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Using Free AI Tools to Create a 100% Automated Youtube Shorts Channel

Authors:
(1) Vasiliki Kougia, University of Vienna, Faculty of computer science, Vienna, Austria & UniVie Doctoral School Computer Science, Vienna, Austria;
(2) Simon Fetze, University of Vienna, Faculty of computer science, Vienna, Austria;
(3) Thomas Kirchmair, University of Vienna, Faculty of computer science, Vienna, Austria;
(4) Erion Çano, University of Vienna, Faculty of computer science, Vienna, Austria;
(5) Sina Moayed Baharlou, Boston University, Department of Electrical and Computer Engineering, Boston, MA, USA;
(6) Sahand Sharifzadeh, Ludwig Maximilians University of Munich, Faculty of Computer Science, Munich, Germany;
(7) Benjamin Roth, University of Vienna, Faculty of computer science, Vienna, Austria. Table of Links Abstract and Introduction Related Work MemeGraphs Benchmarking Analysis Conclusion, Acknowledgments, and References 3 MemeGraphs In this section, we describe our method for automatic augmentation of a memes dataset, an approach we call MemeGraphs. Our proposed method consists of three steps: 1. Scene graph construction, 2. Knowledge linking to detected entities and 3. The construction of the final MemeGraphs input. The first two steps are performed automatically by using off-the-shelf models. Hence, the result of MemeGraphs are knowledge graphs representing the meme as a whole, which can be used to classify them, e.g., for hate detection. We build on the MultiOFF dataset, which contains memes extracted from social media during the 2016 U.S. Presidential Election [28]. In what follows, we present the three individual steps of MemeGraphs in detail and how the final result is constructed. 3.1 Scene graphs For 67 memes, Schemata did not produce any output. For the remaining 676 memes, 10,426 relations and 9,666 objects were detected in total. The number of unique objects and relations was 142 and 970 respectively. And while the number of objects detected in an image ranged from 2 to 16, the number of detected relations ranged from 1 to 40. The top 15 most frequently detected object and relation types are shown in Fig. 2. 3.2 Knowledge linking 3.3 Knowledge graph input [6] This number was chosen after observation of the scene graphs resulting from the memes in order to avoid having an overcrowded scene graph with multiple objects. [7] https://spacy.io/universe/project/spacy-transformers [8] https://www.wikidata.org/wiki/Wikidata:Main_Page [9] All the texts are concatenated with a full stop. This paper is available on arxiv under CC 4.0 license. Authors: (1) Vasiliki Kougia, University of Vienna, Faculty of computer science, Vienna, Austria & UniVie Doctoral School Computer Science, Vienna, Austria; (2) Simon Fetze, University of Vienna, Faculty of computer science, Vienna, Austria; (3) Thomas Kirchmair, University of Vienna, Faculty of computer science, Vienna, Austria; (4) Erion Çano, University of Vienna, Faculty of computer science, Vienna, Austria; (5) Sina Moayed Baharlou, Boston University, Department of Electrical and Computer Engineering, Boston, MA, USA; (6) Sahand Sharifzadeh, Ludwig Maximilians University of Munich, Faculty of Computer Science, Munich, Germany; (7) Benjamin Roth, University of Vienna, Faculty of computer science, Vienna, Austria. Authors: Authors: (1) Vasiliki Kougia, University of Vienna, Faculty of computer science, Vienna, Austria & UniVie Doctoral School Computer Science, Vienna, Austria; (2) Simon Fetze, University of Vienna, Faculty of computer science, Vienna, Austria; (3) Thomas Kirchmair, University of Vienna, Faculty of computer science, Vienna, Austria; (4) Erion Çano, University of Vienna, Faculty of computer science, Vienna, Austria; (5) Sina Moayed Baharlou, Boston University, Department of Electrical and Computer Engineering, Boston, MA, USA; (6) Sahand Sharifzadeh, Ludwig Maximilians University of Munich, Faculty of Computer Science, Munich, Germany; (7) Benjamin Roth, University of Vienna, Faculty of computer science, Vienna, Austria. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work MemeGraphs MemeGraphs Benchmarking Benchmarking Analysis Analysis Conclusion, Acknowledgments, and References Conclusion, Acknowledgments, and References 3 MemeGraphs In this section, we describe our method for automatic augmentation of a memes dataset, an approach we call MemeGraphs. Our proposed method consists of three steps: 1. Scene graph construction, 2. Knowledge linking to detected entities and 3. The construction of the final MemeGraphs input. The first two steps are performed automatically by using off-the-shelf models. Hence, the result of MemeGraphs are knowledge graphs representing the meme as a whole, which can be used to classify them, e.g., for hate detection. We build on the MultiOFF dataset, which contains memes extracted from social media during the 2016 U.S. Presidential Election [28]. In what follows, we present the three individual steps of MemeGraphs in detail and how the final result is constructed. 3.1 Scene graphs For 67 memes, Schemata did not produce any output. For the remaining 676 memes, 10,426 relations and 9,666 objects were detected in total. The number of unique objects and relations was 142 and 970 respectively. And while the number of objects detected in an image ranged from 2 to 16, the number of detected relations ranged from 1 to 40. The top 15 most frequently detected object and relation types are shown in Fig. 2. 3.2 Knowledge linking 3.3 Knowledge graph input [6] This number was chosen after observation of the scene graphs resulting from the memes in order to avoid having an overcrowded scene graph with multiple objects. [7] https://spacy.io/universe/project/spacy-transformers [8] https://www.wikidata.org/wiki/Wikidata:Main_Page [9] All the texts are concatenated with a full stop. 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

MemeGraphs: Automated Augmentation for Hate Detection in Memes

The TechBeat: Leveraging MinIO and Apache Tika for Automated Text Extraction and Analysis (4/26/2024)

The TechBeat: Leveraging MinIO and Apache Tika for Automated Text Extraction and Analysis (4/24/2024)

Leveraging MinIO and Apache Tika for Automated Text Extraction and Analysis

স্বয়ংক্রিয় পাঠ্য নিষ্কাশন এবং বিশ্লেষণের জন্য MinIO এবং Apache Tika ব্যবহার করা

Nutzung von MinIO und Apache Tika zur automatischen Textextraktion und -analyse

Aprovechando MinIO y Apache Tika para la extracción y el análisis de texto automatizados

Tirer parti de MinIO et Apache Tika pour l’extraction et l’analyse automatisées de texte

स्वचालित पाठ निष्कर्षण और विश्लेषण के लिए मिनियो और अपाचे टिका का लाभ उठाना

MinIO と Apache Tika を活用した自動テキスト抽出と分析

자동화된 텍스트 추출 및 분석을 위해 MinIO 및 Apache Tika 활용

Aproveitando MinIO e Apache Tika para extração e análise automatizada de texto

Использование MinIO и Apache Tika для автоматического извлечения и анализа текста

Otomatik Metin Çıkarma ve Analizi için MinIO ve Apache Tika'dan Yararlanma

Tận dụng MinIO và Apache Tika để trích xuất và phân tích văn bản tự động

利用 MinIO 和 Apache Tika 进行自动文本提取和分析

This Article Is Worth Your Attention if You Are passionate about writing good quality software and want to enhance the stability of your app with tests. Are tired of unexpected bugs popping up in your production systems. Need help understanding what automated tests are and how to approach them. Why Do We Need Automated Tests? As engineers, we want to , but with each new feature we create, we inevitably increase the size and complexity of our apps. build things that work As the product grows, it becomes more and more time-consuming to (e.g., with your hands) test every functionality affected by your changes. manually The absence of automated tests leads to us either spending too much time and slowing our shipping speed down or spending too little to save the velocity, resulting in new bugs in the backlog along with the late-night calls from PagerDuty. On the contrary, . So, computers can be programmed to do the same repeatedly let's delegate testing to computers! Types of Tests The suggests . Let's dive deep into every kind and understand why we need each. Testing pyramid idea three main types of tests: unit, integration, and end-to-end Unit Tests A is a small piece of logic you test in (without relying on other components). unit isolation They finish within seconds. allows them to run them at any point in time, locally and on CI, without spinning up the dependent services/making API and database calls. Unit tests are fast. Isolation A function that accepts two numbers and sums them together. We want to call it with different arguments and assert that the returned value is correct. Unit test example: // Function "sum" is the unit
const sum = (x, y) => x + y

test('sums numbers', () => {
  // Call the function, record the result
  const result = sum(1, 2);

  // Assert the result
  expect(result).toBe(3)
})

test('sums numbers', () => {
  // Call the function, record the result
  const result = sum(5, 10);

  // Assert the result
  expect(result).toBe(15)
}) A more interesting example is the React component that renders some text after the API request is finished. We need to mock the API module to return the necessary values for our tests, render the component, and assert the rendered HTML has the content we need. // "MyComponent" is the unit
const MyComponent = () => {
  const { isLoading } = apiModule.useSomeApiCall();

  return isLoading ? <div>Loading...</div> : <div>Hello world</div>
}

test('renders loading spinner when loading', () => {
  // Mocking the API module, so that it returns the value we need
  jest.mock(apiModule).mockReturnValue(() => ({
    useSomeApiCall: jest.fn(() => ({
      // Return "isLoading: true" for this test case
      isLoading: true
    }))
  }))

  // Execute the unit (render the component)
  const result = render(<MyComponent />)

  // Assert the result
  result.findByText('Loading...').toBeInTheDocument()
})

test('renders text content when not loading', () => {
  // Mocking the API module
  jest.mock(apiModule).mockReturnValue(() => ({
    useSomeApiCall: jest.fn(() => ({
      // Return "isLoading: false" for this test case
      isLoading: false
    }))
  }))

  // Execute the unit (render the component)
  const result = render(<MyComponent />)

  // Assert the result
  result.findByText('Hello world').toBeInTheDocument()
}) Integration Tests When your interacts with other , we call it an . These tests are slower than unit tests, but they test how the parts of your app connect. unit units (dependencies) integration A service that creates users in a database. This requires a DB instance ( ) to be available when the tests are executed. We will test that the service can create and retrieve a user from the DB. Integration test example: dependency import db from 'db'

// We will be testing "createUser" and "getUser"
const createUser = name => db.createUser(name) // creates a user
const getUser = name => db.getUserOrNull(name) // retrieves a user or null

test("creates and retrieves users", () => {
  // Try to get a user that doesn't exist, assert Null is returned
  const nonExistingUser = getUser("i don't exist")
  expect(nonExistingUser).toBe(null);

  // Create a user
  const userName = "test-user"
  createUser(userName);

  // Get the user that was just created, assert it's not Null
  const user = getUser(userName);
  expect(user).to.not.be(null)
}) End-to-End Tests It's an test when we test the , where all its dependencies are available. Those tests best simulate actual user behavior and allow you to catch in your app, but they are the type of tests. end-to-end fully deployed app all possible issues slowest Whenever you want to run end-to-end tests, you must provision all the infrastructure and make sure 3rd party providers are available in your environment. You want to have them for the features of your app. only mission-critical Login flow. We want to go to the app, fill in the login details, submit it, and see the welcome message. Let's take a look at an end-to-end test example: test('user can log in', () => {
  // Visit the login page
  page.goto('https://example.com/login');

  // Fill in the login form
  page.fill('#username', 'john');
  page.fill('#password', 'some-password');

  // Click the login button
  page.click('#login-button');

  // Assert the welcome message is visible
  page.assertTextVisible('Welcome, John!')
}) How Do You Choose What Kind of Test to Write? Remember that , and tests. end-to-end tests are slower than integration integration tests are slower than unit If the feature you are working on is mission-critical, consider writing at least one test (such as checking how the Login functionality works when developing the Authentication flow). end-to-end Besides mission-critical flows, we want to test as many edge cases and various states of the feature as possible. allow us to test how the parts of the app work together. Integration tests Endpoints should perform the operations, produce the expected result, and not throw any unexpected errors. Having integration tests for endpoints and client components is a good idea. Client components should display the correct content and respond to user interactions with how you expect them to respond. And finally, when should we choose ? All the small functions that can be tested in isolation, such as that sums the numbers, that renders tag, are great candidates for unit tests. Units are perfect if you follow the approach. unit tests sum Button <button> Test Driven Development What's Next? (but start small) Write some tests! that suits your project/language. Each language has a popular library for testing, such as / for JavaScript, / for end-to-end (uses JavaScript as well), for Java, etc. Install a testing framework Jest Vitest Cypress Playwright JUnit Find a small function in your project and write a test for it. unit Write an test for some component/service-database interaction. integration Choose a critical scenario that can be quickly tested, such as a simple login flow, and write an test for that. end-to-end Do the things above once to understand how it works. Then, do it again during some feature/bug work. Then share it with your colleagues so that you all write tests, save time, and sleep better at night! Useful Resources: My personal blog by The Practical Test Pyramid Ham Vocke by Test Driven Development Martin Fowler

Stable Software: The Power of Automated Tests

স্থিতিশীল সফ্টওয়্যার: স্বয়ংক্রিয় পরীক্ষার শক্তি সম্পর্কে জানুন

Stabile Software: Erfahren Sie mehr über die Leistungsfähigkeit automatisierter Tests

Software estable: conozca el poder de las pruebas automatizadas

Logiciel stable : découvrez la puissance des tests automatisés

स्थिर सॉफ़्टवेयर: स्वचालित परीक्षणों की शक्ति के बारे में जानें

安定したソフトウェア: 自動テストの威力について学ぶ

안정적인 소프트웨어: 자동화된 테스트의 힘에 대해 알아보세요

Software estável: aprenda sobre o poder dos testes automatizados

Стабильное программное обеспечение: узнайте о возможностях автоматизированных тестов

Kararlı Yazılım: Otomatik Testlerin Gücünü Öğrenin

Phần mềm ổn định: Tìm hiểu về sức mạnh của kiểm thử tự động

稳定的软件：了解自动化测试的力量

What Are Code Explainers? Code explainers are tools designed to make code more comprehensible. They work by breaking down complex lines of code into simpler, understandable chunks. They can highlight the logic behind each code block and depict the flow of data throughout the program. The ability to explain code is essential for software development teams to understand the functionality of the code, , and . carry out debugging collaborate efficiently takes this a step further. Automated code explainers are advanced tools that use various techniques, including AI and machine learning, to automatically interpret and explain the code. They not only explain the code but also suggest improvements and detect potential errors. This automation can transform and scale up the code review process. Automating code explainers Types of Automated Code Explainers Static Code Analysis Tools Static code analysis tools are software applications that examine source code without executing it. They identify potential errors, security vulnerabilities, and coding standard violations. These tools are effective for maintaining code quality and ensuring adherence to coding standards. They analyze the code for common programming errors, such as syntax errors, logical errors, and potential bugs. Static analysis tools can also check for compliance with coding conventions and guidelines, helping to maintain a consistent codebase. By using static analysis tools, developers can detect and fix problems early in the development process. This proactive approach reduces the likelihood of bugs and vulnerabilities in the final product. Additionally, these tools can automate the tedious and error-prone process of code review, enabling developers to focus on more complex tasks. Integration of static analysis tools into the continuous integration/continuous deployment (CI/CD) pipeline ensures continuous code quality checks throughout the development lifecycle. Code Visualization Tools Code visualization tools offer a graphical representation of the codebase, making it easier to understand complex code structures and relationships. They can display various aspects of the software, like class hierarchies, dependency graphs, and control flow diagrams. This visual approach helps developers comprehend the architecture and design patterns within the code, aiding in better decision-making during development and maintenance. These tools are particularly useful in large projects with intricate codebases, as they provide a macroscopic view of the software architecture. Visualization assists in identifying redundant or tightly coupled components, enabling refactoring efforts to improve code quality and maintainability. Code visualization tools also facilitate new team members' understanding of the existing codebase, speeding up the onboarding process. By integrating these tools into the development environment, teams can continuously monitor and analyze the evolving structure of their software. Automated Code Review Tools Automated code review tools are designed to analyze source code and suggest improvements, similar to a human reviewer. They check the code for common issues like coding standard violations, potential bugs, and performance issues. These tools use a combination of static code analysis, pattern recognition, and more recently, large language models (LLMs), to evaluate code quality and consistency. The use of automated code review tools streamlines the code review process, allowing teams to focus on more complex and subjective aspects of code quality. They also facilitate consistent code reviews across teams, reducing the variability that can occur with manual reviews. Integrating these tools into the development workflow can significantly speed up the review process and improve the overall quality of the codebase. Furthermore, automated reviews provide immediate feedback to developers, fostering a culture of continuous improvement and learning. AI Coding Assistants AI coding assistants use artificial intelligence and machine learning, most notably LLMs, to assist developers in writing code. They can suggest code completions, identify errors, and offer recommendations for code optimization. These assistants learn from a vast repository of code examples and adapt to the coding style and preferences of the developer. They can significantly speed up the coding process and reduce the likelihood of errors. AI coding assistants are particularly useful for repetitive coding tasks and boilerplate code, allowing developers to focus on more complex and creative aspects of software development. They also serve as an educational tool for novice developers, providing real-time feedback and suggestions. By integrating AI coding assistants into the development environment, developers can enhance their productivity and code quality. These assistants are continually evolving, becoming more sophisticated and helpful as they learn from more data and user interactions. When used as code explainers, AI coding assistants can dissect code line by line, providing explanations in plain language. They can clarify the purpose of specific functions, variables, and algorithms, making it easier for developers to grasp the underlying logic. This capability is invaluable for learning and understanding new codebases or programming paradigms. For example, when encountering an unfamiliar function or method, a developer can use an AI assistant to get an instant explanation of its purpose and usage, along with examples of similar code patterns. Benefits of Using Automated Code Explainers Efficiency in Understanding Code Automated code explainers can break down complicated code into simpler parts, making it easier for developers to grasp. They can also trace the flow of data and logic across the program, giving a visual representation of the code's functionality. Automated code explainers can also detect potential errors and bugs in the code. By using advanced analysis techniques and machine learning algorithms, they can foresee issues that might not be apparent to the human eye. This can save developers the time they would otherwise spend troubleshooting and debugging. Improved Collaboration Automated code explainers significantly improve collaboration among team members. By providing clear and concise explanations of the code, they ensure all team members have a shared understanding of the code. This shared understanding fosters better communication and collaboration among the team. Additionally, automated code explainers can serve as a neutral third party during code reviews. They can provide unbiased feedback on the code, preventing any personal biases from influencing the review process. This unbiased review promotes a fair and collaborative environment where the focus remains on improving the code. Learning and Development Automated code explainers are excellent learning tools for developers. They provide real-time feedback on the code, allowing developers to learn and improve as they code. Developers can see the impact of their code changes immediately, understanding what works and what doesn't. Moreover, for junior developers or those learning a new programming language, automated code explainers can serve as a valuable guide. They can help these developers understand complex code structures and patterns, accelerating their learning process. 5 Best Practices for Using Code Explainers Effectively 1. Integrate with Development Workflow Code explainers should be a part of your daily coding routine. For example, you can set up your explainer tool to automatically analyze your code whenever you commit changes. This approach ensures that you receive feedback on your code in real-time, allowing you to address any issues or inefficiencies promptly. 2. Regularly Review and Refine Suggestions Code explainers use static rules and machine learning algorithms to analyze your code and provide suggestions for improvement. As such, they are not infallible, and their suggestions should not be accepted blindly. Instead, you should take the time to review the suggestions and consider whether they make sense in the context of your code. You should also consider whether the suggested changes align with your coding style and the overall architecture of your project. 3. Maintain Code Readability While code explainers can help you identify potential issues and inefficiencies in your code, they can't replace the human understanding and intuition that is necessary to write readable and maintainable code. Readability is one of the most critical aspects of a good code. Readable code is easy to understand, debug, and maintain. Therefore, even though code explainers are available in your project, you should still take care to ensure that your code remains readable and understandable. 4. Balance Automation with Critical Thinking Remember, code explainers are simply tools that can provide useful insights and suggestions. They are not a substitute for human intelligence and intuition. It's important to remember that while code explainers can speed up the development process and improve code quality, they should not replace critical thinking. Always question the suggestions provided by the tool and use your judgment to decide whether they make sense in the context of your code. 5. Tailor the Settings to Suit Your Coding Style Most code explainers come with various settings that you can customize to fit your needs better. For example, you can adjust the sensitivity of the tool to control the number of suggestions it provides. Some types of tools allow you to customize the types of suggestions and additions they make to the code or even provide natural language instructions specifying how you want the tool to behave. In conclusion, automating code explainers can be a powerful ally in your software development process. However, to reap the maximum benefits of this technology, it's crucial to follow the best practices discussed in this article. By integrating the tool into your workflow, regularly reviewing and refining its suggestions, maintaining code readability, balancing automation with critical thinking, and tailoring the settings to suit your coding style, you can ensure that you get the most out of your code explainer tool.

Understanding Automated Code Explainers and Best Practices

Despite the evident advantages of automated testing in enhancing speed, precision, and cost-efficiency in specific scenarios, an intriguing statistic reveals that only . 5% of companies commit to fully automated testing In this article, we will unravel why manual testing remains an indispensable component of every project for the foreseeable future despite the relentless advancement of automated testing technologies. Manual Testing Manual testing forms the foundation for automation and is crucial in various scenarios: In situations where product functionality undergoes frequent changes, manual testing becomes pivotal. It offers the flexibility to swiftly adapt to changes without the burden of perpetually updating automated scripts. Rapidly Changing Functionality: Manual testing shines when dealing with intricate or ambiguous test scenarios, such as multi-tier user authentication processes. Manual testers excel in improvisation, navigating the application to uncover edge cases and modifying their test strategies. Complex for Automation or Undefined Test Scenarios: Manual testers provide critical insights into usability, design effectiveness, and overall user satisfaction, elements often missed by automated processes. UI/UX Testing: Automation may not be necessary on smaller projects where a few manual testers suffice. In such contexts, manual testing provides comprehensive coverage and is a more cost-effective and practical approach. Small-Scale Projects: Example: Evaluating a novel gesture-based interaction feature in a mobile app requires exclusively manual testing. This involves assessing the naturalness, responsiveness, and overall user satisfaction with these interactions, which automated tests cannot effectively measure. Testers manually execute gestures to navigate the app, evaluating the feature's intuitiveness and user experience firsthand. Automated Testing Automated testing excels in stable environments and for regression testing. It's ideal for scenarios where functionalities are consistent and manual testing might be limited due to high volume, complexity, or repetitive tasks. Automated tests can run independently, even overnight, providing quick, reliable results by the following day. This makes it a strategic choice for ensuring stability and efficiency in the testing process. Example: Automated testing is essential for stress testing a web application expected to handle high traffic, such as during a flash sale. It's impractical for manual testing to simulate the scale of simultaneous user activities that automated tools like JMeter can, ensuring the application's performance under peak load conditions. Comparative Analysis of Manual vs. Automated Testing Before delving into the central question of our article, let's examine the critical differences between automated and manual testing. This table summarizes the main distinctions between manual and automated testing, offering a quick overview of their strengths and challenges. The choice between them depends on project requirements, such as scale, complexity, and the need for immediate feedback. Most organizations opt for a hybrid approach, combining both methods to ensure comprehensive testing and high-quality software delivery. Why a Balanced Approach to Automated and Manual Testing Is Best for Projects Therefore, the most optimal approach for this project is the combination of manual and automated testing. Here’s why a balanced integration of automated and manual testing methodologies emerges as the best practice for projects: #1. The Base for Automation Manual testing serves as the bedrock upon which automated testing is built. Test cases and scenarios are validated manually to establish a baseline for expected behavior. These manual tests help identify critical features, user workflows, and potential areas of concern, which are then translated into automated test scripts. Without this initial manual testing phase, automating tests becomes a blind process lacking the human insights to detect early defects and vulnerabilities. __Manual __ test case example: Submit a «Contact Us» form with the valid data TITLE: STEPS: Navigate to the «Contact Us» page Enter the valid data Check the «Privacy Policy» checkbox Click the «Send Message» button The «Message is sent» success popup should be displayed EXPECTED RESULT: __Automation __ example (using Playwright and TypeScript): import { test } from '@playwright/test'; import ContactUsPage from '../pages/ContactUsPage'; const validFullName = 'Test Customer'; const validEmail = 'testemail@gmail.com'; const validMessage = 'It is interesting to learn about your services, get advice on my project and discuss the free trial in more detail.\nThank you!\nWaiting for feedback!'; test.describe('Contact Us form test', () => { test('Submit a «Contact Us» form with the valid data', async ({ page }) => { const contactUsPage = new ContactUsPage(page); // Navigate to the «Contact Us» page await contactUsPage.navigate(); // Enter the valid data await contactUsPage.enterFullName(validFullName); await contactUsPage.enterEmail(validEmail); await contactUsPage.enterMessage(validMessage); // Check the «Privacy Policy» checkbox await contactUsPage.clickPrivacyPolicyCheckbox(); // Click the «Send Message» button await contactUsPage.clickSendMessageButton(); // Verify success message is displayed await contactUsPage.isSuccessModalDisplayed(); }); }); #2. Accessibility Testing Ensuring that software is accessible to users with disabilities is a critical aspect of quality assurance. Manual testing allows testers to evaluate the application from the perspective of users with disabilities, including screen readers, keyboard navigation, and other assistive technologies. These aspects are challenging to automate fully, making manual testing essential to ensuring inclusivity and compliance with accessibility standards. consider a government website that provides essential information to citizens. Manual testing ensures that individuals with visual impairments can navigate the site using screen readers, access necessary forms, and receive critical updates. For instance, These functionalities are challenging to automate comprehensively due to the varied needs of users with disabilities, emphasizing the indispensability of manual accessibility testing in guaranteeing equal access to crucial information. #3. Risk Management By leveraging both testing methodologies, teams can ensure a more comprehensive risk assessment. Imagine a healthcare software project where manual testing is crucial for validating patient data input and ensuring the software's compliance with medical regulations. Automated testing complements this by verifying data integrity and the system's overall functionality. #4. Continuous Integration and Agile Environments Combining automated and manual testing in continuous integration (CI) and agile development environments ensures that quality keeps pace with development. For instance, consider a social media platform where automated tests verify core functionalities like user authentication, while manual testing focuses on user interface improvements and usability. #5. Comprehensive Bug Discovery Manual testing can uncover subtle, context-specific bugs that automated scripts may overlook. Testers can apply human intuition and creativity to explore the software from end-users perspectives, identifying issues that might not be covered in automated test cases. This aspect becomes crucial for ensuring a polished user experience, catching unexpected edge cases, and improving overall software quality. imagine testing an e-commerce website where an automated script checks the functionality of the "Add to Cart" button and appears to work flawlessly. However, during manual testing, a tester notices that when adding multiple items rapidly, the cart icon briefly flashes, potentially confusing users about the status of their selections. For instance, This nuanced issue, uncovered by human intuition and creativity, highlights the importance of manual testing in ensuring a user-friendly experience, improving overall software quality, and benefiting end-users. Сonclusions In conclusion, the ideal approach to software testing lies in a balanced integration of manual and automated testing methodologies. Manual testing, with its adaptability, user-centric insights, and ability to uncover nuanced issues, is the foundation upon which automation is built. Combining both approaches ensures comprehensive testing, high-quality software, and an improved user experience, making it the best practice for modern projects.

To Automate or Not: Navigating Manual and Automated Testing Paradigms

স্বয়ংক্রিয় বা না করা: ম্যানুয়াল এবং স্বয়ংক্রিয় পরীক্ষার দৃষ্টান্ত নেভিগেট করা

Automatisieren oder nicht: Navigieren in manuellen und automatisierten Testparadigmen

Automatizar o no: navegar por paradigmas de pruebas manuales y automatizadas

Automatiser ou non : naviguer dans les paradigmes de tests manuels et automatisés

स्वचालित करें या नहीं: मैनुअल और स्वचालित परीक्षण प्रतिमानों को नेविगेट करना

自動化するか否か: 手動テスト パラダイムと自動テスト パラダイムをナビゲートする

자동화 여부: 수동 및 자동 테스트 패러다임 탐색

Para Automatizar ou Não: Navegando nos Paradigmas de Testes Manuais e Automatizados

Автоматизировать или нет: ориентироваться в парадигмах ручного и автоматического тестирования

Otomatikleştirmek veya Otomatikleştirmemek: Manuel ve Otomatik Test Paradigmalarında Gezinme

Để tự động hóa hay không: Điều hướng các mô hình thử nghiệm thủ công và tự động

是否自动化：导航手动和自动测试范式

Building a DevOps Automated Deployment Pipeline using Flask, Aptible, CircleCI, and Prometheus

Authors: (1) Nowshin Nawar Arony; (2) Ze Shi Li; (3) Bowen Xu; (4) Daniela Damian. Table of Links Abstract & Introduction Motivation Related Work Methodology A Taxonomy of Inclusiveness Inclusiveness Concerns in Different Types of Apps Inclusiveness Across Different Sources of User Feedback Automated Identification of Inclusiveness User Feedback Discussion Conclusion & References 8 AUTOMATED IDENTIFICATION OF INCLUSIVENESS USER FEEDBACK In answering our fourth research question, How effective are pre-trained large language models in automatically identifying inclusiveness-related user feedback?, we assessed the effectiveness of the five LLMs, as detailed in Section 4.3 through evaluating the same dataset. We fine-tuned the pre-trained models on each of the sources and measured the performance in terms of precision, recall, F1-score, and accuracy. The performance results of the five models for each source are outlined in Table 3. We found that the overall evaluation results are best for Twitter user feedback. All the evaluation metrics are above 0.85 for Twitter for all five different classifiers. Among the five classifiers, we find BART to have the best results in terms of F1-score, achieving a value of 0.930. One possible reason for the better performance on Twitter may be that Twitter data has a lot of unrelated discussion (e.g. ads), making it relatively easier for the classifier to differentiate between inclusiveness and non-inclusiveness. Since there are many Twitter posts that consist of completely random dialogue or topics obviously unrelated to inclusiveness, this may help the models to classify the user feedback. We observe that the classification results for app reviews are not as good as Twitter’s (i.e., roughly 8-12% lower). For Play Store, the best performing classifier is BERT, with an F1-score of 0.849. There are many user feedback reviews that report bugs concerning the apps, but not all of these bug reports are about inclusiveness. This may be a reason for the increased difficulty for the classifiers to identify inclusiveness in app reviews. Finally, we observed the classifiers exhibiting a comparatively lower performance on Reddit in comparison to the other feedback sources. The best performing classifier for Reddit data is GPT-2, whereas the overall performance for classifiers for Reddit is roughly 8-14% lower than Twitter. The most likely reason why performance on Reddit is lower is that user feedback from Reddit is often complex and detailed in nature, which may contain an assortment of topic discussions. Identifying the inclusiveness aspect in a Reddit post is less clear cut than in a Twitter post or app review. This paper is under CC 4.0 license. available on arxiv

Automated Identification of Inclusiveness User Feedback: Testing the Effectiveness of 5 LLMs

You can jump to any section of the National Aeronautics Science & Technology Priorities here. II. Transforming the National Airspace System The U.S. Government will prioritize the efforts of the FAA’s NextGen Program to modernize the NAS. Emerging technologies, increasingly frequent commercial space launches, and high-altitude operations combined with increases in projected demand for traditional passenger travel and cargo shipments require a continued modernization of the air transportation system. The NAS of the near future will seamlessly manage complexity, become scalable, and dynamically adapt to a diverse set of users. Bolstered by the power of technology, this new paradigm will accommodate traditional aviation activities and the new entrants into the NAS. The safe integration of these aircraft at all levels of airspace challenges current monitoring systems; communications, navigation and surveillance infrastructure; and the regulatory environment. R&D will help mature these advanced technologies, supporting infrastructure, and the regulatory framework for the certification of new aircraft and technologies. As part of this transformation, the U.S. Government will prioritize the comprehensive integration of new aeronautics technologies, with the potential to transform aviation, in both urban and rural communities, creating new industries and jobs. These technologies include small drones and Advanced Air Mobility (AAM) vehicles, such as electric vertical takeoff and landing (eVTOL) aircraft, electric short takeoff and landing (eSTOL) aircraft, and other highly automated electric passenger or cargo carrying aircraft. The future NAS will incorporate strategies for the safe, efficient integration of civil and national security operations, ranging from slow, low altitude operations with observers to those conducted beyond the pilot’s Beyond Visual Line-Of-Sight (BVLOS) at a variety of altitudes and speeds. The U.S. Government will support updates to the regulatory framework for the certification of new aircraft and technologies, thereby fostering the safe, secure growth of the drone and AAM industries. The U.S. Government will continue to expand drone certification and integration to accommodate the multiple personal and business uses of drones in the NAS as well as support research and training at the many colleges and universities that offer drone programs. The U.S. Government will also promote the safety of pilots, passengers, and the general public by updating infrastructure, training, and certification processes to operationalize AAM services. The U.S. Government will support the development of scalable AAM solutions to permit the growth of applications such as cargo and package delivery and emergency services. It will maintain research facilities and upgrade as needed to keep pace with emerging technologies, including Vertical Lift Research Centers of Excellence, and test facilities such as flight simulation and wind tunnels. The future of flight is highly automated. Integrating autonomous processes and vehicles into the airspace of the future requires that autonomous vehicles have healthy self-monitoring systems and high-fidelity awareness of their surroundings, especially adjacent traffic. Certification of new vehicle architectures will need to ensure the reliability of the software and sensor suites to effect mature, safe, and stable flight. In October 2022, the Advanced Air Mobility Coordination and Leadership Act was signed into law (Pub. L 117-203)[6] to coordinate efforts to integrate new aviation technologies into the NAS. Consistent with the law, the Department of Transportation (DOT) will work with relevant Federal departments and agencies to create a national strategy to implement the law, while ensuring continued U.S. leadership in these technologies. [6] Congress, “Advanced Air Mobility Coordination and Leadership Act”. https://www.congress.gov/bill/117thcongress/senate-bill/516/text This was originally published in March 2023 on whitehouse.gov. This content has been broken-up into bite sized sections, each with unique headlines and AI-generated lead images, in line with the provisions of Creative Commons 3.0.

U.S. Initiates Overhaul of National Airspace for Automated Future

Automated deployment strategies have emerged as a game-changing method to simplify , empowering organizations to achieve release cycles and improve their development flexibility. This article delves into the details of automated deployment strategies examining their importance, advantages, and how they effectively tackle challenges. software delivery What Are Automated Deployment Strategies? Automated deployment strategies consist of various tools and techniques that streamline the process of deploying software applications to production environments. These strategies remove the requirements for intervention greatly reducing the potential for mistakes and minimizing downtime during deployment. By automating the deployment process organizations can guarantee consistency, reliability, and repetition, in their software delivery cycles. Significance of Automated Deployment Strategies The significance of automated deployment strategies lies in their ability to address several critical challenges faced by software development teams: Manual deployment processes are often time-consuming, error-prone, and require significant effort from developers and operations personnel. Automated deployment strategies eliminate these inefficiencies, allowing teams to focus on higher-value tasks. Manual Deployment Inefficiencies: Manual deployments often introduce bottlenecks in the release cycle, delaying the delivery of new features and updates to users. Automated deployment strategies accelerate release cycles, enabling organizations to respond rapidly to market demands and user feedback. Release Cycle Bottlenecks: Manual deployments often involve downtime, disrupting user access to applications. Automated deployment strategies minimize downtime, ensuring seamless and uninterrupted service delivery. Reduced Deployment Downtime: Automated deployment strategies ensure consistent and repeatable deployment processes, eliminating the risk of variations and inconsistencies that can lead to errors and failures. Improved Deployment Consistency: Challenges of Manual Deployment Processes Manual deployment processes pose several challenges that hinder software development agility and reliability: Manual deployments are susceptible to human error, which can lead to configuration mistakes, compatibility issues, and application downtime. Human Error: Manual deployments lack repeatability, as the process is often dependent on individual knowledge and experience. This can lead to inconsistencies and variations in deployment outcomes. Lack of Repeatability: Manual deployments require significant time and effort from developers and operations personnel, diverting resources away from more strategic initiatives. Time-Consuming Efforts: Manual deployments become increasingly challenging as the scale and complexity of software applications increase. Limited Scalability: Automated deployment strategies effectively address these challenges, paving the way for streamlined, reliable, and scalable software delivery. Types Of Automated Deployment Strategies Automated deployment strategies encompass a spectrum of approaches tailored to specific deployment scenarios and application requirements. Each strategy offers unique advantages and considerations, making the choice of deployment method crucial for ensuring optimal performance and user experience. Rolling deployment , also known as incremental deployment, involves releasing a new version of the application in a gradual manner, rolling out the update to a small subset of users first. This approach minimizes disruption and allows for continuous monitoring of the deployment's impact. If any issues arise, the deployment can be rolled back or halted, preventing widespread disruption. Rolling deployment Blue-green deployment involves maintaining two identical production environments, designated as "blue" and "green." When a new version is ready, it is deployed to the "green" environment, while the "blue" environment continues to serve live traffic. Once the new version is thoroughly tested and validated, the traffic is switched to the "green" environment, while the "blue" environment is decommissioned. This approach ensures seamless transitions and minimal downtime. Blue-green deployment Canary deployment Canary deployment is a technique that involves releasing a new version of the application to a small, controlled group of users, often referred to as "canaries." This group is selected to represent a diverse range of user demographics and usage patterns. By observing the canaries, organizations can detect potential issues early on before rolling out the update to a broader audience. A/B testing deployment deployment involves releasing two different versions of an application to separate groups of users. This approach enables organizations to compare the performance and user acceptance of the two versions. By analyzing user behavior and feedback, organizations can determine which version is more effective and make informed decisions about the wider rollout. A/B testing The choice of automated deployment strategy depends on various factors, including application architecture, deployment frequency, and risk tolerance. Rolling deployment is particularly suitable for applications that can tolerate minor disruptions, while blue-green deployment is ideal for mission-critical applications where downtime is unacceptable. Canary deployment is valuable for identifying potential issues early on, while A/B testing deployment is crucial for data-driven decision-making. Advantages and Disadvantages of Automated Deployment Strategies Each automated deployment strategy offers unique benefits and considerations, making the choice of deployment method crucial for ensuring optimal performance and user experience. Rolling Deployment Advantages: Minimal downtime and disruption Continuous monitoring and rollback capability Suitable for applications with high availability requirements Disadvantages: Requires careful configuration and monitoring May introduce performance degradation during deployment Not suitable for applications with strict consistency requirements Real-world example: Netflix utilizes rolling deployment to release new features and updates to its streaming platform. By gradually rolling out updates to a small subset of users, Netflix can monitor the impact of the update and quickly roll back if necessary, minimizing disruption to its global user base. Blue-Green Deployment Advantages: Zero downtime during deployments Seamless traffic switching Suitable for mission-critical applications Disadvantages: Requires maintaining two identical production environments May increase infrastructure costs Complexity of setup and maintenance Real-world example: 's e-commerce platform, Amazon.com, employs blue-green deployment to ensure seamless transitions during major software releases. By maintaining two identical production environments, Amazon can thoroughly test new versions without impacting live traffic, ensuring a smooth and reliable user experience. Amazon Canary Deployment Advantages: Early detection of potential issues Controlled rollout to a limited audience Suitable for identifying performance bottlenecks Disadvantages: May introduce performance variations Requires careful selection of canary users Not suitable for applications with strict consistency requirements Real-world example: Google utilizes canary deployment to release new features to its search engine. By deploying new features to a small group of canary users, Google can gather feedback and identify potential issues before rolling out the update to a broader audience, ensuring a high-quality user experience. A/B Testing Deployment Advantages: Data-driven decision-making Comparison of different feature implementations Suitable for optimizing user engagement and conversion Disadvantages: Requires careful experiment design and analysis May introduce performance variations Not suitable for applications with strict consistency requirements Real-world example: E-commerce platforms like Shopify and Etsy use A/B testing deployment to optimize their checkout processes and landing pages. By comparing different versions of these pages, e-commerce platforms can identify the most effective design and maximize conversion rates. Implementing Automated Deployment Pipelines Automated deployment pipelines are the backbone of modern software delivery, enabling organizations to streamline the process of releasing new software to production environments. These pipelines encompass a series of automated steps that transform code changes into deployable artifacts, test the artifacts, and deploy them to the target environment. Key Components of Automated Deployment Pipelines Effective automated deployment pipelines typically consist of the following key components: CI involves automatically building and testing code changes as they are committed to a version control system. This ensures that code is always in a buildable and testable state. Continuous Integration (CI): CD extends CI by automatically deploying tested code changes to production environments. This minimizes the time between code changes and deployment, enabling rapid delivery of new features and updates. Continuous Delivery (CD): IaC treats infrastructure as software, enabling automated provisioning and management of infrastructure resources. This ensures that infrastructure is consistently configured and aligned with the application's requirements. Infrastructure as Code (IaC): Continuous monitoring and logging are crucial for tracking the health and performance of deployments. This enables early detection of issues and prompt corrective actions. Monitoring and Logging: Tools and Technologies for Automated Deployment A rich ecosystem of tools and technologies supports automated deployment pipelines. Some popular examples include: Jenkins, GitLab CI/CD, Azure DevOps Pipelines, CircleCI CI/CD tools: Terraform, Ansible, Chef, Puppet Infrastructure as Code tools: Ansible, Chef, Puppet, SaltStack Configuration management tools: Prometheus, Grafana, Splunk, ELK Stack Monitoring and logging tools: Building and Implementing Automated Deployment Pipelines The process of building and implementing automated deployment pipelines typically involves the following steps: Determine the stages of the deployment pipeline, such as development, testing, staging, and production. Define deployment stages: Integrate CI tools to automatically build and test code changes as they are committed. Automate code building and testing: Configure CD tools to automatically deploy tested code changes to a staging environment for final validation. Automate deployment to staging: Utilize IaC tools to automate the provisioning and configuration of infrastructure resources. Automate infrastructure provisioning: Integrate monitoring and logging tools to provide continuous visibility into deployment health and performance. Implement continuous monitoring: Establish automated rollback and recovery procedures to handle deployment failures. Automate rollback and recovery: Continuously refine and improve the deployment pipeline based on experience and feedback. Continuous improvement: By following these steps and leveraging appropriate tools and technologies, organizations can effectively implement automated deployment pipelines, enabling rapid, reliable, and consistent software delivery. Conclusion Automated deployment strategies have completely transformed the landscape of software development empowering organizations to achieve progress and bring value to users at an unprecedented pace and effectiveness. By automating the process of deploying software organizations can eliminate the potential for mistakes, reduce downtime and guarantee an experience, across different environments. The key takeaways from this article are as follows: Automated deployment strategies encompass a range of techniques that automate the process of releasing new software versions to production environments. Different deployment strategies, such as rolling deployment, blue-green deployment, canary deployment, and A/B testing deployment, offer unique advantages and considerations tailored to specific deployment scenarios and application requirements. Automated deployment pipelines are the backbone of modern software delivery, enabling organizations to streamline the process of releasing new features and updates. Implementing automated deployment pipelines requires careful planning, selection of appropriate tools and technologies, and continuous improvement based on experience and feedback. Automated deployment strategies are not just a trend; they are an essential component of modern software development practices. By embracing automation, organizations can unlock the full potential of agile methodologies and deliver software that meets the ever-changing demands of today's digital world. For further learning and exploration, consider the following resources: Continuous Delivery: Reliable Software Releases in the Age of DevOps: Here The Phoenix Project: A Novel About IT Operations: Here

Efficient Strategies for Automated Deployment

Building an Analysis Service for Automated Test Failures Using OpenAI

Mobile devices have become a go-to solution for most businesses these days. From managing finances to grocery shopping or managing operations, the mobile is a handy solution to all the operations. Therefore, it has become more important than ever to create applications that can offer flawless output and the desired user experience. However, mobile application development has become more complicated with growing functionalities and end-user demands. According to , one in four mobile applications contains at least one high-risk security flaw. Besides, 50% of apps that have five to ten million downloads include some security flaws. reports Here comes the roles of mobile testing and advanced test automation practices like automated screenshot testing. Actually, visual and test automation go hand in hand when it comes to the lifecycle. While functional tests ensure the app performs as expected, visual tests focus on user experience, ensuring the UI elements are correctly rendered and positioned. mobile app development Therefore, visual testing is one of the most significant components of comprehensive mobile app testing. This blog will delve into the world of Automated Screenshot Testing, emphasizing its vital role in both visual and test automation and shedding light on its advantages, best practices, test setup, tools, and more. Let's begin! Mobile App Automation With Screenshot Testing Automated Screenshot Testing is a game-changer in mobile app testing. But before we jump to the details surrounding automated screenshot testing, it is necessary to understand the term. Automated screenshot testing as a test approach enables developers and QA teams to capture screenshots of different screens within the app automatically. These screenshots further serve as visual checkpoints, allowing for quick identification of any deviations from the expected UI. Wondering how it incorporates the UI Testing Component? Automated Screenshot Testing is not just about capturing screenshots but also involves UI testing. By comparing the screenshots against predefined baseline images, the technique ensures that the app's UI remains consistent across various devices and operating systems. In other words, screenshot testing is instrumental in identifying UI regressions and preventing visual glitches. Advantages Of Screenshot Testing In Mobile Automation One of the most significant reasons automated screenshot testing is implemented is its potential to enable QA teams to identify any unintended changes in the UI swiftly. Besides, it helps catch regressions early in the development cycle, preventing them from reaching the end-users. Some of the benefits that testers could enjoy with automated screenshot testing could be listed as: Cross-Platform Compatibility Assurance With the multitude of devices and platforms available, ensuring consistent UI across them can be challenging. Screenshot testing provides a reliable way to verify cross-platform compatibility, ensuring a seamless user experience. Efficient Bug Detection Screenshot testing complements functional testing by visually highlighting any anomalies. The process makes bug detection more efficient and precise, reducing the time and effort required for debugging. Improved Collaboration The visual nature of screenshot testing facilitates more transparent communication between developers, designers, and QA teams. Also, it provides a tangible reference point for discussing UI-related issues complementing the test process and development. Enhanced User Satisfaction When testers need to attain a polished and visually appealing UI to meet user satisfaction goals, automated screenshot testing can be harnessed to boost app retention rates. Best Practices For Automated Screenshot Testing While investing in automated screenshot testing is a consuming task for most test teams, implementing the same in the QA workflow could deliver the necessary speed and accuracy required to create mobile applications with rich UI and visual flawlessness. Keep Tests Atomic and Isolated Each test case should focus on a specific functionality or UI component. It helps in confirming a clean and organized test suite. Use Test Data Generation Automate the process of generating test data to ensure consistent and repeatable results. It helps in avoiding false positives in the visual tests. Leverage Headless Browsers Headless browsers allow you to run tests in a simulated environment without a graphical user interface. It significantly speeds up the testing process and saves the consumption of system resources. Regularly Review and Update Tests As your application evolves, it's important to review and update your test cases to reflect any changes in the UI or functionality. Screenshot testing helps to fasten the test process, cutting off the latencies. Setting Up Automated Screenshot Testing Step 1: Choose a Testing Framework There are several popular testing frameworks available for automated screenshot testing. Some of the widely used ones include: : A Node.js library developed by Google that provides a high-level API for headless Chrome. Puppeteer : A versatile browser automation framework that supports multiple programming languages. Selenium : A JavaScript end-to-end testing framework that also provides screenshot-capturing capabilities. Cypress It is important to choose a framework that aligns with a project's specific requirements and the selection of programming languages. Step 2: Write Test Cases Once you've selected a testing framework, it's time to start writing test cases. These test cases should focus on the critical paths and UI components of your application. For example, capture screenshots of the homepage, product pages, and all other interactive elements. Step 3: Capture Screenshots Using the chosen testing framework, you can programmatically navigate the application and capture screenshots at specific points in the defined test cases. Make sure to organize all the screenshots in a structured way, allowing quick comparison between different versions. Step 4: Implement Visual Regression Testing To perform visual regression testing, you'll need a tool that can compare screenshots and highlight any differences. There are specialized tools available, like Applitools and Percy, that integrate seamlessly with testing frameworks. These tools provide powerful features such as pixel-by-pixel comparison, image diffing, and visual reporting. Besides, one can even switch to advanced AI-driven test automation tools like BugBot to meet their regression goals through visual testing. Step 5: Set Up Continuous Integration Integrating automated screenshot testing into your continuous integration (CI) pipeline ensures that your visual tests are executed automatically whenever there's a code change. It helps to catch any visual regressions early in the development process, reducing the likelihood of shipping a flawed user interface. Screenshot Testing Using Selenium When it comes to Automated Screenshot Testing, one name stands out: Selenium. Selenium, as an open-source testing framework, is renowned for its versatility and robust capabilities. Besides, Selenium's WebDriver API enables seamless interaction with web elements, making it an ideal choice for capturing screenshots during automated tests. Unique Features of Selenium for Screenshot Testing Cross-Browser Compatibility Selenium supports various browsers, ensuring screenshots are captured consistently across different platforms. Flexible Language Support Selenium can be used with diverse programming languages, like Java, Python, C#, and more, offering flexibility to development teams. Integration with Testing Frameworks Selenium seamlessly integrates with popular testing frameworks like JUnit and TestNG, streamlining the automation process. Extensive Community Support The vast community around Selenium provides access to a wealth of resources, making it easier to troubleshoot and find solutions to testing challenges. Conclusion In conclusion, automated screenshot testing is a powerful technique for ensuring the visual consistency of your web applications across different browsers and devices. Also, implementing the practice in the development workflow could complement catching visual regressions early, leading to a smooth user experience and higher customer satisfaction. Besides, it is important to remember that gaining maximum effectiveness demands choosing the right testing framework, writing effective test cases, and leveraging visual regression testing tools precisely. After all, it is only the right approach and practice that turns out to be an invaluable asset in your toolkit for delivering high-quality web applications, especially when you are deploying advanced practices like automated screenshot testing. Good Luck!

Mastering Mobile App Testing with Automated Screenshot Testing

In the era where decisions hinge on data, gleaning valuable insights from documents has become crucial for businesses in various industries. AWS Textract, a cutting-edge service from , is a potent instrument for extracting text from documents. Amazon Web Services Thanks to its sophisticated machine learning capabilities, AWS Textract can process an array of document formats, like images and PDFs, to extract text and data efficiently and accurately. This piece delves into how harnessing AWS Textract can simplify document processing and transform information extraction, driving businesses toward heightened efficiency and triumph. What Does AWS Textract Entail? represents a cloud-based service courtesy of Amazon Web Services. This service employs sophisticated machine learning algorithms to process documents, extracting text and data. AWS Textract It demonstrates compatibility with a broad spectrum of document formats, such as scanned images and PDF files, thus showcasing adaptability to diverse business requirements. The Benefits of AWS Textract Manual data extraction from documents can prove to be an exhaustive task. AWS Textract can process large documents quickly, significantly reducing the time needed for data extraction. Time Conservation: AWS Textract employs machine learning capabilities to achieve excellent text and data recognition accuracy. This reduces the likelihood of errors and guarantees dependable data extraction. Superior Precision: Once operational, AWS Textract can autonomously extract information from incoming documents, refining business processes and lessening the need for manual intervention. Automated Information Extraction: Using AWS Textract for Document Text Extraction Step 1: AWS Account Setup Initiate your journey with AWS Textract by establishing an AWS account, assuming one still needs to exist. Upon account creation, the AWS Management Console becomes accessible, allowing you to enable and utilize AWS Textract. Step 2: Engage With AWS Textract Post login to the AWS Management Console, steer towards the AWS Textract service page to take advantage of its features. Step 3: Selection of Input Method Choosing between synchronous API and asynchronous API hinges on your document processing requirements. The synchronous API caters to the immediate processing of smaller documents, while the asynchronous API is preferable for larger documents. Step 4: Preparation of Input Documents Ready the documents for processing using AWS Textract. Acceptable formats encompass JPEG or PNG images and PDF files. Step 5: Initiation of Document Processing Job For handling larger documents, launch a job using the asynchronous API. AWS Textract will handle the document processing and reserve the extracted data for later access. Step 6: Collection of Results Upon completion of processing, the extraction of text and data is possible. The output lands in a structured format, simplifying interactions with the data. Step 7: Pagination Management In the event of a document extending over multiple pages, AWS Textract manages pagination, enabling results retrieval from all pages. Step 8: Additional Processing of Results (Optional) The extracted data might require further post-processing to meet specific needs, including data validation or normalization. Step 9: Cleaning Proceeding (Optional) Should the extracted data or processed documents become redundant, deletion of the corresponding AWS Textract resources is possible to prevent unwarranted expenses. Conclusion In conclusion, AWS Textract brings a new era of document text extraction with automation, precision, and rapidity. Businesses can capitalize on this potent service to enhance their operations, lessen manual labor, and unearth meaningful insights from the data concealed within their documents. AWS Textract offers a smart and effective option for cloud-based document text extraction, from processing invoices and pulling information from forms to digitizing historical records.

Automated Document Text Extraction With AWS Textract

To achieve reliable visual testing, it’s crucial to prevent unreliable tests by effectively hiding dynamic parts of your application. When dealing with elements prone to change, like ad banners, moving text inputs, GIFs, and user cursors, it’s essential to use dynamic element masking. This means hiding these elements before taking screenshots during to avoid false results due to dynamic content. testing Let’s use an example and imagine we want to assess the layout of the Stories section through visual testing. Facebook The challenge here is that the images and titles in the stories tray element will differ whenever you reload the page. This isn’t ideal for visual testing. Modern testing frameworks provide methods for this. For instance, Playwright lets you hide specific page elements when taking screenshots by completely covering its bounding box with a #FF00FF color. Let’s try to hide all <img> elements before making a screenshot. let mask_locator = page.locator("//img")
await page.screenshot({path: 'masked.png', mask: [mask_locator]}) The problem with such an approach is that it completely covers your element bounding box, so since every element is a rectangle, you will see something like that. HTML Default Playwright masking is excellent for simple rectangle ad banners and buttons, but you want something better and more accurate for complex layouts. For instance, you can toggle the visibility of images in this section because they aren’t necessary for layout testing. To implement this, please review the script provided below. // Get all img elements on the page
var imgElements = document.getElementsByTagName('img');

// Loop through each img element and hide it
for (var i = 0; i < imgElements.length; i++) {
    imgElements[i].style.display = 'none';
}
// Get the element with aria-label="stories tray"
var storiesTray = document.querySelector('[aria-label="stories tray"]');
// Check if the stories tray element exists
if (storiesTray) {
    // Get all span elements inside the stories tray
    var spanElements = storiesTray.querySelectorAll('span');
    // Loop through each span element inside the stories tray and replace its text content with "test."
    spanElements.forEach(function(span) {
        span.textContent = 'test';
    });
} This script hides all <img> elements and changes the story titles into “test” because we don’t need to check the text value, just a layout. After executing such a script, the section will resemble a clean layout that you can capture in a screenshot and use for comparison in your visual tests. If you don’t want to hide such elements or want to highlight hidden areas, you can fill it with any color, i.e., #FF00FF, using the next script: // Get all img elements on the page
const imgElements = document.querySelectorAll('img');

// Loop through each img element and set its background color to red
imgElements.forEach((img) => {
  img.style.backgroundColor = '#FF00FF';
  img.src = ''; // Optional: Remove the image source to hide the original image
});

// Get the element with aria-label="stories tray"
var storiesTray = document.querySelector('[aria-label="stories tray"]');
// Check if the stories tray element exists
if (storiesTray) {
    // Get all span elements inside the stories tray
    var spanElements = storiesTray.querySelectorAll('span');
    // Loop through each span element inside the stories tray and replace its text content with "test"
    spanElements.forEach(function(span) {
        span.textContent = 'test';
    });
} Embracing this technique ensures stable and trustworthy visual tests, allowing you to detect real issues without distractions from transient visual elements. Also published here.

How to Mask Dynamic Elements in Automated Visual Tests

Roundabouts are circular intersections that can replace traditional four-way junctions. Vehicles travel around the center island to merge and diverge from different directions. When designed well, roundabouts can improve traffic flow and safety over other intersections. However, optimizing their layout requires extensive testing and validation - a costly endeavor, especially for developing countries. , researchers demonstrate how could automate roundabout design generation, enabling more efficient exploration of diverse, realistic options. What makes this problem suited for an AI solution, and how did they reach their conclusions? Let's find out. In a new paper AI The Critical Role of Roundabouts Roundabouts provide key benefits over regular intersections. Their circular design forces vehicles to slow down, reducing the likelihood of dangerous right-angle and head-on collisions. Studies show roundabouts can achieve a 37-75% reduction in overall collisions compared to intersections. Deaths and injuries are lowered by around 90% since crashes are less severe. Roundabouts also keep traffic flowing smoothly. Unlike intersections, vehicles don't have to fully stop if no other cars are present. And the circular design eliminates dangerous left turns across oncoming traffic. This cuts congestion and reduces pollution from idling vehicles. These safety and efficiency benefits make roundabouts an essential component of transportation infrastructure. They serve an important role within networks of roads, highways, pedestrian crossings* * and more. , The Challenges of Roundabout Design But roundabouts have to be meticulously designed to realize their full potential. Small tweaks to the geometry and lanes can significantly impact safety and traffic flow. For example, inadequate acceleration/deceleration lanes or tight turning radii increase crash risks and bottlenecks. But over-designed roundabouts take up too much space and money. Optimizing roundabout layout requires modeling many scenarios and constraints. Road curvature, lane count, pedestrian crossings, size, and connections must balance safety, traffic throughput, cost, and aesthetics. Slight changes can affect all of these factors, as roundabouts form interconnected systems. Manually testing alternatives is cumbersome and expensive. Developing countries especially lack the expertise and resources for extensive physical modeling and simulation. Procedural generation using AI could provide a more efficient solution. Rule-Based Methods Fall Short To date, roundabout generators rely on hand-coded rules and constraints. But this limits the diversity of designs explored, hindering robustness. Researchers argue more sampling-based, "generate-and-test" approaches are needed. Their key insight is that the diversity of options itself should be rewarded, not just validity. Expanding the search space surface reveals a multitude of high-performing local optima. Greater diversity also improves resilience should constraints change in the future. Exhaustively testing every potential roundabout is computationally infeasible. Generating detailed simulations to assess each design is prohibitively slow. Can AI enable efficient search across diverse, performant possibilities? Generative Flow Networks for Automated Exploration Enter Generative Flow Networks (GFlowNets) - the proposed in this paper. AI architecture GFlowNets can learn policies to efficiently sample from complex, high-dimensional search spaces. The key is focusing on high-reward areas, avoiding exhaustively enumerating all invalid candidates. GFlowNets works by incrementally constructing roundabout designs step-by-step. The sequencing of these constructions is learned to maximize a reward tied to realism and lack of collisions. In this way, the model explores the space guided by quality while capturing diverse modes and avoiding unproductive regions. The team formulated roundabout generation as a Markov Decision Process to determine this sequential building. Actions connect incoming roads to different points along the central circular junction. The policy opts for actions that probabilistically maximize valid connections without overlaps. Notably, the GFlowNets architecture also includes an approximation scheme. Rather than a time-intensive simulation, it first rewards configurations based on simpler intersection checks between road geometries. This allows much faster training to focus sampling on promising areas. The most highly rewarded designs can then be validated through full simulation. GFlowNets Enable Efficient and Diverse Generation The researchers tested GFlowNets on synthesizing roundabouts for simulated road scenarios of varying complexity. They also compared against rule-based algorithms and standard reinforcement learning techniques. GFlowNets could successfully generate valid and diverse roundabouts for all road arrangements. As road connections increased, the model continued to produce varied options without compromising quality. The approximations also accelerated training by over 200x compared to simulation-based rewards. Overall, GFlowNets exceeded other methods on combined diversity and realism metrics. This demonstrates the value of guided generative modeling for exploring expansive design spaces. By focusing on high-performing regions, near-optimal alternatives can be automatically discovered. The Future of AI-Assisted Infrastructure Design I thought this paper was mostly interesting because this research provides a promising proof-of-concept for automated roundabout generation. By incorporating road simulator engines, the system could be further enhanced to handle more constraints and accurate evaluation. The sampling-based approach also enables continuous improvement - new constraints can be added over time to generate and assess more options. And the concepts could be generalized to designing other road structures. Of course, thorough real-world testing is still required before deploying such tools. But by unlocking new efficiencies, AI design assistants could make high-quality transportation infrastructure more accessible worldwide. This study offers an exciting glimpse into that future. Also published . here

Revolutionizing Roadway Design: AI's Role in Automated Roundabout Generation

Ayse’s hand landed firmly on Mert’s desk, causing papers to scatter everywhere. The morning sun highlighted the mess in the room. Clothes, books, and various items were scattered everywhere, a testament to a teenager’s whirlwind life. “Mert!” Ayse’s voice was filled with frustration. “How many times have I told you about this room? How can you find anything in here?” Mert, looking defeated, responded, “I’ve searched everywhere, Mom. I can’t find it.” Ayse took a deep breath and stepped into the room. “Describe the book to me,” she said, her voice calmer. He thought for a moment. “It’s this size,” he showed with his hands. “It has a blue cover. I drew a maze on it with a silver pen. There’s also a red ribbon bookmark.” Ayse began looking around, hoping to spot the book. Mert just watched, his energy spent from searching. As she moved to a pile of bags near the closet, Ayse’s fingers brushed against the familiar texture of a book’s spine. She pulled out Mert’s weekend bag, the one he used for visits to his father’s house. Unzipping it, her heart leapt as she spotted the blue cover with the intricate silver labyrinth. “Here it is!” Ayse exclaimed. “Why on earth was it in this bag?” He looked away, slightly embarrassed. “I took it to Dad’s,” he said. “I wanted to show him.” She handed him the book, her face showing a mix of emotions. “We need to leave,” she said, checking the time. “Now.” Mert held the book to his chest as they left the room, the atmosphere thick with unspoken feelings. The tram’s rhythmic hum provided a backdrop as Ayse and Mert found seats next to each other. The city passed by in a blur outside the window, but inside, the tension was palpable. “This can’t continue, Mert,” Ayse began, her voice carrying a mix of concern and frustration. Mert, feeling the weight of the morning’s events, nodded. “I know, Mom. I’ll try harder.” She started to give him a detailed breakdown, “Your clothes should go in the wardrobe, books on the shelf, toys in the box…” But she stopped herself mid-sentence, realizing the futility of her words. “No, this isn’t the solution. It won’t help with the mess.” Distracting herself, Ayse began to rummage through her purse. Her fingers brushed against some old business cards. She pulled them out, hoping to divert her mind from the current situation. Glancing at one, she read aloud, “System analyst.” An idea began to form. Grabbing a pen, she scribbled on the back of the cards. Mert watched curiously, his earlier despondency replaced by intrigue. Ayse cleared her throat, “This is my adventure game for you.” She held up the cards, reading them one by one: “All items have a home. Return them after use.” “Daily declutter: Spend 5 minutes before bed putting things away.” “Group like items together: All books in one place, all toys in another.” “If you haven’t used it in a month, consider if you really need it.” Mert looked at the cards, then back at his mother. “It’s like a game?” Ayse smiled, “Exactly. Think of it as leveling up in real life. Each step helps you maintain order and makes your life easier.” He pondered for a moment, then nodded. “Okay, I’ll give it a try.” The tram had come to a stop, and Ayse stepped out, heading towards her office building. As she approached the entrance, she spotted Jamal, her colleague, waiting for her. “Morning, Ayse,” he greeted with a smile. “Morning, Jamal,” she replied, still thinking about the conversation with Mert. Jamal, always eager to discuss work, began, “You remember the ‘Vladimir’s revenge’ section of our codebase?” Ayse chuckled, “How could I forget? That legacy code has been a thorn in our side for ages.” Jamal nodded, “Exactly. So, I’ve been thinking about the evolution of ” refactoring. Ayse raised an eyebrow, intrigued. “Go on.” “Well,” Jamal began, counting off on his fingers, “First, we did it manually. Like, if we changed the parameters of a function, we’d search for every instance of that function and update it manually.” Ayse grimaced, “Time-consuming and error-prone.” “Exactly. Then, we moved to regex, using it to replace instances across multiple files.” Ayse nodded, “A bit better, but still not foolproof.” “Right. And then, we started using the IDE. It has some basic refactoring techniques, like adding a parameter or renaming a function.” Ayse recalled the countless hours they’d spent on ‘Vladimir’s revenge’. “But we were still stuck with that part of the code.” Jamal’s eyes lit up, “That’s when I did some digging and found out about tools that work with the AST.” Ayse frowned, “AST?” “Abstract Syntax Tree,” Jamal explained. “It’s a tree representation of the structure of code. Instead of seeing code as just text, AST sees it as a structure with nodes representing different elements, like functions, variables, and so on.” Jamal could see the curiosity mixed with confusion in Ayse’s eyes. “Let me give you a simple example to illustrate AST,” he said, pulling out a notepad. He quickly scribbled down a basic code snippet: function add(x, y) {
    return x + y;
} “This is a simple function in JavaScript,” Jamal began. “Now, when this code is parsed into an Abstract Syntax Tree, it’s broken down into a structured format.” He drew a tree diagram: FunctionDeclaration
│
├─ Identifier (name: "add")
│
├─ Parameters
│   ├─ Identifier (name: "x")
│   └─ Identifier (name: "y")
│
└─ BlockStatement
    └─ ReturnStatement
        └─ BinaryExpression
            ├─ Identifier (left: "x")
            ├─ Operator: "+"
            └─ Identifier (right: "y") “This tree,” Jamal continued, “represents the structure of our code. Each node corresponds to a part of the code. For instance, FunctionDeclaration represents our function, Identifier nodes represent variable or function names, and BinaryExpression represents the addition operation.” Ayse studied the tree, “So, when we refactor using tools that understand AST, they’re working with this structured representation rather than just text. That’s why it’s more accurate?” “Exactly,” Jamal confirmed. “If we wanted to rename the function from add to sum, an AST-based tool would look for the Identifier node with the name ‘add’ under FunctionDeclaration and change it there. This ensures that only the function name is changed and not any other variable named ‘add’ in the code.” Ayse’s face lit up with understanding. “That’s brilliant. It’s like having a blueprint of a building and making changes to it, rather than just painting over the exterior.” Jamal nodded, pleased. “Precisely. It’s a more intelligent way of refactoring, especially for complex codebases like ours.” Ayse smiled, “Thanks for the crash course, Jamal. Let’s put an end to ‘Vladimir’s revenge’ once and for all.” Ayse’s initial enthusiasm began to wane as the implications of what Jamal was suggesting settled in. “Wait a minute,” she said, her brow furrowing. “Doesn’t this mean we’ll have to write a tool to convert our source code into this AST format? And then another tool to manipulate the AST? And yet another to convert the modified AST back into source code? That sounds like a lot of work.” Jamal chuckled softly, sensing her apprehension. “I understand why you’d think that, but no, we don’t have to start from scratch. Every programming language has a parser that constructs an AST. It’s a fundamental part of how compilers and interpreters work.” He continued, “For instance, in JavaScript, there’s a tool called ‘Esprima’ that can parse code into AST. And there are libraries like ‘Recast’ that allow us to manipulate the AST and then convert it back into source code.” Ayse looked relieved. “So, we don’t have to reinvent the wheel?” Jamal shook his head, “Not at all. The tools are out there. We just need to leverage them. Think of it as assembling a puzzle with most of the pieces already provided.” The tram rumbled softly beneath them as Ayse and Mert settled into their seats. Despite the morning bustle, both seemed notably calmer than the previous week. In Mert’s hands was a sleek pen holder, its design hinting at the precision of a 3D printer. He looked pleased with it. Ayse, recognizing it, commented, “That’s the pen holder from your tech project, right?” “Yeah,” Mert replied, a small smile forming. “And I found my design notes and the USB without having to turn my room upside down.” Ayse gave a half-smile, “Seems like the game had some effect.” Mert nodded, looking at the pen holder. “It made organizing less tedious. Projects like this became simpler.” Ayse patted his hand gently. “It’s good to see. Both the tidier room and this impressive piece.” Mert just shrugged modestly, “Feels good to have things in order.” As the tram continued its journey, Ayse’s gaze wandered from the pen holder to the organized layout of the city outside. The structured design of the holder began to remind her of something else, something related to their codebase. A thought began to crystallize in her mind. Without a word, she rummaged in her purse and pulled out the business cards, scribbling down the nascent idea. Mert, noticing her sudden preoccupation, queried, “Something on your mind, Mom?” Ayse looked up, her thoughts still forming. “I might be onto something for work. Just connecting some dots.” In the open-concept office, Ayse approached Jamal’s desk, her steps purposeful. The hum of keyboards and hushed conversations filled the space, but for Ayse, the world had narrowed down to the idea she had scribbled on the back of a business card. Jamal looked up from his monitor, noticing her approach. “Hey, Ayse. You look like you’ve had an epiphany.” She smiled, holding up the business card. “Remember how we talked about refactoring using the AST?” Jamal nodded, leaning back in his chair. “Of course. What’s on your mind?” Ayse took a deep breath, laying out her idea. “Instead of directly refactoring the source code and then opening a PR with those changes, what if we took a different approach? We could write a program that does the refactoring for us. This program, or meta-program, would be what we review.” Jamal raised an eyebrow, intrigued. “Go on.” Ayse continued, her excitement evident, “Once we’re satisfied with the meta-program, we integrate it into our CI/CD pipeline. Here’s how it would work:” “We push the meta-program to a dedicated branch.” “The CI/CD pipeline picks it up and runs the transformation on our codebase, converting it using the AST logic we’ve defined.” “Automated tests are then run on the transformed code. If everything passes, the pipeline automatically merges the changes into the main branch. If there are issues, it flags them for us to review.” Jamal leaned forward, his interest piqued. “So, instead of reviewing the refactored code, we review the logic that does the refactoring. It’s like… setting the rules and letting the system apply them.” “Exactly,” Ayse confirmed. ” Think of it like a database migration, but for our source code. It could streamline our process, reduce human error, and ensure consistency. Plus, it would save us countless hours of manual code review. We’d be reviewing the transformation logic, not every single line of changed code.” Jamal thought for a moment, then nodded. “It’s a bold approach, but it might just work. It’s like teaching the system how to clean its own room but on a much larger scale.” Ayse chuckled, “Inspired by real-life events.” [The next part of this story is ] Proglogging: The Developer's Detective Toolkit

Code That Cleans Itself: A Journey into Automated Refactoring

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