How has AI changed web testing?

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Web testing is a crucial process for ensuring the quality, functionality, and usability of web applications. However, web testing can also be challenging, time-consuming, and prone to errors, especially as web applications become more complex, dynamic, and diverse. That's why many web testers are turning to artificial intelligence (AI) to help them automate, optimize, and improve their web testing practices. In this article, we'll explore how AI has changed web testing and what benefits and challenges it brings.

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Muhammad Usman Sharif

Senior QA Engineer | Fintech | Manual Testing | Automation Testing | API Testing
Abhishek Roy

"ISTQB Certified QA Lead | Advanced Lean Six Sigma & Azure Certified | AINS & Duck Creek Certified BA and Configurator|…
Muneeb Mazhar

Sr.SQA Engineer|Database Testing|SOAP & Rest API| Functional/Non Functional|Web, Desktop Mobile Apps|Integration…

1 AI and web testing

AI is the branch of computer science that deals with creating systems that can perform tasks that normally require human intelligence, such as learning, reasoning, and decision making. AI can be applied to web testing in various ways, such as generating test cases, executing test scripts, analyzing test results, and providing feedback. AI can also use natural language processing (NLP) and computer vision to understand the content and layout of web pages and interact with them as a human user would.

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Muhammad Usman Sharif

Senior QA Engineer | Fintech | Manual Testing | Automation Testing | API Testing
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AI has revolutionized web testing by enabling greater automation, efficiency, accuracy, and adaptability in testing processes, ultimately leading to higher-quality web applications delivered to end-users.

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Muneeb Mazhar

Sr.SQA Engineer|Database Testing|SOAP & Rest API| Functional/Non Functional|Web, Desktop Mobile Apps|Integration Testing|UI/UX-Cross Browser|Agile/Scrum|Jira|Selenium IDE| Load Testing - Jmeter |Trained Juniors|
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Automated Test Generation: AI algorithms can analyze application code and automatically generate test cases, reducing the manual effort required for test case creation. Intelligent Test Execution: AI-powered testing tools can prioritize and execute test cases based on factors such as code changes, risk analysis, and historical data, optimizing test coverage and resource utilization. Self-healing Tests: AI-driven testing frameworks can identify and fix minor issues in test scripts autonomously, enhancing the robustness and reliability of automated tests. Dynamic Test Data Generation: AI algorithms can generate diverse and realistic test data sets, enabling comprehensive testing scenarios.

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Awais Saleem

Sr. Software Quality Assurance Engineer
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AI has revolutionized web testing by: Expanding Test Coverage: Testing more scenarios, browsers, devices faster and more thoroughly. Smarter Automation: Creating and maintaining test cases automatically, adapting to changes. Improved Efficiency: Freeing testers from repetitive tasks for higher-level activities. Deeper Bug Detection: Finding subtler issues human testers might miss. Data-Driven Insights: Providing valuable data on performance, usability and user behavior.

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2 Benefits of AI for web testing

AI can offer many advantages for web testing, such as increasing test coverage and efficiency, reducing maintenance and costs, enhancing test quality and reliability, and improving test intelligence and insights. AI can test more scenarios, devices, browsers, and environments than manual or traditional automated testing with greater accuracy and speed. It can also adapt to changes in the web application without requiring constant updates or modifications to the test scripts, and reuse and optimize existing test cases. AI can detect and report bugs, errors, and anomalies that might be missed by human testers or conventional tools, as well as provide suggestions and recommendations for fixing them. Additionally, it can leverage data and analytics to provide meaningful and actionable information about the web application's performance, usability, and user behavior. Furthermore, AI can learn from previous test results and feedback to improve future tests.

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Abhishek Roy

"ISTQB Certified QA Lead | Advanced Lean Six Sigma & Azure Certified | AINS & Duck Creek Certified BA and Configurator| Guidewire Certified Analyst & QA Lead| Ex-Accenture & Duck Creek | Senior Manager at PwC"
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The benefits of using AI in web testing efficiency by automating repetitive tasks, allowing for faster testing cycles and reducing the time and effort required for manual testing.AI algorithms can analyze large amounts of data quickly and accurately.AI testing tools can scale to handle testing across various platforms, devices, and browsers, ensuring comprehensive test coverage and improved reliability.AI can generate and execute test cases that cover a broader range of scenarios, increasing test coverage and reducing the likelihood of missing critical issues.AI can analyze historical testing data to identify patterns, trends.AI-powered tools can efficiently perform regression testing by automatically re-running test cases.

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Paulo Oliveira

Especialista em Qualidade de Software há 17+ anos | Mentor | Professor | Escritor | LinkedIn Top Quality Assurance Voice | Expert QA Academy | Líder da Comunidade The Test Tribe Porto | Membro do PSTQB | Entusiasta de IA
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There are some companies already working with Autonomous Bots for web testing, that is a bot that scan your site, create a tree map, and exploring you app, create test cases in order to test it autonomously. This seems to be promising, this is already working and near to maturity in the following 3 years. AI can assist us in this field, and we need to be aware on how to work with this.

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3 Challenges of AI for web testing

AI can present some challenges for web testing, such as necessitating the use of new skills and tools, and a lack of transparency and explainability. Additionally, AI-based tools and systems may raise ethical and legal issues concerning the ownership, privacy, security, and accountability of data and information. Web testers must learn how to integrate AI-based tools into their workflows, as well as how to interpret and validate the outputs of these tests. Furthermore, complex algorithms or models can produce results that are not clear or understandable to humans, and may have biases or limitations that affect accuracy or reliability.

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Eran Kinsbruner

Lightrun’s Global Head of Product Marketing and Brand Strategy ?? Best-Selling Author ?? FinOps Certified Practitioner ?? Keynote Speaker?? Advisory Board??7 x Top LinkedIn Voice ??Marquis Who's Who Top Executive Listee
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The biggest challenge is test reliability, flaky tests, test maintenance over time and plugin the test cases into existing automated pipeline processes. AI web testing tools are advancing but still not as reliable and integrated well into non AI solutions

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4 Examples of AI for web testing

There are numerous AI-based tools and platforms available or in development for web testing, such as Testim, which uses machine learning to create, execute, and maintain automated tests. Applitools utilizes computer vision and visual AI to perform visual testing and validation of web applications by comparing screenshots. ReTest uses AI and genetic algorithms to generate test cases and test data based on user specifications. Mabl is a platform that uses ML and NLP to create, run, and analyze automated tests based on natural language inputs and feedback.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Bathin Vattivella

Seasoned Quality Engineering Leader | Agile Program Manager | PRINCE 2 | CSM | SAFe | RTE | LSSGB | QA COE | AI Enabler | Delivery Excellence | KAIZEN Manager | Ex-LTI | Ex-NCR | Ex-CA |
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The benefits of AI in web testing are as follows. We can derive the proper acceptance criteria for any story. Derive the test coverage for the scenario's Automation code review process. Optimizing the code with Gemini or chat GPT Preparing the test scenario's/test cases for web testing Test data creation for web testing Test estimates for web testing Automatic regression coverage based on the machine learning models Defect detection and fixing the issues by self healing tools.

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Amit Kumar Singh

Senior software QA Engineer at Maersk, Java/Python/Javascript|Web|Mobile|Desktop Application Automation | BDD | API | Microservices | CI/CD | Quality Assurance
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Future Directions and Integration: AI-driven Test Generation:- Autonomous Testing Agents Deep Learning for Test Automation Integration with DevOps Practices Ethical and Regulatory Considerations

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How has AI changed web testing?

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1 AI and web testing

2 Benefits of AI for web testing

3 Challenges of AI for web testing

4 Examples of AI for web testing

5 Here’s what else to consider

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How has AI changed web testing?

1

2

3

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5

1 AI and web testing

2 Benefits of AI for web testing

3 Challenges of AI for web testing

4 Examples of AI for web testing

5 Here’s what else to consider

Quality Assurance

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