DCTracVis: a system retrieving and visualizing traceability links between source code and documentation

Xiaofan Chen, John Hosking, John Grundy, Robert Amor

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

It is well recognized that traceability links between software artifacts provide crucial support in comprehension, efficient development, and effective management of a software system. However, automated traceability systems to date have been faced with two major open research challenges: how to extract traceability links with both high precision and high recall, and how to efficiently visualize links for complex systems because of scalability and visual clutter issues. To overcome the two challenges, we designed and developed a traceability system, DCTracVis. This system employs an approach that combines three supporting techniques, regular expressions, key phrases, and clustering, with information retrieval (IR) models to improve the performance of automated traceability recovery between documents and source code. This combination approach takes advantage of the strengths of the three techniques to ameliorate limitations of IR models. Our experimental results show that our approach improves the performance of IR models, increases the precision of retrieved links, and recovers more correct links than IR alone. After having retrieved high-quality traceability links, DCTracVis then utilizes a new approach that combines treemap and hierarchical tree techniques to reduce visual clutter and to allow the visualization of the global structure of traces and a detailed overview of each trace, while still being highly scalable and interactive. Usability evaluation results show that our approach can effectively and efficiently help software developers comprehend, browse, and maintain large numbers of links.

Original languageEnglish
Pages (from-to)703-741
Number of pages39
JournalAutomated Software Engineering
Volume25
Issue number4
DOIs
Publication statusPublished - Dec 2018

Keywords

  • Software traceability
  • Traceability recovery
  • Traceability visualization

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