Improving Trace Link Recommendation by Using Non-Isotropic Distances and Combinations

TL;DR

This paper explores the use of non-isotropic distances and their combinations to improve automatic trace link recommendation in software engineering, emphasizing a geometric approach to semantic similarity.

Contribution

It introduces a novel geometric perspective on semantic similarity and evaluates non-linear measures for trace link computation across diverse datasets.

Findings

Non-isotropic distance measures can enhance trace link accuracy.

Geometric similarity approaches are promising for traceability tasks.

Findings are applicable to broader information retrieval problems.

Abstract

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.