Which feature location technique is better?

Emily Hill, Alberto Bacchelli, Dave Binkley, Bogdan Dit, Dawn Lawrie, Rocco Oliveto

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

6 Citations (Scopus)
1 Downloads (Pure)


Feature location is a fundamental step in software evolution tasks such as debugging, understanding, and reuse. Numerous automated and semi-automated feature location techniques (FLTs) have been proposed, but the question remains: How do we objectively determine which FLT is most effective? Existing evaluations frequently use bug fix data, which includes the location of the fix, but not what other code needs to be understood to make the fix. Existing evaluation measures such as precision, recall, effectiveness, mean average precision (MAP), and mean reciprocal rank (MRR) will not differentiate between a FLT that ranks higher these related elements over completely irrelevant ones. We propose an alternative measure of relevance based on the likelihood of a developer finding the bug fix locations from a ranked list of results. Our initial evaluation shows that by modeling user behavior, our proposed evaluation methodology can compare and evaluate FLTs fairly.

Original languageEnglish
Title of host publicationProceedings of the 2013 IEEE International Conference on Software Maintenance
Number of pages4
Publication statusPublished - 2013
Event29th IEEE International Conference on Software Maintenance, ICSM 2013 - Eindhoven, Netherlands
Duration: 22 Sep 201328 Sep 2013


Conference29th IEEE International Conference on Software Maintenance, ICSM 2013


  • Concern location
  • Empirical studies
  • Feature location
  • Relevance measures

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