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PostFinder: Mining Stack Overflow posts to support software developers
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DOI：10.1016/j.infsof.2020.106367.png)
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Abstract
En 中文
Context - During the development of complex software systems, programmers look for external resources to understand better how to use specific APIs and to get advice related to their current tasks. Stack Overflow provides developers with a broader insight into API usage as well as useful code examples. Given the circumstances, tools and techniques for mining Stack Overflow are highly desirable. Objective - In this paper, we introduce PostFinder, an approach that analyzes the project under development to extract suitable context, and allows developers to retrieve messages from Stack Overflow being relevant to the API function calls that have already been invoked. Method - PostFinder augments posts with additional data to make them more exposed to queries. On the client side, it boosts the context code with various factors to construct a query containing information needed for matching against the stored indexes. Multiple facets of the data available are used to optimize the search process, with the ultimate aim of recommending highly relevant SO posts. Results - The approach has been validated utilizing a user study involving a group of 12 developers to evaluate 500 posts for 50 contexts. Experimental results indicate the suitability of PostFinder to recommend relevant Stack Overflow posts and concurrently show that the tool outperforms a well-established baseline. Conclusions - We conclude that PostFinder can be deployed to assist developers in selecting relevant Stack Overflow posts while they are programming as well as to replace the module for searching posts in a code-to-code search engine.
Keywords:
Recommender systems
Mining Stack Overflow posts
Indexing posts
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