Paper  abstract   bibtex   

@ARTICLE{Ali13-TSE-Trustrace,
   AUTHOR       = {Nasir Ali and Yann-Ga�l Gu�h�neuc and Giuliano Antoniol},
   JOURNAL      = {Transactions on Software Engineering (TSE)},
   TITLE        = {Trustrace: Mining Software Repositories to Improve the 
      Accuracy of Requirement Traceability Links},
   YEAR         = {2013},
   MONTH        = {May},
   NOTE         = {20 pages.},
   NUMBER       = {5},
   PAGES        = {725--741},
   VOLUME       = {39},
   EDITOR       = {Tetsuo Tamai},
   KEYWORDS     = {Topic: <b>Requirements and features</b>, 
      Venue: <b>TSE</b>},
   PUBLISHER    = {IEEE CS Press},
   URL          = {http://www.ptidej.net/publications/documents/TSE13a.doc.pdf},
   ABSTRACT     = {Traceability is the only means to ensure that the source 
      code of a system is consistent with its requirements and that all and 
      only the specified requirements have been implemented by developers. 
      During software maintenance and evolution, requirement traceability 
      links become obsolete because developers do not/cannot devote effort 
      to update them. Yet, recovering these traceability links later is a 
      daunting and costly task for developers. Consequently, the literature 
      proposed methods, techniques, and tools to recover these traceability 
      links semi-automatically or automatically. Among the proposed 
      techniques, the literature showed that information retrieval (IR) 
      techniques can automatically recover traceability links between 
      free-text requirements and source code. However, IR techniques lack 
      accuracy (precision and recall). In this paper, we show that mining 
      software repositories and combining mined results with IR techniques 
      can improve the accuracy (precision and recall) of IR techniques and 
      we propose Trustrace, a trust-based traceability recovery approach. 
      We apply Trustrace on four medium-size open-source systems to compare 
      the accuracy of its traceability links with those recovered using 
      state-of-the-art IR techniques from the literature, based on the 
      Vector Space Model and Jensen--Shannon model. The results of 
      Trustrace are up to 22.7\NOmore precise and have 7.66\NObetter recall 
      values than those of the other techniques, on average. We thus show 
      that mining software repositories and combining the mined data with 
      existing results from IR techniques improves the precision and recall 
      of requirement traceability links.}
}