Automatic query performance assessment during the retrieval of software artifacts

  • Authors:

  • Sonia Haiduc;Gabriele Bavota;Rocco Oliveto;Andrea De Lucia;Andrian Marcus

  • Affiliations:

  • Wayne State University, USA;University of Salerno, Italy;University of Molise, Italy;University of Salerno, Italy;Wayne State University, USA

  • Venue:
  • Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
  • Year:
  • 2012

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Abstract

Text-based search and retrieval is used by developers in the context of many SE tasks, such as, concept location, traceability link retrieval, reuse, impact analysis, etc. Solutions for software text search range from regular expression matching to complex techniques using text retrieval. In all cases, the results of a search depend on the query formulated by the developer. A developer needs to run a query and look at the results before realizing that it needs reformulating. Our aim is to automatically assess the performance of a query before it is executed. We introduce an automatic query performance assessment approach for software artifact retrieval, which uses 21 measures from the field of text retrieval. We evaluate the approach in the context of concept location in source code. The evaluation shows that our approach is able to predict the performance of queries with 79% accuracy, using very little training data.