Skip to Main Content
Information retrieval (IR) approaches have proven useful in recovering traceability links between free text documentation and source code. IR-based traceability recovery approaches produce ranked lists of traceability links between pieces of documentation and source code. These traceability links are then pruned using various strategies and, finally, validated by human experts. In this paper we propose two contributions to improve the precision and recall of traceability links and, thus, reduces the required human experts' manual validation effort. First, we propose a novel approach, Trustrace, inspired by Web trust models to improve the precision and recall of traceability links: Trustrace uses any traceability recovery approach to obtain a set of traceability links, which rankings are then re-evaluated using a set of other traceability recovery approaches. Second, we propose a novel traceability recovery approach, Histrace, to identify traceability links between requirements and source code through CVS/SVN change logs using a Vector Space Model (VSM). We combine a traditional recovery traceability approach with Histrace to build TrustraceVSM, Histrace in which we use Histrace as one expert adding knowledge to the traceability links extracted from CVS/SVN change logs. We apply TrustraceVSM, Histrace on two case studies to compare its traceability links with those recovered using only the VSM-based approach, in terms of precision and recall. We show that TrustraceVSM, Histrace improves with statistical significance the precision of the traceability links while also improving recall but without statistical significance.