A Code-Centric Cluster-Based Approach for Searching Online Support Forums for Programmers

Online forums provide peer-to-peer technical support for many user populations, including programmers struggling to master a new language. Programmers can help one another by uploading code samples to such a forum. Unfortunately, finding relevant code samples can prove difficult using existing search engines for large, diverse forums. Therefore, we have prototyped a new kind of code search engine for online forums that draws upon unsupervised machine learning in two ways. First, it displays code samples in visual groupings based on the mutual similarity of code samples. Second, it uses the assignment of code samples to clusters to achieve a form of query expansion, thereby identifying additional search results as potentially useful. We evaluated the system by running it on the forum for the LabVIEW programming language. A textual analysis of posts showed that the unsupervised machine learning algorithm successfully tended to assign code samples to clusters based on topical similarity. An empirical user evaluation confirmed that the new search engine improved on the forum's existing search engine by providing results for more queries, by generating more results per query, and by providing more relevant search results.