Adaptive User Feedback for IR-Based Traceability Recovery

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Trace ability recovery allows software engineers to understand the interconnections among software artefacts and, thus, it provides an important support to software maintenance activities. In the last decade, Information Retrieval (IR) has been widely adopted as core technology of semi-automatic tools to extract trace ability links between artefacts according to their textual information. However, a widely known problem of IR-based methods is that some artefacts may share more words with non-related artefacts than with related ones. To overcome this problem, enhancing strategies have been proposed in literature. One of these strategies is relevance feedback, which allows to modify the textual similarity according to information about links classified by the users. Even though this technique is widely used for natural language documents, previous work has demonstrated that relevance feedback is not always useful for software artefacts. In this paper, we propose an adaptive version of relevance feedback that, unlike the standard version, considers the characteristics of both (i) the software artefacts and (ii) the previously classified links for deciding whether and how to apply the feedback. An empirical evaluation conducted on three systems suggests that the adaptive relevance feedback outperforms both a pure IR-based method and the standard feedback.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE/ACM 8th International Symposium on Software and Systems Traceability, SST 2015
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages7
ISBN (Electronic)9780769555935
Publication statusPublished - 5 Aug 2015
Event8th IEEE/ACM International Symposium on Software and Systems Traceability, SST 2015 - Florence, Italy
Duration: 17 May 2015 → …


Conference8th IEEE/ACM International Symposium on Software and Systems Traceability, SST 2015
Period17/05/15 → …


  • Empirical Software Engineering
  • Information Retrieval
  • Software Traceability
  • User Feedback Analysis


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