"AI BASED SOFTWARE TESTING" --- Chapter 7.pptx
- 1. SOFTWARE CONSTRUCTION AND DEVELOPMENT BY SANA RIZWAN CHAPTER 7 INFUSING AI IN SOFTWARE TESTING
- 2. CONTENTS AI in Software Testing & Quality Assurance: • Enhances Software Testing with AI • AI-Driven Testing Tools • Automated Test Generation • Key Challenges • Benefits of AI in Software Testing 2
- 3. HOW TRADITIONAL SOFTWARE TESTING IS DIFFERENT FROM AI INFUSED SOFTWARE TESTING 3 • The infusion of AI into software testing is not just an incremental improvement but a big leap forward. • Traditional testing methods have long been dependent on manual effort and scripted automation. • They are increasingly being augmented or replaced by AI- driven techniques. • AI tools can analyze vast amounts of data, analyze or learn from patterns, and adapt to changes in real-time. • Its ability to predict defects and generate intelligent test cases makes AI uniquely suited to address the challenges of the next generation of software development.
- 4. 4 MANUAL SOFTWARE TESTING • In the early days of software and application development, testing was a manual, labor- intensive process. • Testers would execute test cases by hand, compare the results to expected outcomes, and manually log any defects they found. • This approach was sufficient for simple or small applications but was not scalable and quickly became unsustainable as the software grew in complexity and scale. • As part of manual testing process, testers would follow predefined test cases, which were often documented in Word or Excel documents. • Each test case would describe a series of steps or tasks to execute and the expected results. • If the actual result differed from the expected result, the tester or a quality analyst would report a defect using the defect tracking tool. • The primary limitation of manual testing is its scalability. • As the number of test cases grows, so does the time required to execute them. • Manual testing is also prone to human error, as testers may miss steps or misinterpret results.
- 5. 5 AUTOMATION ERA • To address the limitations of manual testing, automation tools were developed. • These tools allowed testers to write scripts that could execute test cases automatically. • Automation significantly reduced the time required to run tests and increased the reliability of test execution. • Early automation tools used scripting languages like Selenium, which allowed testers to write scripts that could interact with web browsers and simulate user actions. • These scripts could be executed repeatedly, ensuring that the application behaved as expected after each code change. • While automation reduced the manual effort required for testing, it introduced new challenges. • Writing and maintaining test scripts is a time-consuming process, and scripts can be fragile—breaking when the application’s user interface or functionality changes.
- 6. HOW AI ENHANCES SOFTWARE TESTING 6
- 7. 7 ML ALGORITHMS • ML algorithms can analyze historical test data to identify patterns, predict potential defects, and optimize test coverage. • ML models can also continuously learn and improve as they process more data, making them increasingly effective over time. • For example, suppose a retail application has a history of defects in its payment processing module. • An ML model trained on past defect reports might predict that new code changes in this module have a high likelihood of causing issues. • This prediction would prompt targeted testing, focusing efforts on areas most likely to contain defects.
- 8. 8 NATURAL LANGUAGE PROCESSING (NLP) • NLP enables AI systems to understand and process human language, which can be incredibly useful in generating test cases from textual requirements or user stories. • For example, imagine a scenario where a project manager writes a user story describing a new feature: "As a user, I want to receive an email confirmation after completing a purchase." • An NLP-powered AI tool can interpret this user story and automatically generate relevant test cases to ensure that the email confirmation functionality works as expected.
- 9. 9 NEURAL NETWORKS • Neural networks, particularly deep learning models, are adept at recognizing complex patterns in data. • They can be used in visual testing to detect UI anomalies that are difficult for traditional automation to catch. • A neural network might analyze screenshots of a mobile application’s user interface across different devices and operating systems, identifying subtle visual inconsistencies that could impact the user experience.
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- 12. 12 ADVANTAGES
- 13. 13 BENEFIT S
- 14. 14 CHALLENGES Some of the challenges in implementing AI into the software testing process include data quality and data availability. Gathering and maintaining the data needed for AI training can be challenging, particularly in test and production environments where data privacy and security are critical concerns. • Compatibility issues often arise when integrating existing systems and workflows, as many of the tools and platforms are not compatible. • The technical complexity of integrating AI into existing workflows can be a significant barrier, requiring specialized knowledge and expertise. • AI models can inherit biases from the data they are trained on, leading to biased testing outcomes. For example, if a model is trained on data that underrepresents certain user demographics, it may not perform well for those users. • Talent upskilling and learning curve issues often exist. Implementing and managing AI-based testing systems effectively requires specialized skills and knowledge, which may be in short supply. • The upfront costs of AI tools, training, and integration can be substantial, particularly for small and medium- sized organizations. • Organizations must carefully consider the initial costs versus the long-term benefits of AI in testing.
- 15. 15 HOW THE V-MODEL HAS EVOLVED WITH AI TESTING
- 16. 16 HOW THE V-MODEL HAS EVOLVED WITH AI TESTING
- 17. 17 HOW THE V-MODEL HAS EVOLVED WITH AI TESTING
- 18. 18 AI-DRIVEN TESTING TOOLS • Appvance AI ( https://www.youtube.com/watch?v=OAkMD4-FLc4) Appvance’s AI-driven testing platform offers features such as automated test case generation, regression testing, and performance testing. The platform uses ML algorithms to optimize test coverage and reduce testing time. This tool is best suited for organizations looking for a comprehensive testing solution that combines AI with traditional test automation techniques. It includes the AIQ Platform, which offers a wide range of features, including automated test case generation, regression testing, and performance testing. Appvance enables supervised AI-driven exploratory script generation. It increases or completes test coverage where bugs are found, heals the bugs, and helps solve complex testing needs.
- 20. 20 AI-DRIVEN TESTING TOOLS • Testim ( https://www.testim.io/resources/five-keys-to-growin g-agile-testing-projects-efficiently/ ) Testim is an AI-driven testing tool that focuses on mobile and web applications. It uses ML to automatically generate test cases and execute them across different devices and browsers. Testim Copilot provides the following functionalities: Automatically produces custom test steps from a text description using generative AI. Provides clear explanations of JavaScript code, enhancing understanding and facilitating documentation and reuse. Accelerates debugging by automatically analyzing code and suggesting corrections. Testim can generate a test to check whether a web page contains a header image followed by a login prompt automatically as part of the execution. https://www.testim.io/test-automation-tool/
- 22. 22 FOUR BASIC STEPS FOR THE PROCESS OF IMPLEMENTING VISUAL UI TESTING
- 23. 23 TOSCA – AI- INFUSED TESTING PLATFORM Tosca is ideal for large-scale and complex testing needs 1. AI-Powered Capabilities: • Tosca Copilot: A newly introduced generative AI assistant that optimizes testing processes by helping with test portfolio management, explaining complex test cases, and offering actionable insights on execution failures. It uses advanced Large Language Models (LLMs) for these tasks, allowing non-technical users to manage test automation efficiently. • Vision AI: Employs patented neural network technology to ensure resilient UI automation and includes self-healing features to adapt tests dynamically 2. Test Automation Features: • Fully codeless, model-based testing for UI, API, and data layers across 160+ technologies, including enterprise, mobile, and custom applications • Features like risk-based testing, test data management, and service virtualization enable faster and more robust testing. These capabilities minimize dependencies and bottlenecks during the testing lifecycle 3. Execution & Reporting: • Supports distributed testing by running multiple tests in parallel across various infrastructures for scalability • Includes detailed testing reports and analytics for informed decision-making and faster issue resolution 4. Integration & Flexibility: • Seamlessly integrates with CI/CD pipelines and various enterprise applications. • Offers cross-platform testing for web, mobile, and desktop environments. 5. Cost & Productivity Gains: • Automating repetitive tasks and enabling faster onboarding with tools like the AI-powered Tosca Copilot reduces testing costs and cycle times
- 24. 24 FUNCTIONIZE –AI INFUSED TESTING TOOL
- 25. 25 MICROSOFT’S AI TESTING STRATEGIES
- 27. 27 GOOGLE’S AI-DRIVEN TESTING FRAMEWORK
- 28. 28 KEY FUNCTIONALITIES REQUIRED FOR AI-BASED TESTING
- 29. 29 KEY FUNCTIONALITIES REQUIRED FOR AI-BASED TESTING
- 30. THANK YOU Sana Rizwan Assistant Professor Department of Computer Science, COMSATS University Islamabad, Lahore Campus Email: sana@cuilahore.edu.pk 30