Advancing Trace Recovery Evaluation - Applied Information Retrieval in a Software Engineering Context

Successful development of software systems involves the efficient navigation of software artifacts. However, as artifacts are continuously produced and modified, engineers are typically plagued by challenging information landscapes. One stateof-practice approach to structure information is to establish trace links between artifacts; a practice that is also enforced by several development standards. Unfortunately, manually maintaining trace links in an evolving system is a tedious task. To tackle this issue, several researchers have proposed treating the capture and recovery of trace links as an Information Retrieval (IR) problem. The goal of this thesis is to contribute to the evaluation of IR-based trace recovery, both by presenting new empirical results and by suggesting how to increase the strength of evidence in future evaluative studies. This thesis is based on empirical software engineering research. In a Systematic Literature Review (SLR) we show that a majority of previous evaluations of IR-based trace recovery have been technology-oriented, conducted in “the cave of IR evaluation”, using small datasets as experimental input. Also, software artifacts originating from student projects have frequently been used in evaluations. We conducted a survey among traceability researchers and found that while a majority consider student artifacts to be only partly representative of industrial counterparts, such artifacts were typically not validated for industrial representativeness. Our findings call for additional case studies to evaluate IR-based trace recovery within the full complexity of an industrial setting. Thus, we outline future research on IR-based trace recovery in an industrial study on safety-critical impact analysis. Also, this thesis contributes to the body of empirical evidence of IR-based trace recovery in two experiments with industrial software artifacts. The technologyoriented experiment highlights the clear dependence between datasets and the accuracy of IR-based trace recovery, in line with findings from the SLR. The humanoriented experiment investigates how different quality levels of tool output affect the tracing accuracy of engineers. While the results are not conclusive, there are indications that it is worthwhile further investigating into the actual value of improving tool support for IR-based trace recovery. Finally, we present how tools and methods are evaluated in the general field of IR research, and propose a taxonomy of evaluation contexts tailored for IR-based trace recovery in software engineering.