The Critical Piece in AI-Powered Testing: A Context-Aware Approach
In my previous post, we explored the exciting possibilities of AI in software testing. From automating tedious tasks to uncovering hidden bugs, AI is set to transform the way we ensure software quality. But there's one crucial aspect often overlooked : the importance of Context.
Understanding Context: The Next Frontier
Most AI-powered testing tools focus on analysing code and specifications, which is valuable. However, truly effective testing requires a deeper understanding of the entire project context.
This includes:
By integrating this rich context, AI can become a true Co-worker, not just a tool. It can generate more relevant test cases, adapt to your specific needs, and provide insights you wouldn't uncover otherwise.
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The Benefits of a Context-Aware Approach
The Road Ahead
I believe that context-aware AI is the key to unlocking the full potential of software testing. By leveraging the vast amount of knowledge available within your projects, we can build testing solutions that are more accurate, efficient, and adaptable.
This approach is not just theoretical. We're actively developing a platform that embodies these principles, and I will be sharing more details in the coming days.?
Meanwhile, I would love to hear from you: What are the biggest challenges you face in API testing due to lack of context? Share your thoughts in the comments below!
Knack to solve problems.
5 个月API usage Context is different for each organization that uses an API of the application. This usage context is the combination / sequence of parameter with which an API get called for its desired purpose followed by other API calls in certain sequence to complete a task. The lack of precise documentation with example about parameter sequence adds to the challenges for automation. Deprecation of parameters and addition of new ones with newer release breaks the backward compatibility. AI can help to identify some of the problems in above areas to improve testing efficiency. AI model should be trained on past defects which resulted in code change or workaround suggestion to the user of the API to begin with followed by evaluating documentation change request for every API.
Software Engineer at IBM
5 个月Good post!? It's important to recognize that context in software systems is a moving target and it keeps changing/evolving. Integration provides better context and awareness of it and over time it needs to get more and more clear. A good AI system not only 'builds' awareness, it also 'adapts' to it as it moves. A context can be logical or physical in nature. A superior AI system is the one that is 'continuously' trained on relevant data over time. RAG is at the heart of such system.