Improving automated documentation to code traceability by combining retrieval techniques

  • Authors:

  • Xiaofan Chen;John Grundy

  • Affiliations:

  • Department of Computer Science, University of Auckland, New Zealand;Centre for Computing & Engineering Software Systems, Swinburne University of Technology, Melbourne, Australia

  • Venue:
  • ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
  • Year:
  • 2011

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Abstract

Documentation written in natural language and source code are two of the major artifacts of a software system. Tracking a variety of traceability links between software documentation and source code assists software developers in comprehension, efficient development, and effective management of a system. Automated traceability systems to date have been faced with a major open research challenge: how to extract these links with both high precision and high recall. In this paper we introduce an approach that combines three supporting techniques, Regular Expression, Key Phrases, and Clustering, with a Vector Space Model (VSM) to improve the performance of automated traceability between documents and source code. This combination approach takes advantage of strengths of the three techniques to ameliorate limitations of VSM. Four case studies have been used to evaluate our combined technique approach. Experimental results indicate that our approach improves the performance of VSM, increases the precision of retrieved links, and recovers more true links than VSM alone.