Mining requirements links

[Context & motivation] Obtaining traceability among requirements and between requirements and other artifacts is an extremely important activity in practice, an interesting area for theoretical study, and a major hurdle in common industrial experience. Substantial effort is spent on establishing and updating such links in any large project - even more so when requirements refer to a product family. [Question/problem] While most research is concerned with ways to reduce the effort needed to establish and maintain traceability links, a different question can also be asked: how is it possible to harness the vast amount of implicit (and tacit) knowledge embedded in already-established links? Is there something to be learned about a specific problem or domain, or about the humans who establish traces, by studying such traces? [Principal ideas/results] In this paper, we present preliminary results from a study applying different machine learning techniques to an industrial case study, and test to what degree common hypothesis hold in our case. [Contribution] Reshaping traceability data into knowledge can contribute to more effective automatic tools to suggest candidates for linking, to inform improvements in writing style, and at the same time provide some insight into both the domain of interest and the actual implementation techniques. © 2011 Springer-Verlag Berlin Heidelberg