https://doi.org/10.1016/j.infsof.2020.106277

Uddin, G., Khomh, F., & Roy, C. K. (2020). Mining API usage scenarios from stack overflow. Information and Software Technology, 122, 106277. https://doi.org/10.1016/j.infsof.2020.106277

Context
APIs play a central role in software development. The seminal research of Carroll et al. [15] on minimal manual and subsequent studies by Shull et al. [79] showed that developers prefer task-based API documentation instead of traditional hierarchical official documentation (e.g., Javadoc). The Q&A format in Stack Overflow offers developers an interface to ask and answer questions related to their development tasks.

Objective
With a view to produce API documentation, we study automated techniques to mine API usage scenarios from Stack Overflow.

Method
We propose a framework to mine API usage scenarios from Stack Overflow. Each task consists of a code example, the task description, and the reactions of developers towards the code example. First, we present an algorithm to automatically link a code example in a forum post to an API mentioned in the textual contents of the forum post. Second, we generate a natural language description of the task by summarizing the discussions around the code example. Third, we automatically associate developers reactions (i.e., positive and negative opinions) towards the code example to offer information about code quality.

Results
We evaluate the algorithms using three benchmarks. We compared the algorithms against seven baselines. Our algorithms outperformed each baseline. We developed an online tool by automatically mining API usage scenarios from Stack Overflow. A user study of 31 software developers shows that the participants preferred the mined usage scenarios in Opiner over API official documentation. The tool is available online at: http://opiner.polymtl.ca/ .

Conclusion
With a view to produce API documentation, we propose a framework to automatically mine API usage scenarios from Stack Overflow, supported by three novel algorithms. We evaluated the algorithms against a total of eight state of the art baselines. We implement and deploy the framework in our proof-of-concept online tool, Opiner.

https://doi.org/10.1016/j.infsof.2020.106277