Exploring a Bayesian and Linear approach to requirements traceability

For large software projects it is important to have some traceability between artefacts from different phases (e.g., requirements, designs, code), and be-tween artefacts and the involved developers. This is especially critical dur-ing maintenance, when people working on the software may be different from the original developers and therefore have a harder struggle to understand the artefacts and the consequences of changes. However, if the capturing of traceability information during the project is felt as laborious to the original developers, they will often be sloppy in registering the relevant traceability links so that the information is incomplete. This makes automated tool-based collection of traceability links a tempting alternative, but this has the opposite challenge of generating too many potential trace relationships, not all of which are equally relevant. A key issue is therefore how to rank such auto-generated trace relationships. This paper presents two approaches for such a ranking: a Bayesian technique and a linear inference technique. Both techniques depend on the interaction event trails left behind by collaborating developers while working within a development tool. The advantage of our approach is that it can be used to provide traceability insights that are con-textual and would have been much more difficult to capture manually. The outcome of a preliminary study suggest the advantage of the linear approach, we also explore the challenges and potentials of the two techniques. Finally we present some key lessons learnt during this research. Key words: requirements traceability, maintenance, collaborative wor