How do you use AI to improve software engineering testing and debugging?

Cognitive testing is a huge upside to integrating AI into the Software Development life cycle. Predicting user behaviour from patterns and developing persona based test scenarios enrich the cycle overall with benefits of user delight, faster time to market and interestingly deepening domain understanding for the engineers.

Symbolic testing, model-based testing, and NLP create a triangle where each component assists and enhances the others. The structured model from model-based testing provides a foundation for both symbolic testing and NLP. Symbolic testing explores the abstract model, while NLP adds a human-readable layer to the model and helps generate test cases that are more aligned with real-world usage. Each component leverages the strengths of the others. The triangle of mutual assistance.

A gera??o de testes sempre foi uma tarefa desafiadora, exigindo um esfor?o considerável, conhecimento de domínio e criatividade por parte dos engenheiros de software. A introdu??o da inteligência artificial (IA) neste contexto é um divisor de águas, oferecendo oportunidades significativas para aprimorar e acelerar o processo de gera??o de casos de teste. A técnica de execu??o simbólica, por exemplo, destaca-se como uma abordagem inovadora. Ao analisar o código e gerar entradas de teste que exploram diferentes caminhos e ramifica??es, a IA pode automatizar a cria??o de casos de teste que cobrem uma variedade mais ampla de cenários.

You can use generative AI for behavioral testing. You should be careful with your sensitive data when you are describing the feature to AI. For unit testing, you can use AI for it's compute power and it could create extended version what you have. Risk of sharing sensitive data is less than the behavioral testing. In Agile world, your product needs to run properly not perfectly. Each version will be better just try to do better. AI can help you with it's speed and coverage.

For automation, using AI can help a lot to generate specific functions or tasks and thus have a more organized, clean code and AI always offers several options, where oneself can now mix all the ideas for the generation of automated tests.

AI enhances software engineering testing and debugging through automated testing.Dynamic code analysis involves AI monitoring code execution in real-time to detect anomalies and potential runtime errors. Predictive analysis allows AI to predict bug-prone areas in code repositories, aiding in prioritizing testing efforts. AI assists in debugging by analyzing error logs, stack traces, and providing suggestions or pinpointing root causes. Natural Language Processing (NLP) helps analyze and understand natural language documentation for more effective maintenance. A User behavior analysis enables the creation of realistic test cases by simulating real-world scenarios based on user interactions.

I have been using AI tools (adaptive testing) to generate test data for my manual test execution. Nowadays, parallel execution of automation suite is a necessity and all the modern automation tools support these. Running the automation suite is just the starting point. Analysis in case of failures and presenting consilidated reports to the stakeholders is paramount.

I have been using AI tools (which uses NLP) to understand the errors found in logs while manual testing as well as automation to understand the scenarios under which this can occur. I than analyse those scenarios in context of my project to narrow down the list or arrive at the exact root cause.

Self-healing programs are a winning aspiration in this space. Teams that have developed a healthy discipline over several years around test driven development, automation and alerting , continuous integration and deployment are intrigued by how this discipline can converge to automated program repair and reinforcement learning. The risks are being cautiously weighed against the benefits that are known at the moment and the benefits that are yet to be unearthed.

Once root cause if failure or error is known or the possible reasons has been narrowed down, it becomes easier to tackle the issue. If the issue is intermittent, we can use performance testing tools like K6, JMeter of any other to understand the circumstances under which issue arise. AI tools (automated program repair and NLP) could be used to modify K6 scripts to reproduce issue effectively thus enabling us to resolve it.