to version 3 2. Chore Chore-type commits are for regular maintenance tasks, such as updating dependencies, package manager configurations, or other tasks that don’t modify the source code or affect the behavior of the application. chore: clean up unused dependencies from package.json 3. CI Commits related to continuous integration configurations or scripts. This includes changes to tools and processes used for automation and testing. ci: add vault stage to gitlab ci pipeline to fetch api secrets 4. Docs Commits that only involve documentation changes, such as updating README files, adding comments, or writing documentation for functions or classes. docs: add auth service instructions to README 5. Feat This type is for new features added to the project. If you introduce new functionality or capabilities, it’s considered a feature. feat: introduce real-time notifications for new messages 6. Fix Fix-type commits are for patches that resolve bugs or issues in the codebase. If the commit addresses a problem or bug, it falls under this category. fix: resolve issue with form submission not triggering validation 7. Performance Commits that improve the performance of the codebase without changing its external behavior. Optimization-related changes would be categorized as performance improvements. perf: optimize guest misconduct database search by reservation for faster retrieval 8. Refactor Refactoring commits involve modifications to the code that neither fix a bug nor add a feature. Refactoring aims to improve the code’s structure, readability, or maintainability without changing its external behavior. refactor: simplify validation logic in user registration 9. Revert Revert-type commits are used when reverting previous changes. It’s essential to mention the commit ID or title of the changes being reverted in the commit message. revert: revert changes made in commit abc123 10. Style Commits that are related to code style and formatting. This could include indentation changes, code reformatting, or renaming variables to follow coding conventions. style: format code according to linting rules 11. Test Commits that add or modify tests. This includes unit tests, integration tests, or any other kind of automated testing. test: add unit tests for authentication service 12. Breaking Change This is not a commit type but a section in the description as per conventional commit specification. This introduces changes that are not backward compatible with the existing codebase or API. In other words, it signifies modifications that could potentially break existing functionality or require adjustments in dependent code or configurations. feat: update API response format to include additional fields

This commit extends the API response format to include new fields for user profile information. 
The added fields include 'birthdate' and 'country', providing more comprehensive user data. This 
change enhances the functionality of the API and enables richer user experiences.

BREAKING CHANGE:
- The structure of the API response has been modified to include additional fields.
- Clients relying on the previous response format will need to be updated to accommodate the changes.
Another syntax for breaking change — https://www.conventionalcommits.org/en/v1.0.0/#commit-message-with--to-draw-attention-to-breaking-change Usage in Open Source One of the many open-source examples using conventional commit specification is angular. Link — angular/CONTRIBUTING.md <type>(<scope>): <short summary>
│ │ │
│ │ └─⫸ Summary in present tense. Not capitalized. No period at the end.
│ │
│ └─⫸ Commit Scope: animations|bazel|benchpress|common|compiler|compiler-cli|core|
│ elements|forms|http|language-service|localize|platform-browser|
│ platform-browser-dynamic|platform-server|router|service-worker|
│ upgrade|zone.js|packaging|changelog|docs-infra|migrations|
│ devtools
│
└─⫸ Commit Type: build|ci|docs|feat|fix|perf|refactor|test What Are the Advantages? Consistency: Helps maintain consistency and standardization across commits within the team. When everyone follows the same format, it becomes easier to understand the history of changes.


Semantic Meaning: By categorizing commits into types like feat, fix, docs, chore, etc., conventional commit messages provide semantic meaning to changes. This makes it easier to understand the nature of each commit at a glance.


Clarity: Encourages writing clear and descriptive commit messages, including information about the changes made, why they were made, and any associated issues or task identifiers.


Communication: Facilitates communication among team members by providing a structured way to convey information about code changes.


Automation: Enables the use of automated tools that can parse commit messages to generate release notes, changelogs, or perform other tasks based on commit history. What Are the Disadvantages? Inconsistency: Without guidelines, commit messages may vary widely in format and quality, making it harder to understand the history of changes.


Ambiguity: Lack of structure can lead to vague or incomplete commit messages, which may make it difficult for team members to understand the context or purpose of certain changes.


Difficulty in Tracking: Tracking changes becomes more challenging, especially when trying to understand why certain changes were made or when investigating issues.


Miscommunication: Poorly written commit messages can result in miscommunication or misunderstandings among team members.


Manual Processes: Tasks such as generating release notes or tracking changes may require more manual effort without structured commit messages. FAQ About Conventional Commit Recommended to go over this list — Conventional Commits Resources Conventional Commits Specification
Commitzen tool for the teams practices This blog was originally published on https://www.pranaybathini.com/2024/03/conventional-commit-specification.html The Conventional Commits specification is a lightweight convention that provides a standardized format for writing commit messages. It defines a simple set of rules to structure commit messages in a consistent manner, making it easier to understand the purpose and context of each commit. It is based on the above anatomies of git. By addressing numerous “why”, “what” and “how” questions, this specification can enable an efficient understanding of past decisions and changes made in the codebase, even after months or years. They serve as an indirect wiki, offering valuable insights into the reasons behind code changes. According to this specification, a commit message should be structured as: <commit type>[optional scope]: <description>
[optional body]
[optional footer(s)] <commit type>[optional scope]: <description>
[optional body]
[optional footer(s)] Categorization of Commit Types 1. Build This type is used for changes that affect the build system or external dependencies. For example, if you upgrade a library or modify build configurations, it falls under this category. build: update webpack configuration
build: update Hive Maven dependency to version 3 build: update webpack configuration
build: update Hive Maven dependency to version 3 2. Chore Chore-type commits are for regular maintenance tasks, such as updating dependencies, package manager configurations, or other tasks that don’t modify the source code or affect the behavior of the application. chore: clean up unused dependencies from package.json chore: clean up unused dependencies from package.json 3. CI Commits related to continuous integration configurations or scripts. This includes changes to tools and processes used for automation and testing. ci: add vault stage to gitlab ci pipeline to fetch api secrets ci: add vault stage to gitlab ci pipeline to fetch api secrets 4. Docs Commits that only involve documentation changes, such as updating README files, adding comments, or writing documentation for functions or classes. docs: add auth service instructions to README docs: add auth service instructions to README 5. Feat This type is for new features added to the project. If you introduce new functionality or capabilities, it’s considered a feature. feat: introduce real-time notifications for new messages feat: introduce real-time notifications for new messages 6. Fix Fix-type commits are for patches that resolve bugs or issues in the codebase. If the commit addresses a problem or bug, it falls under this category. fix: resolve issue with form submission not triggering validation fix: resolve issue with form submission not triggering validation 7. Performance Commits that improve the performance of the codebase without changing its external behavior. Optimization-related changes would be categorized as performance improvements. perf: optimize guest misconduct database search by reservation for faster retrieval perf: optimize guest misconduct database search by reservation for faster retrieval 8. Refactor Refactoring commits involve modifications to the code that neither fix a bug nor add a feature. Refactoring aims to improve the code’s structure, readability, or maintainability without changing its external behavior. refactor: simplify validation logic in user registration refactor: simplify validation logic in user registration 9. Revert Revert-type commits are used when reverting previous changes. It’s essential to mention the commit ID or title of the changes being reverted in the commit message. revert: revert changes made in commit abc123 revert: revert changes made in commit abc123 10. Style Commits that are related to code style and formatting. This could include indentation changes, code reformatting, or renaming variables to follow coding conventions. style: format code according to linting rules style: format code according to linting rules 11. Test Commits that add or modify tests. This includes unit tests, integration tests, or any other kind of automated testing. test: add unit tests for authentication service test: add unit tests for authentication service 12. Breaking Change This is not a commit type but a section in the description as per conventional commit specification. This introduces changes that are not backward compatible with the existing codebase or API. In other words, it signifies modifications that could potentially break existing functionality or require adjustments in dependent code or configurations. feat: update API response format to include additional fields

This commit extends the API response format to include new fields for user profile information. 
The added fields include 'birthdate' and 'country', providing more comprehensive user data. This 
change enhances the functionality of the API and enables richer user experiences.

BREAKING CHANGE:
- The structure of the API response has been modified to include additional fields.
- Clients relying on the previous response format will need to be updated to accommodate the changes.
Another syntax for breaking change — https://www.conventionalcommits.org/en/v1.0.0/#commit-message-with--to-draw-attention-to-breaking-change feat: update API response format to include additional fields

This commit extends the API response format to include new fields for user profile information. 
The added fields include 'birthdate' and 'country', providing more comprehensive user data. This 
change enhances the functionality of the API and enables richer user experiences.

BREAKING CHANGE:
- The structure of the API response has been modified to include additional fields.
- Clients relying on the previous response format will need to be updated to accommodate the changes.
Another syntax for breaking change — https://www.conventionalcommits.org/en/v1.0.0/#commit-message-with--to-draw-attention-to-breaking-change Usage in Open Source One of the many open-source examples using conventional commit specification is angular. Link — angular/CONTRIBUTING.md angular/CONTRIBUTING.md <type>(<scope>): <short summary>
│ │ │
│ │ └─⫸ Summary in present tense. Not capitalized. No period at the end.
│ │
│ └─⫸ Commit Scope: animations|bazel|benchpress|common|compiler|compiler-cli|core|
│ elements|forms|http|language-service|localize|platform-browser|
│ platform-browser-dynamic|platform-server|router|service-worker|
│ upgrade|zone.js|packaging|changelog|docs-infra|migrations|
│ devtools
│
└─⫸ Commit Type: build|ci|docs|feat|fix|perf|refactor|test <type>(<scope>): <short summary>
│ │ │
│ │ └─⫸ Summary in present tense. Not capitalized. No period at the end.
│ │
│ └─⫸ Commit Scope: animations|bazel|benchpress|common|compiler|compiler-cli|core|
│ elements|forms|http|language-service|localize|platform-browser|
│ platform-browser-dynamic|platform-server|router|service-worker|
│ upgrade|zone.js|packaging|changelog|docs-infra|migrations|
│ devtools
│
└─⫸ Commit Type: build|ci|docs|feat|fix|perf|refactor|test What Are the Advantages? Consistency: Helps maintain consistency and standardization across commits within the team. When everyone follows the same format, it becomes easier to understand the history of changes. Semantic Meaning: By categorizing commits into types like feat, fix, docs, chore, etc., conventional commit messages provide semantic meaning to changes. This makes it easier to understand the nature of each commit at a glance. Clarity: Encourages writing clear and descriptive commit messages, including information about the changes made, why they were made, and any associated issues or task identifiers. Communication: Facilitates communication among team members by providing a structured way to convey information about code changes. Automation: Enables the use of automated tools that can parse commit messages to generate release notes, changelogs, or perform other tasks based on commit history. Consistency: Helps maintain consistency and standardization across commits within the team. When everyone follows the same format, it becomes easier to understand the history of changes. Consistency : Helps maintain consistency and standardization across commits within the team. When everyone follows the same format, it becomes easier to understand the history of changes. Consistency Semantic Meaning: By categorizing commits into types like feat, fix, docs, chore, etc., conventional commit messages provide semantic meaning to changes. This makes it easier to understand the nature of each commit at a glance. Semantic Meaning : By categorizing commits into types like feat, fix, docs, chore, etc., conventional commit messages provide semantic meaning to changes. This makes it easier to understand the nature of each commit at a glance. Semantic Meaning Clarity: Encourages writing clear and descriptive commit messages, including information about the changes made, why they were made, and any associated issues or task identifiers. Clarity : Encourages writing clear and descriptive commit messages, including information about the changes made, why they were made, and any associated issues or task identifiers. Clarity Communication: Facilitates communication among team members by providing a structured way to convey information about code changes. Communication : Facilitates communication among team members by providing a structured way to convey information about code changes. Communication Automation: Enables the use of automated tools that can parse commit messages to generate release notes, changelogs, or perform other tasks based on commit history. Automation : Enables the use of automated tools that can parse commit messages to generate release notes, changelogs, or perform other tasks based on commit history. Automation What Are the Disadvantages? Inconsistency: Without guidelines, commit messages may vary widely in format and quality, making it harder to understand the history of changes. Ambiguity: Lack of structure can lead to vague or incomplete commit messages, which may make it difficult for team members to understand the context or purpose of certain changes. Difficulty in Tracking: Tracking changes becomes more challenging, especially when trying to understand why certain changes were made or when investigating issues. Miscommunication: Poorly written commit messages can result in miscommunication or misunderstandings among team members. Manual Processes: Tasks such as generating release notes or tracking changes may require more manual effort without structured commit messages. Inconsistency: Without guidelines, commit messages may vary widely in format and quality, making it harder to understand the history of changes. Inconsistency : Without guidelines, commit messages may vary widely in format and quality, making it harder to understand the history of changes. Inconsistency Ambiguity: Lack of structure can lead to vague or incomplete commit messages, which may make it difficult for team members to understand the context or purpose of certain changes. Ambiguity : Lack of structure can lead to vague or incomplete commit messages, which may make it difficult for team members to understand the context or purpose of certain changes. Ambiguity Difficulty in Tracking: Tracking changes becomes more challenging, especially when trying to understand why certain changes were made or when investigating issues. Difficulty in Tracking : Tracking changes becomes more challenging, especially when trying to understand why certain changes were made or when investigating issues. Difficulty in Tracking Miscommunication: Poorly written commit messages can result in miscommunication or misunderstandings among team members. Miscommunication : Poorly written commit messages can result in miscommunication or misunderstandings among team members. Miscommunication Manual Processes: Tasks such as generating release notes or tracking changes may require more manual effort without structured commit messages. Manual Processes : Tasks such as generating release notes or tracking changes may require more manual effort without structured commit messages. Manual Processes FAQ About Conventional Commit Recommended to go over this list — Conventional Commits Conventional Commits Resources Conventional Commits Specification Commitzen tool for the teams practices Conventional Commits Specification Conventional Commits Specification Commitzen tool for the teams practices Commitzen tool for the teams practices This blog was originally published on https://www.pranaybathini.com/2024/03/conventional-commit-specification.html https://www.pranaybathini.com/2024/03/conventional-commit-specification.html

Conventional Commit Specification Guidelines: What You Need to Know

Authors: (1) Dominic Petrak, UKP Lab, Department of Computer Science, Technical University of Darmstadt, Germany; (2) Nafise Sadat Moosavi, Department of Computer Science, The University of Sheffield, United Kingdom; (3) Ye Tian, Wluper, London, United Kingdom; (4) Nikolai Rozanov, Wluper, London, United Kingdom; (5) Iryna Gurevych, UKP Lab, Department of Computer Science, Technical University of Darmstadt, Germany. Table of Links Abstract & Introduction Related Work Datasets Examined Manual Error Type Analysis and Taxonomies Automatic Filtering for Potentially Relevant Dialogs Statistical Analysis Evaluation and Experiments Discussion Conclusion, Limitation, Acknowledgments, and References A Integrated Error Taxonomy – Details B Error-Indicating Sentences And Phrases C Automatic Filtering – Implementation D Automatic Filtering – Sentence-Level Analysis E Task-Oriented Dialogs – Examples F Effectiveness Of Automatic Filtering – A Detailed Analysis G Inter-Annotator Agreement – Detailed Analysis H Annotation Guidelines I Hyperparameters and Baseline Experiments J Human-Human Dialogs – Examples H Annotation Guidelines H.1 Annotation Task Learning from textual user feedback, i.e., free-form text feedback that expresses user dissatisfaction, describes new knowledge (new concepts), provides  corrections or alternative responses, is increasingly important to continuously improve dialog systems. As of today, only few datasets provide annotation for such data. However, there are various dialog datasets publicly available – from a variety of domains and for all kinds of tasks. As an annotator, you are provided with two different taxonomies, an error type taxonomy, and a user response type taxonomy, and two different types of dialogs, conspicuous dialogs and cold dialogs. Your task is to annotate the dialogs using the given taxonomies. The conspicuous dialogs are identified  to contain conspicuous sentences in user utterance that address an error in the previous system response. You have to decide (1) whether or not this conspicuous sentence correctly addresses an error in the previous system response, (2) which error type is represented in the previous system response, and (3) how the user reacted. The cold dialogs do not contain such information. In this case, your task is to identify error situations and user response types in the dialogs just given the taxonomies. H.2 Dialog Format To avoid bias, we provide the dialogs in a unified format without the original task-specific annotations. Here’s an example of a conspicuous dialog: { "unique_id": "PMUL0121. json_multiwoz_train", "id": "PMUL0121.json", "turns": [ { "utterance": "Hi there! Can you give me some info on Cityroomz?", "response": "Cityroomz is located at Sleeperz Hotel, Station Road in the Centre area. It is a moderatley priced hotel with a 0 star rating. Would you like to book a room?" }, { "utterance": "Yes please. I need it for 7 people for 3 nights starting on tuesday. ", "response": "How many days would you like to book it for?" }, { "utterance": "I would like to book it for 3 nights beginning on Tuesday.", "response": "Ok, Your rooms have been booked. Your reference number is CYJDAR7R . Is there anything else I can help you with today?" }, { "utterance": "Yeah, I need a restaurant in the west and with expensive pricing.", "response": "There are 9 expensive restaurants in the west area. Do you prefer a certain type of food?" }, { "utterance": "No, I don’t have a preference, but I need the phone number and postcode.", "response": "I recommend Graffiti, the number is 01223277977 and the postcode is cb30lx. Would you like me to book a table for you?" }, { "utterance": "No, right now, I just need the infor. Thanks !", "response": "Can I help you with anything else today?" }, { "utterance": "That is all. thanks", "response": "Thank you for using our system." } ], "conspicuous": [ { "phrase": [ "actually, i apologize no need to book, i was just gathering information.", " i would like to book it for 3 nights beginning on tuesday." ], "confidence": 0.593, "turn": 2 } ], "annotations": [ { "turn": 2, "annotation": { "error_type": "E2", "comment": "the system misses intent/slots. the user already said that he need it for three nights", "error": "C1", "user_response": "B3" } } ] } Each dialog consists of a unique id, an id, and its turns. Conspicuous is an array. The first value is an error-indicating phrase, a phrase that was identified to express user dissatisfaction in the utterance of the corresponding turn. The second value is the value from an utterance of this dialog that was identified to be similar to this error-indicating sentence. Confidence represents the similarity. Dialogs with multiple conspicuous values are possible. The annotations list has an entry for each conspicuous phrase. Please add your annotations here. In comment, you can share your thoughts with us. Here’s an example for an cold dialog: [ { "dialog": "p2 cats are like cartoons. p1 that’s cool , whats your favorite food ? p2 pizza. p1 ni hao . as my father says . you must have great plans ahead ? p2 yes, i plan to be a success.", "error": "C2", "error_type": "", "user_response": "", "comment": "", "turn": "", "phrase": "", }, ... ] The structure is a bit different. All cold dialogs are provided in one large json file, and the dialogs themselves maintain the structure of the original dataset. In this case, it is an dialog from the humanbot split of the Self-Feeding Chatbot (p2 represents the system, p1 represents the user). There are two additional fields here: turn and phrase. If you by chance find a phrase that indicates dissatisfaction in the user’s response to a system’s error, please add phrase and turn to these fields. H.3 Taxonomies H.3.1 Error Type Taxonomy This is the taxonomy for the field error type. [ { "id": "E1", "name": "Ignore Question", "description": "The response ignores a user\’s question.", "example": "User: ’How is the weather in Berlin?’, System:’ Have you ever heard of Bobby Jean?’" }, { "id": "E2", "name": "Ignore Request", "description": "The response ignores a user’s request to do something. ", "example": "User: ’Can you book me a room for two please?’, System: ’Look at these flowers ... Aren’t they beautiful?’" }, { "id": "E3", "name": "Ignore Expectation", "description": "The response does not fulfill the user’s expectation.", "example": "User: ’Can you book me a room at The Inn for two nights starting tomorrow? Can you also provide me with the address?’, System:’Absolutely!’ " }, { "id": "E4", "name": "Attribute Error", "description": "The response suggests that the system did not get the attributes/slots right.", "example": "User: ’Can you book me an expensive restaurant in the west of town?’, System:’ Sure. I would recommend The Breakfast Club. It is a very cheap and nice location." }, { "id": "E5", "name": "Factually Incorrect ", "description": "The response contains information that is factually incorrect.", "example": "User: ’Who is the current president of the United States of America?’, System:’Abraham Lincoln.’" }, { "id": "E6", "name": "Topic Transition Error", "description": "The response transitions to another / a previous topic without reasonable explanation.", "example": "User: ’I think I have a cold.’, System:’I really love pears. So juicy!’" }, { "id": "E7", "name": "Conversationality", "description": "The response indicates that the system lost track, e.g., it repeats previous responses (without asking for missing information) or contradicts itself.", "example": "System: ’Man is shaped by his environment.’, User:’ Yes. But does that justify this tone?’, System: ’Man is shaped by his environment.’" }, { "id": "E8", "name": "Unclear Intention", "description": "The response suggests that the user’ s intent was not successfully conveyed.", "example": "User: ’Can you book me a flight to London?’, System: ’Yes. What do you want for dinner?’" }, { "id": "E9", "name": "Lack of Sociality", "description": "The response lacks consideration of social standards, e.g., greetings, is toxic or disrespectful.", "examples": "User: ’Hi! My name is Jochen Schweitzer!’, System: ’ Sure. How can I help you?’" }, { "id": "E10", "name": "Lack of Common Sense", "description": "The information in the responses opposes the opinion of the majority. ", "example": "User: ’You have to work hard for success.’, System: ’Hmm... No. What is to be, will be.’" } ] H.3.2 User Response Taxonomy This is the taxonomy for the field user response. [ { "id": "UR1", "short": "The user ignores the error and continues the conversation.", "description": "The user simply continues and does not draw the system’ s attention to the error. ", "example": "-" }, { "id": "UR2", "short": "The user repeats or rephrases his/her concern.", "description": "The user repeats or rephrases his originally concern.", "example": "’Can you book a restaurant for two for tonight?’ vs. ’Can you book a table for two for tonight?’" }, { "id": "UR3", "short": "The user makes the system aware of the error and provides a correction.", "description": "The user makes the system aware of the error and provides information to address what is missing or wrong in its utterance. ", "example": "’No, I didn’t want you to book a table. I just wanted the address!’" }, { "id": "UR4", "short": "The user makes the system aware without providing a correction.", "description": "The user makes the system aware without providing additional information", "example": "’No. You’re wrong.’" }, { "id": "UR5", "short": "The user asks for clarification.", "description": "The user is puzzled and asks for clarification, e.g. the system suddenly switches to another topic or mixed concepts up.", "example": "’What do you mean?’" } ] This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv Lead image by Clayton Robbins on Unsplash

Dialog Datasets Annotation Guidelines

Code reviews are a type of software quality assurance activity that involves rigorous evaluations of code in order to identify bugs, improve code quality, and assist engineers in understanding the source code. Implementing a systematic approach for human code reviews is one of the most effective ways to enhance software quality and security. Given the probability of mistakes during code authorship, using many fresh eyes with complementary knowledge may disclose flaws that the original programmer may have overlooked. A successful peer review process requires a careful balance of well-established protocols and a non-threatening, collaborative atmosphere. Highly structured peer evaluations can hinder productivity, while lax approaches are frequently unsuccessful. Managers must find a happy medium that allows for fast and successful peer review while also encouraging open communication and information sharing among coworkers. The Benefit/Importance of Code Reviews The fundamental goal of code review is to guarantee that the codebase’s code health improves with time. is a “concept” used to measure if the codebase on which one or more developers are working is — manageable, readable, stable (or less error-prone), buildable, and testable. Code health Code reviews enhance code quality by detecting issues before they turn unmanageable, it ensures a consistent design and implementation and also assures consistency of standards. It contributes to the software’s maintainability and lifespan, resulting in sturdy software created from components for smooth integration and functioning. It is inevitable that adjustments will be required in the future, thus it is critical to consider who will be accountable for implementing such changes. When source code is regularly reviewed, developers can learn dependable techniques and best practices, as well as provide better documentation because some developers may be oblivious of optimization approaches that could be applicable to their code. The code review process allows these engineers to learn new skills and improve the efficiency of their code and produce better software. Another significant benefit of code reviews is that they make it easier for analysts and testers to comprehend. In Quality Assurance (QA) testing, testers must not only evaluate the code quality but also discover issues that contribute to bad test results. This can result in ongoing, needless development delays owing to further testing and rewriting. Performing Code Reviews Good code reviews should be the standard that we all strive toward. Here are some guidelines for establishing a successful code review to ensure high-quality and helpful reviews in the long run: Use Checklists Every member of your team is quite likely to repeat the same mistakes because omissions are the most difficult to identify since it is hard to evaluate something that does not exist. Checklists are the most effective method for avoiding frequent errors and overcoming the challenges of omission detection. Checklists for code reviews can help team members understand the expectations for each type of review and can be beneficial for reporting and process development. Set Limits for Review Time and Code Lines Checked It might of course be very much tempting to rush through a review and expect someone else to detect the mistakes you omitted. However, a indicates a considerable decline in defect density at speeds quicker than 500 LOC per hour. The most effective code review is performed in a suitable quantity, at a slower speed, for a limited period of time. SmartBear study Code review is vital, but it can also be a time-consuming, as well as a painstaking, process. As a result, it is critical to control how much time a reviewer or team spends on the specifics of each line of code. Best practices in this area include ensuring that team members do not spend more than an hour on code reviews and that the team does not examine more than a few hundred lines in a certain amount of hours. In essence, it is strongly advised not to review for more than 60 minutes at a time, as studies suggest that taking pauses from a task over time can significantly increase work quality. More regular evaluations should lessen the need for a review of this length in the future. Performing a Security Code Review. A security code review is a manual or automated method that assesses an application’s source code. Manual reviews examine the code’s style, intent, and functional output, whereas automated tools check for spacing or name errors and compare it to known standard functions. A security code review, the third sort of evaluation, examines the developer’s code for security resilience. The goal of this examination is to identify any current security weaknesses or vulnerabilities. Among other things, code review searches for logic flaws, reviews spec implementation, and verifies style guidelines. However, it is also important that a developer should be able to write code in an environment that protects it against external attacks, which can have effects on everything from intellectual property theft to revenue loss to data loss. Limiting code access, ensuring robust encryption, and establishing Secrets Management to safeguard passwords and hardcodes from widespread dissemination are some examples. Make Sure Pull Requests Are Minimal and Serve a Single Function. Pull requests (PRs) are a typical way of requesting peer code evaluations. The PR triggered the review process when a developer completes an initial code modification. To improve the effectiveness and speed of manual code review, the developer should submit PRs with precise instructions for reviewers. The lengthier the review, the greater the danger that the reviewer may overlook the fundamental goal of the PR. In fact, a PR should be no more than 250 lines long because a shows reviewers may find 70–90 percent of errors in under an hour. study Offer Constructive Feedback. Giving constructive feedback is very essential as code reviews play very important roles in software development, however, it is also important to be constructive rather than critical or harsh in your feedback to maintain your team’s morale and ensure the team learns from the mistake. Code Review Examples The main outcome of a code review process is to increase efficiency. While these traditional methods of code review have worked in the past, you may be losing efficiency if you haven’t switched to a code review tool. A code review tool automates the process of code review so that a reviewer solely focuses on the code. A code review tool integrates with your development cycle to initiate a code review before new code is merged into the main codebase. You can choose a tool that is compatible with your technology stack to seamlessly integrate it into your workflow. A great example of code review, especially in Python, which is my favored language, would be dealing with Duck Typing, which is strongly recommended in Python to be more productive and adaptable. Emulating built-in Python types such as containers is a common use case: # Pythonic!
    class DictLikeType:
        def __init__(self, *args, **kwargs):
            self.store = dict(*args, **kwargs)

        def __getitem__(self, key):
            return self.store[key] Full container protocol emulation involves the presence and effective implementation of several magic methods. This can become time-consuming and error-prone. A preferable approach is to build user containers on top of a respective abstract base class: # Extra Pythonic!
class DictLikeType(collections.abc.MutableMapping):
    def __init__(self, *args, **kwargs):
        self.store = dict(*args, **kwargs)

    def __getitem__(self, key):
        return self.store[key] We would not only have to implement fewer magic methods, but the ABC harness would also verify that all necessary protocol methods were in place. This mitigates some of the inherent instability of dynamic typing. Top Code Review Tools for Developers The fundamental purpose of a code review process, as described earlier in this article, is to enhance efficiency. While the traditional code review approaches outlined above have worked in the past (and continue to work), you may be losing efficiency if you haven’t switched to using a code review tool. A code review tool automates the code review process, freeing up the reviewer’s time to concentrate solely on the code. Before adding new code to the main codebase, code review tools interact with your development cycle to initiate a code review. You should choose a tool that is compatible with your technological stack so that it can be readily integrated into your workflow. Here is a list of some of the top code review tools: You may have previously used forks and pull requests to evaluate code if you use Github to manage your Git repositories in the cloud. Github Github also stands out due to its discussion feature during a pull request; with Github, you can analyze the difference, comment inline, and view the history of changes. You can also use the code review tool to resolve small Git conflicts through the web interface. To establish a more thorough procedure, GitHub even allows you to integrate with other review tools via its . marketplace Crucible Atlassian’s Crucible is a collaborative code review tool that lets you examine code, discuss plan modifications, and find bugs across a variety of version control systems. Crucible integrates well with other products in Atlassian’s ecosystem, including Confluence and Enterprise BitBucket. And, just like with any product that is encircled by other products in its ecosystem, combining Crucible with Jira, Atlassian’s Issue, and Project Tracker, will provide the greatest advantage. It allows you to do code reviews and audits on merged code prior to committing. 3. Smartbear Collaborator SmartBear Collaborator is a peer code and document review tool for development teams working on high-quality code projects. Collaborator allows teams to review design documents in addition to source code. You can use Collaborator to see code changes, identify defects, and make comments on specific lines of code. You can also set review rules and automatic notifications to ensure that reviews are completed on time. It also allows for easy integration with multiple SCMs and IDEs such as Visual Studio and Eclipse amongst others. 4. Visual Expert is an enterprise solution for code review specializing in . It has support for three platforms only: PowerBuilder, , and . If you are using any other DBMS, you will not be able to integrate Visual Expert for code review. Visual Expert database code SQL Server Oracle PL/SQL spares no line of code from rigorous testing. The code review tool delivers a comprehensive analysis of code gathered from a customer’s preferred platform. Visual Expert 5. RhodeCode is a secured, open-source enterprise source code management tool. It is a unified tool for Git, Subversion, and Mercurial. Its primary functions are team collaboration, repository management, and code security and authentication. Rhodecode RhodeCode distinguishes itself by allowing teams to synchronize their work through commit code commentary, live code discussions, and sharing code snippets. Teams may also use coding tools to assign review jobs to the appropriate person, resulting in a more frictionless workflow for teams. Conclusion We learned what code review is and why it is crucial in the software life cycle in this tutorial. We also discussed best practices for reviewing code and the various approaches for doing so, as well as an example of a code review and lists of top code review tools to assist you to get started reviewing code throughout your organization or team. Also published here

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