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Nvidia achieved a significant milestone by overtaking Google-parent Alphabet’s stock market capitalization briefly on February 14. The surge was driven by robust demand for Nvidia’s chips, which are widely used in artificial intelligence computing. Over the past 12 months, the company’s ...

Nvidia briefly becomes third-largest US company, overtaking ...

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(Reuters) -Nvidia eclipsed Google-parent Alphabet as the third most valuable U.S. company on Wednesday, just after overtaking Amazon and as investors looked to the dominant AI chipmaker&#x27;s upcoming quarterly report. The Santa Clara, California company&#x27;s stock rose 2.46%, putting its value at ...

Nvidia replaces Alphabet as Wall St's third most valuable company

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Tech giant Nvidia hit an all-time high on the stock market on Monday, managing to briefly surpass even Amazon when it reached a market valuation of $1.83 trillion. The rally saw it temporarily become the fourth highest valued US-listed company behind Alphabet, Apple, and Microsoft, Bloomberg ...

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Capital is a financial asset that usually comes with a cost. Here we discuss the four main types of capital: debt, equity, working, and trading.

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What Is a Company, How to Start One, Different Types

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Building a User-Friendly Interface for Central Bank Digital Currency Applications: A Case Study 

Real-Time Event Monitoring with Redpanda, OpenTelemetry, and Grafana

This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Salim Chouaki, LIX, CNRS, Inria, Ecole Polytechnique, Institut Polytechnique de Paris; (2) Oana Goga, LIX, CNRS, Inria, Ecole Polytechnique, Institut Polytechnique de Paris; (3) Hamed Haddadi, Imperial College London, Brave Software; (4) Peter Snyder, Brave Software. Table of Links Abstract and Introduction Background Measurement Methodology Results Limitations Related Work Conclusion, Acknowledgments, and References Appendix 2 BACKGROUND This section briefly discusses the policies of the main search engines alongside popular tracking approaches. 2.1 Private search engines We study the two dominant search engines that rely on user tracking for personalized search results and advertisements, namely Google and Bing, and three of the most popular privacy-branded search engines that provide users with nonpersonalized results and ads: DuckDuckGo, StartPage, and Qwant [11, 29]. Private search engines can either build their own independent search indexes or use big tech search engines like Bing, Google, or Yahoo to provide search results. Both types of private search engines claim not to store users’ search histories and not to collect nor share tracking and personal data. We now describe the advertising systems employed by the different private search engines and present a summary of their data-sharing policies outlined in their respective About pages. DuckDuckGo is a standalone search engine that maintains and uses its own search index alongside other indexes, such as Bing’s, to provide search results [31]. DuckDuckGo relies on Microsoft’s advertising system but only serves ads based on the search results and not the behavioral profiles of users [30]: "search ads on DuckDuckGo are based on the search results page you’re viewing instead of being based on you as a person" When clicking an ad on DuckDuckGo, the user is redirected to the ad’s landing page through Microsoft Advertising’s platform. DuckDuckGo claims Microsoft does not store ad-click behaviors from DuckDuckGo for purposes other than accounting and does not associate ad-clicks with users’ profiles [18]: "When you click on a Microsoft-provided ad that appears on DuckDuckGo, Microsoft Advertising does not associate your adclick behavior with a user profile. It also does not store or share that information other than for accounting purposes." This implies that Microsoft can, though currently chooses not to, link the ad-click to an existing Microsoft user profile. The privacy policy is signed by both DuckDuckGo and Microsoft. is a standalone EU-based search engine that allows users to access online resources without being tracked nor profiled [32]. Qwant relies on Microsoft’s advertising system to deliver ads in their search results pages. Although Qwant reports transmitting some information concerning search queries to Microsoft to enable the latter to present pertinent advertisements, it remains unclear which specific information is shared. In addition, to detect fraud, Qwant uses a specialized service offered by Microsoft, which has access to the user’s IP address and the browser "User-Agent". Qwant assures that this service does not have access to the search query, which is sent to another service that does not know the IP address of the user [32]. Qwant Unlike DuckDuckGo, which also uses Microsoft advertising, we did not find any mention to ad click information on Qwant’s privacy policy. They do not mention whether Microsoft stores this data and for what purposes they use it. is a meta-search engine that allows users to obtain non-personalized search results from Google’s search index while protecting their privacy. StartPage relies on Google AdSense to show ads to users. According to StartPage’s privacy policy, the search engine serves strictly non-personalized ads since it does not share any identifiable information with Google. Therefore, ads displayed on the search results page are solely based on the user’s search query [38]. StartPage Regarding ad-click behavior data, the privacy policy does not make any reference to whether Google tracks or profiles users based on this information. Nevertheless, StartPage emphasizes that by clicking on an ad, users leave the protection of StartPage’s privacy policies and become subject to the practices of the website they are redirected to [37]. "By clicking on an ad, like any other external website you click on after performing a StartPage search, you leave the privacy protection of StartPage and are subject to those websites’ data collection policies." 2.2 Cross-site tracking Cross-site tracking refers to the practice of following a user across multiple first-party websites and associate their browsing activities to a unique identifier. Web tracking practices require first party websites (e.g. the content providers) to share data about a user’s activity with third parties (the trackers). Online tracking has been traditionally implemented through browser cookies. However, due to increasing adoption of cookie-blocking browsers and extensions, and the push on adopting partitioned cookies storage on web browsers, more and more trackers started to rely on navigational tracking techniques. We next discuss how these techniques work. To enable cross-site cookie tracking, whenever a user visits a first-party website, the website makes a request to the third-party website (the tracker). This allows the tracker to set a cookie, which will identify the user and will be associated with the browsing activity of the user. For example, when the user visits a website A that makes a request to the tracker T, the tracker associates the cookie identifier of the user with the fact that the user visited website A (see Figure 1). Later, when the user visits website B, which also makes a request to the tracker T, the tracker will be able to associate the cookie identifier of the user with the fact that the user visited website B. Hence, the tracker will be able to know that the user visited websites A and B. 2.2.1 Cookie tracking. This was initially possible because browsers had a common cookie storage containing all cookies, and trackers could read their corresponding cookies regardless of which firstparty website allowed the tracker cookie to be set (see Figure 1). However, several browsers, such as Safari, Firefox, and Brave, have implemented partitioned storage to prevent using cookies for cross-site tracking [33]. These browsers use a partitioned cookies storage with a hierarchical namespace where a tracker accesses a different storage area on each website that loads it, preventing trackers from matching or assigning the same identifiers to users across multiple websites. Hence, cross-site tracking based on cookies can no longer be performed on these browsers. Chrome -the most used web browser- is in the process of testing partitioned cookies storage but does not use it by default [27, 28]. Navigational tracking refers to tracking techniques that use one or more URL navigations to share user information across sites. Navigational tracking does not require third-party cookies and can be used to circumvent browsers’ privacy protections from cross-site tracking using partitioned cookies storage. 2.2.2 Navigational tracking. is a navigational tracking technique that refers to redirecting users through one or more redirectors when navigating from one website to another. To allow this, a website A containing links to another website B does not directly link to the target B but instead links to an intermediary redirector (R)–the tracker (see Figure 2). When users click on a link on website A, they are taken to the redirector first, which then redirects them to the intended destination (website B) or other intermediary redirectors. The website A can directly change the actual link of the destination (b.com) to a redirection link (r.com), or a redirector’s third-party script can do it. On its turn, the redirector can change the destination link again and send it further to other redirectors. Bounce tracking Hence, from the link in the ad on the website A, one cannot know all the different redirectors the users will pass through when they click on an ad. We call the redirection path all the websites a user navigates through to arrive from A to B. Since, from a browser perspective, the redirector is the first-party domain, it can read or set cookies in its own partition [26]. In the following, we describe what data redirectors can infer according to the redirector’s behavior. (1) If the redirector does not set a first-party cookie, it will only know that a user went from website A to website B and will not be able to link this to other user browsing activities. (2) If the redirector sets a first-party cookie, it will be able to aggregate all the activity of the user that is redirected through it (either from website A or other websites that use it as a redirector), hence, it will allow cross-site tracking. (3) If the redirector also sets third-party cookies on websites A and B, it will not be able to link the activity of the user on website A with the activity of the user on website B, and with the activity of the user that goes through its own site (through redirects) since they do not share the same user ID [33]. Hence, while bounce tracking allows to a certain degree, cross-site tracking, it does not have the same coverage as the traditional third-party cookie tracking. is a navigational tracking technique that modifies users’ navigation requests by adding information to the navigation URLs in the form of query parameters. In addition, similar to bounce tracking, UID smuggling may redirect the user to one or more third-party trackers before UID smuggling redirecting the user to the intended destination. Figure 3 describes this process. When a user clicks on a link on a website A, the originator page itself or a tracker on the page–through a script–decorates the URL by adding the originator’s user identifier (UID) as a query parameter. The user then passes through zero or more redirectors which are invisible to him. Each of these redirectors can get the UID from the query parameter and has permission to store it in a first-party cookie under the redirector’s domain. Finally, the user is sent to the destination website B, and the redirector can forward or not to website B the UID it received from A. All the trackers on website B will be able to read the UID from the query parameter and know that it was the UID sent by the originator (through request headers). UID smuggling is more powerful than bounce tracking. Trackers using UID smuggling regain the ability to share UIDs across websites with different domains and can circumvent restrictions from partitioned cookie storage spaces [33]. For example, they can link the user’s visits to the website A with the user’s visits to website B and the user’s activity that goes through its site (through redirects) since they can all be linked to the same user ID. In addition, UID smuggling can help other trackers on website B (and website A) to link users’ browsing activity across all the websites that received the UID as a query parameter.

Understanding the Privacy Risks of Popular Search Engine Advertising Systems: Background

Behavioral interviews are the most uncertain type of interview in the tech industry. I've seen many people underestimate the importance of preparing for these interviews, only to fail to receive an offer precisely because of their poor preparation. Fortunately, nailing behavioral interviews does not require months of preparation. In this guide, I will describe the key steps for success in such interviews. I’m a software development manager at Amazon. This article is a reflection of my thoughts and has nothing to do with Amazon or its subsidiaries. Why Do Behavioral Interviews Exist? Companies use behavioral interviews to assess whether you would be a successful hire based on your previous experiences. Thus, the most popular types of questions are example-based, such as “ . Companies test whether: Tell me about a time…” you are a “culture fit,” your previous are sufficient for the role you applied for. scope and impact Not all companies place equal importance on behavioral interviews. You can forget about receiving an offer from Netflix or Amazon if you haven't read about their principles or values. Meanwhile, companies like Google or Microsoft may place less emphasis on this aspect, and you might pass if you avoid showing red flags. Do your research to understand how important the behavioral part is at a particular company. Step 1. Prepare Examples It can be incredibly hard to come up with a good story during an interview, so I’d recommend preparing at least two examples for each bucket of behavioral interview questions. What are the buckets? A I like includes: categorization — challenges involving specific technical skills or knowledge. Technical Problem Solving — learning new skills, mentoring, taking feedback, managing failures, or achieving growth. Professional Growth — situations where you delivered results that impacted the business or project outcomes. Impact and Results — managing conflicts, stakeholders, negotiations, or leading people. Communication and Team Dynamics — handling ambiguity, adapting to change, or offering innovative solutions. Adaptability and Innovation Research the typical question buckets for the company you're applying to. You can’t prepare for all questions, but with a good set of stories, adapting them during the interview becomes quite easy. Don't have enough examples to prepare strong stories? Some might suggest making them up. It works, but I don't like that approach for many reasons. It is not ethical, and it takes more effort than building upon real stories. A better strategy would be to slightly augment your existing stories. That way, you can be more confident in your responses, answer follow-up questions more effectively, and spend less time on preparation. If you don't have a good story to tell during the interview, consider skipping the question. It is much better than giving an inappropriate example. Skipping five questions in a row isn't good, but skipping one or two doesn't hurt, especially if these aren't mainstream questions. Step 2. Use Examples Appropriate for Your Level Telling a story about a conflict with an intern over class naming might be acceptable for a junior-level engineer but not for a senior. Your stories should reflect your seniority level. For a principal or staff level, the examples should impact multiple organizations; for a senior level — at least your team; for a middle level — several people; and for an entry-level position, impacting even one person is acceptable. Strive to broaden your impact as much as possible. Name all organizations and teams impacted, and mention senior-level people involved. Failing to do so may result in being downleveled. Note that having many people involved is not a universal rule. Saving millions by introducing a clever optimization may be within the scope of your team, but this story could be strong enough for a principal-level position. Keep in mind the level you aim to land at and calibrate your answers. Step 3. Demonstrate "Culture Fit" Each company adheres to a specific set of values or principles, which they might call , , or . In essence, these are sets of attitudes and behaviors companies want to see in their employees. Google these values before the interview, and try to frame your answers according to these values. Culture Memos Leadership Principles Core Values Many companies do not document their values; however, behavioral interviews remain as crucial as any other type of interview. Even some well-known businesses, such as , fail to clearly communicate their corporate culture. In that case, I'd recommend the following: Apple Find a YouTube interview with the company's founder or a top manager discussing its culture. Talk with someone from this company, such as a friend of a friend or someone on LinkedIn. Research questions and answers on platforms like Glassdoor. Step 4. Structure Your Answers The standard recommendation for behavioral interviews is to use the (Situation, Task, Action, Result). Unfortunately, even candidates familiar with this method often fail to apply it effectively. The sections that usually present challenges are actions and results. STAR method For actions, it's critical to clarify your personal contribution — use “I” to articulate this. Statements like "We optimized this..." or "We escalated to..." do not clarify your specific role and contribution. Were you the one who optimized this, or was it a team member? Did you personally escalate the issue to a product manager, or was the escalation handled by your manager? Ideally, spend about half of your story's time discussing your actions. For the results, avoid vague statements like “it became faster” or “the project was delivered.” Instead, articulate the specific impact with numbers, even if approximate. More compelling answers might be: “The loading time decreased from 1s to 500ms” or “The project received 4.5 stars on the Apple Store and generated $5 million in revenue.” Adding a brief summary at the beginning of your response can enhance the STAR method. Briefly describing your story from start to finish helps the interviewer follow along and quickly determine if they need to hear more, potentially saving you several minutes of interview time. Step 5. Answer the Specific Question It's easy to misunderstand the question or drift into irrelevant stories due to nerves or misinterpretation. This often happens when candidates fail to actively listen or assume they know what the interviewer is asking without seeking clarification. Providing an accurate and relevant response is critical for demonstrating that you understand the requirements of the role and have the necessary skills. For complex multi-part questions writing them down is a good idea. That way, you can ensure you're answering all parts of the question, not just what you’ve remembered. If the interviewer asks about contributions outside of your area of responsibility, do not describe how you refactored a piece of code belonging to your team or resolved a bug introduced by a colleague. These are your core responsibilities. If the interviewer asks about a conflict, don't describe a brief disagreement where you accepted either point of view after five minutes. Conflict implies prolonged periods of disagreement, such as several weeks. Got a multi-part question about a difficult situation with a customer that significantly affected the business. Don't only describe why this situation was difficult and how it was solved. Write down the question and also explain how it affected the business. Step 6. Ask the Right Questions At the end of interviews, you have 5-10 minutes for your questions. Don’t fall into the trap of thinking the interview has ended — it has not. During the interview, everything matters, and the questions you ask at the end can influence the interviewer's decision on whether you pass or not. To use this time to your benefit, it's important to understand which questions to ask and which to avoid. Avoid HR questions about compensation, work from home, etc. Avoid personal questions about the country of origin, gender, mood, etc. Avoid mainstream questions such as “What does this company do?” or “What is the culture like here?” These show a lack of preparedness on your part. So, what should you ask? Choose questions that prove your interest in the company and suggest that you would be a valuable employee. For instance, ask, “What does success look like for this position?” “How do you manage to stay innovative despite being in the market for decades?” “What does a typical day look like for this position?” and “If I have an idea for a product feature or technical improvement, what is the process to implement it?.” Show that you've done your homework and are knowledgeable about the company and the position. Demonstrate your desire to succeed in this role and to be a team member that everyone would appreciate. Your future manager needs to see your independence (so they don't have to micromanage you) and your desire to achieve shared goals. Step 7. Practice Having impactful stories is critical, but effectively communicating them is key to resonating with interviewers. You might forget a story or struggle with detailing it due to anxiety. Interviewing can be stressful, and sometimes, is the optimal solution as many issues there can often be resolved. Fortunately, practicing behavioral interviews, also known as mock interviews, can significantly reduce stress during actual interviews and better prepare you to describe your experiences in a structured and clear manner. staying at your current job Many online platforms offer mock interviews on a paid basis. There, you can even choose interviewers from a specific company who can provide valuable feedback. This can be a bit expensive, so as an alternative, you can ask your friends to conduct such an interview. I’d recommend having at least three mock interviews before the real ones. Step 8. Impress and Connect Behavioral interviews are subjective by nature. The same story might be evaluated differently by different interviewers or even by the same interviewer under different circumstances. What can you do about this? Connect with your interviewer. Treat the interview as a dialogue, and listen attentively for any hints from the interviewer. Ask if your example fits well if they would like more details about the numbers or impact, etc. Demonstrate engagement and passion in your answers. I’d like to see that you’re interested in working with me. After all, this step is about making a positive impression on your interviewers. Final Thoughts Interviewing is a separate skill. You can be great at your day-to-day work but struggle with interviewing, and vice versa. It is a great investment to develop your interviewing skills, and every interview is a learning opportunity. Reflect on the feedback you receive and continuously refine your approach to become a more compelling candidate in future interviews.

Behavioral Interview: The Guide to Fitting In

আচরণগত ইন্টারভিউ: ফিটিং এর জন্য গাইড

Verhaltensinterview: Der Leitfaden zur Anpassung

Entrevista conductual: la guía para adaptarse

Entretien comportemental : le guide pour s'intégrer

व्यवहारिक साक्षात्कार: फिट होने की मार्गदर्शिका

行動面接：適応するためのガイド

행동 인터뷰: 적응 가이드

Entrevista comportamental: o guia para se adaptar

Поведенческое собеседование: руководство по адаптации

Davranışsal Röportaj: Uyum Sağlama Rehberi

Phỏng vấn hành vi: Hướng dẫn hòa nhập

行为面试：融入指南

In these intensely competitive times, every application needs to be aesthetically appealing in addition to providing seamless functionality to satisfy the users. If an app does not function properly because of a visual interface error or if the site is unreadable, users may uninstall or leave the app. This can ultimately lead to a loss of customers and a drop in revenues. To avoid this and ensure the best possible user experience, companies are prioritizing visual regression testing. Let us study a couple of examples of visual regression testing to understand its importance: When you click on a drop-down image the menu does not slide down after adding more options.
You have reworked the complete layout of the page, and the submit button on the page is broken. These UI errors need to be fixed since changes in the elements can lead to the breakdown of the visual aesthetics. These illustrations clearly highlight the importance of conducting thorough visual regression testing before software releases. Visual regression testing checks the UI of an application after code changes and ensures that these different visual elements, like buttons, images, and text, all render correctly. Through this blog, we will examine visual regression, the different techniques used, the challenges testers face when performing the tests, and the best practices to overcome them. Common Visual Regression Techniques Visual testing ensures that visual glitches are identified early and do not spill over to the production stage. Let us now briefly examine the commonly used methods for regression testing: Manual visual testing: In this method, testers manually compare UI screenshots before and after making changes in the code.
Comparing layouts: It requires comparing the UI layout before and after changes are made and accounts for changes in the UI’s font, color, and other visual elements.
Comparing pixel-by-pixel: Used to test small, complex UI elements, this method involves comparing the UI’s pixels before and after changes.
Structural comparison: It compares the UI’s Document Object Model (DOM) before and after making changes.
Inserting visual checkpoints: In this technique, checkpoints are inserted into the UI to check the UI’s visual state at specific points in time. Manual Regression Testing Manual regression testing is recommended in the early testing stages when the UI is unstable. It is suitable for ad hoc testing and conducting quick on-the-spot checks. In this method, the testers manually check the layout and appearance of the visual elements. It involves taking the latest screenshots and comparing them with baseline screenshots to unearth any gaps. Benefits of Automated Visual Regression Testing Over Manual Methods The manual technique can be time-consuming and lead to a waste of resources because of its repetitive nature. The scale of manual testing grows with every new feature addition, thereby increasing the time spent by a human tester pouring over screenshots to identify bugs. It is impractical to manually conduct visual regression testing when there are tight deadlines to meet. In contrast, automated visual regression testing helps you automate repetitive tasks like checking the page following every code change. Even with the heavy initial investment, automated testing, especially with AI-powered features, helps you save time and money in the long run. Though automated visual regression testing reaps many benefits for testers and developers, it has to be properly implemented if they are to receive its benefits. Let us examine the best practices that go into a proper visual regression testing process. What To Remember While Conducting Visual Regression Testing Following these best practices will help optimize your automated visual regression testing process and help you gain its maximum benefits: Develop Appropriate Test Scenarios Clearly define what to record in the snapshots and the time to take them while testing. Remember to include user interactions in such scenarios since the applications will need to deal with them in the real world. Validate the Entire UI Page Instead of only validating individual components, ensure to validate the entire UI page. This practice will increase the test coverage and improve it. Pick the Right Automated Tool The visual regression testing tool you choose should be capable of ignoring false positives. It should be able to take care of pixel offsets, anti-aliasing, and other issues while ensuring the tests don’t fail. The automated algorithm must be able to analyze the page’s structure and perform layout comparisons. It should also be able to take care of content that is dynamic and moving. Compare Screenshots Testers should compare screenshots with recent ones to identify the disparities. This will help ensure that the UI remains the same, improving the user experience and product quality. Identify and Resolve Bugs Ensure to resolve bugs that the testing tool identifies or send them to the respective developers for fixing them. Remember to update screenshots to be used as a baseline for future regression tests after it is done. Choose the Right Situation To Perform Automated Testing Automated visual regression testing may not be appropriate for all scenarios. In some situations, using a combination of manual and automated methods is advisable. For example, when new features are added, they can be validated using an automation technique and then checked manually. Conduct a Detailed Review A team of reviewers or a single reviewer can examine how adjustments are made without causing interruptions in the desired outcome and make a report accordingly. Learn From Your Mistakes Learning from your successes and failures is crucial in visual regression testing. You need to identify the root causes and impacts of the visual changes detected by your tool. Testers must also identify false negatives and positives and adjust the tool settings and test cases accordingly. Conclusion In conclusion, successful implementation of visual regression testing in Agile teams requires a holistic approach that encompasses automation, collaboration, scalability, and comprehensive analysis. Visual regression testing can be a game-changer if you choose an appropriate automated testing tool with the right features and advantages. By adhering to these best practices, Agile teams can effectively leverage visual regression testing to deliver high-quality user interfaces in an iterative and efficient manner. In these intensely competitive times, every application needs to be aesthetically appealing in addition to providing seamless functionality to satisfy the users. If an app does not function properly because of a visual interface error or if the site is unreadable, users may uninstall or leave the app. This can ultimately lead to a loss of customers and a drop in revenues. To avoid this and ensure the best possible user experience, companies are prioritizing visual regression testing . visual regression testing Let us study a couple of examples of visual regression testing to understand its importance: Let us study a couple of examples of visual regression testing to understand its importance: When you click on a drop-down image the menu does not slide down after adding more options. You have reworked the complete layout of the page, and the submit button on the page is broken. When you click on a drop-down image the menu does not slide down after adding more options. You have reworked the complete layout of the page, and the submit button on the page is broken. These UI errors need to be fixed since changes in the elements can lead to the breakdown of the visual aesthetics. These illustrations clearly highlight the importance of conducting thorough visual regression testing before software releases. Visual regression testing checks the UI of an application after code changes and ensures that these different visual elements, like buttons, images, and text, all render correctly. Through this blog, we will examine visual regression, the different techniques used, the challenges testers face when performing the tests, and the best practices to overcome them. Common Visual Regression Techniques Visual testing ensures that visual glitches are identified early and do not spill over to the production stage. Let us now briefly examine the commonly used methods for regression testing: Manual visual testing: In this method, testers manually compare UI screenshots before and after making changes in the code. Comparing layouts: It requires comparing the UI layout before and after changes are made and accounts for changes in the UI’s font, color, and other visual elements. Comparing pixel-by-pixel: Used to test small, complex UI elements, this method involves comparing the UI’s pixels before and after changes. Structural comparison: It compares the UI’s Document Object Model (DOM) before and after making changes. Inserting visual checkpoints: In this technique, checkpoints are inserted into the UI to check the UI’s visual state at specific points in time. Manual visual testing: In this method, testers manually compare UI screenshots before and after making changes in the code. Manual visual testing: Comparing layouts: It requires comparing the UI layout before and after changes are made and accounts for changes in the UI’s font, color, and other visual elements. Comparing layouts: Comparing pixel-by-pixel: Used to test small, complex UI elements, this method involves comparing the UI’s pixels before and after changes. Comparing pixel-by-pixel: Structural comparison: It compares the UI’s Document Object Model (DOM) before and after making changes. Structural comparison: Inserting visual checkpoints: In this technique, checkpoints are inserted into the UI to check the UI’s visual state at specific points in time. Inserting visual checkpoints: Manual Regression Testing Manual regression testing is recommended in the early testing stages when the UI is unstable. It is suitable for ad hoc testing and conducting quick on-the-spot checks. In this method, the testers manually check the layout and appearance of the visual elements. It involves taking the latest screenshots and comparing them with baseline screenshots to unearth any gaps. Benefits of Automated Visual Regression Testing Over Manual Methods The manual technique can be time-consuming and lead to a waste of resources because of its repetitive nature. The scale of manual testing grows with every new feature addition, thereby increasing the time spent by a human tester pouring over screenshots to identify bugs. It is impractical to manually conduct visual regression testing when there are tight deadlines to meet. In contrast, automated visual regression testing helps you automate repetitive tasks like checking the page following every code change. Even with the heavy initial investment, automated testing, especially with AI-powered features, helps you save time and money in the long run. Though automated visual regression testing reaps many benefits for testers and developers, it has to be properly implemented if they are to receive its benefits. Let us examine the best practices that go into a proper visual regression testing process. What To Remember While Conducting Visual Regression Testing Following these best practices will help optimize your automated visual regression testing process and help you gain its maximum benefits: Develop Appropriate Test Scenarios Clearly define what to record in the snapshots and the time to take them while testing. Remember to include user interactions in such scenarios since the applications will need to deal with them in the real world. Validate the Entire UI Page Instead of only validating individual components, ensure to validate the entire UI page. This practice will increase the test coverage and improve it. Pick the Right Automated Tool The visual regression testing tool you choose should be capable of ignoring false positives. It should be able to take care of pixel offsets, anti-aliasing, and other issues while ensuring the tests don’t fail. The automated algorithm must be able to analyze the page’s structure and perform layout comparisons. It should also be able to take care of content that is dynamic and moving. Compare Screenshots Testers should compare screenshots with recent ones to identify the disparities. This will help ensure that the UI remains the same, improving the user experience and product quality. Identify and Resolve Bugs Ensure to resolve bugs that the testing tool identifies or send them to the respective developers for fixing them. Remember to update screenshots to be used as a baseline for future regression tests after it is done. Choose the Right Situation To Perform Automated Testing Automated visual regression testing may not be appropriate for all scenarios. In some situations, using a combination of manual and automated methods is advisable. For example, when new features are added, they can be validated using an automation technique and then checked manually. Conduct a Detailed Review A team of reviewers or a single reviewer can examine how adjustments are made without causing interruptions in the desired outcome and make a report accordingly. Learn From Your Mistakes Learning from your successes and failures is crucial in visual regression testing. You need to identify the root causes and impacts of the visual changes detected by your tool. Testers must also identify false negatives and positives and adjust the tool settings and test cases accordingly. Conclusion In conclusion, successful implementation of visual regression testing in Agile teams requires a holistic approach that encompasses automation, collaboration, scalability, and comprehensive analysis. Visual regression testing can be a game-changer if you choose an appropriate automated testing tool with the right features and advantages. By adhering to these best practices, Agile teams can effectively leverage visual regression testing to deliver high-quality user interfaces in an iterative and efficient manner.

Optimal Strategies for Integrating Visual Regression Testing into Agile Teams

Authors:
(1) Rui Cao, Singapore Management University;
(2) Ming Shan Hee, Singapore University of Design and Technology;
(3) Adriel Kuek, DSO National Laboratories;
(4) Wen-Haw Chong, Singapore Management University;
(5) Roy Ka-Wei Lee, Singapore University of Design and Technology
(6) Jing Jiang, Singapore Management University. Table of Links Abstract and Introduction Related Work Preliminary Proposed Method Experiment Conclusion and References Appendix 3 PRELIMINARY We formally define our task and briefly review the use of pre-trained vision-language models (PVLMs) for zero-shot visual question answering (VQA). At the end of the section, we provide a brief introduction to the specific PVLM utilized in our work. In this work, we use the recently released BLIP-2 model [15] as the PVLM, as it has demonstrated good performance in zero-shot VQA. The BLIP-2 model is composed of a frozen pre-trained image encoder, a frozen pre-trained language model, and a lightweight Querying Transformer, which is responsible for bridging the modality gap. It is worth noting that the BLIP-2 model can be replaced with any other PVLM that is capable of zero-shot VQA. This paper is available on arxiv under CC 4.0 license. Authors: (1) Rui Cao, Singapore Management University; (2) Ming Shan Hee, Singapore University of Design and Technology; (3) Adriel Kuek, DSO National Laboratories; (4) Wen-Haw Chong, Singapore Management University; (5) Roy Ka-Wei Lee, Singapore University of Design and Technology (6) Jing Jiang, Singapore Management University. Authors: Authors: (1) Rui Cao, Singapore Management University; (2) Ming Shan Hee, Singapore University of Design and Technology; (3) Adriel Kuek, DSO National Laboratories; (4) Wen-Haw Chong, Singapore Management University; (5) Roy Ka-Wei Lee, Singapore University of Design and Technology (6) Jing Jiang, Singapore Management University. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Preliminary Preliminary Proposed Method Proposed Method Experiment Experiment Conclusion and References Conclusion and References Appendix Appendix 3 PRELIMINARY We formally define our task and briefly review the use of pre-trained vision-language models (PVLMs) for zero-shot visual question answering (VQA). At the end of the section, we provide a brief introduction to the specific PVLM utilized in our work. In this work, we use the recently released BLIP-2 model [15] as the PVLM, as it has demonstrated good performance in zero-shot VQA. The BLIP-2 model is composed of a frozen pre-trained image encoder, a frozen pre-trained language model, and a lightweight Querying Transformer, which is responsible for bridging the modality gap. It is worth noting that the BLIP-2 model can be replaced with any other PVLM that is capable of zero-shot VQA. 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

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