Do better IR tools improve the accuracy of engineers' traceability recovery?

  • Authors:
  • Markus Borg;Dietmar Pfahl

  • Affiliations:
  • Lund University, Lund, Sweden;Lund University, Lund, Sweden

  • Venue:
  • Proceedings of the International Workshop on Machine Learning Technologies in Software Engineering
  • Year:
  • 2011

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Abstract

Large-scale software development generates an ever-growing amount of information. Multiple research groups have proposed using approaches from the domain of information retrieval (IR) to recover traceability. Several enhancement strategies have been initially explored using the laboratory model of IR evaluation for performance assessment. We conducted a pilot experiment using printed candidate lists from the tools RETRO and ReqSimile to investigate how different quality levels of tool output affect the tracing accuracy of engineers. Statistical testing of equivalence, commonly used in medicine, has been conducted to analyze the data. The low number of subjects in this pilot experiment resulted neither in statistically significant equivalence nor difference. While our results are not conclusive, there are indications that it is worthwhile to investigate further into the actual value of improving tool support for semi-automatic traceability recovery. For example, our pilot experiment showed that the effect size of using RETRO versus ReqSimile is of practical significance regarding precision and F-measure. The interpretation of the effect size regarding recall is less clear. The experiment needs to be replicated with more subjects and on varying tasks to draw firm conclusions.