Poster: Improving Bug Localization with Report Quality Dynamics and Query Reformulation

Recent findings from a user study suggest that IR-based bug localization techniques do not perform well if the bug report lacks rich structured information such as relevant program entity names. On the contrary, excessive structured information such as stack traces in the bug report might always not be helpful for the automated bug localization. In this paper, we conduct a large empirical study using 5,500 bug reports from eight subject systems and replicating three existing studies from the literature. Our findings (1) empirically demonstrate how quality dynamics of bug reports affect the performances of IR-based bug localization, and (2) suggest potential ways (e.g., query reformulations) to overcome such limitations.Comment: The 40th International Conference on Software Engineering (Companion volume, Poster Track) (ICSE 2018), pp. 348--349, Gothenburg, Sweden, May, 201

A Systematic Review of Automated Query Reformulations in Source Code Search

Fixing software bugs and adding new features are two of the major maintenance tasks. Software bugs and features are reported as change requests. Developers consult these requests and often choose a few keywords from them as an ad hoc query. Then they execute the query with a search engine to find the exact locations within software code that need to be changed. Unfortunately, even experienced developers often fail to choose appropriate queries, which leads to costly trials and errors during a code search. Over the years, many studies attempt to reformulate the ad hoc queries from developers to support them. In this systematic literature review, we carefully select 70 primary studies on query reformulations from 2,970 candidate studies, perform an in-depth qualitative analysis (e.g., Grounded Theory), and then answer seven research questions with major findings. First, to date, eight major methodologies (e.g., term weighting, term co-occurrence analysis, thesaurus lookup) have been adopted to reformulate queries. Second, the existing studies suffer from several major limitations (e.g., lack of generalizability, vocabulary mismatch problem, subjective bias) that might prevent their wide adoption. Finally, we discuss the best practices and future opportunities to advance the state of research in search query reformulations.Comment: 81 pages, accepted at TOSE