Improving Trace Link Recommendation by Using Non-Isotropic Distances and Combinations
This work addresses the time-consuming and error-prone creation of trace links for software developers, though it appears incremental as it builds on existing similarity measures.
The paper tackled the problem of automatically computing trace links in software development by proposing a geometric viewpoint on semantic similarity, and evaluated the approach on four open source and two industrial projects, showing potential improvements in efficiency.
The existence of trace links between artifacts of the software development life cycle can improve the efficiency of many activities during software development, maintenance and operations. Unfortunately, the creation and maintenance of trace links is time-consuming and error-prone. Research efforts have been spent to automatically compute trace links and lately gained momentum, e.g., due to the availability of powerful tools in the area of natural language processing. In this paper, we report on some observations that we made during studying non-linear similarity measures for computing trace links. We argue, that taking a geometric viewpoint on semantic similarity can be helpful for future traceability research. We evaluated our observations on a dataset of four open source projects and two industrial projects. We furthermore point out that our findings are more general and can build the basis for other information retrieval problems as well.