Best practices for evaluating IRFL approaches

Information retrieval fault localization (IRFL) is a popular research field and many IRFL approaches have been proposed recently. Unfortunately, the evaluation of some of these IRFL approaches is often too simplistic, which can cause an overestimation of performance of these approaches. In this paper, we discuss evaluation pitfalls and problems. Furthermore, we propose best practices to avoid them. In detail, we discuss evaluation strategies such as parameter tuning and temporal dependencies in the data, dataset issues, metrics, statistical significance testing, and the unavailability of supplemental material. To support our claim of the poor status quo of current evaluation practices in some research papers, we have performed a literature survey on 135 papers. We hope that this paper will help researchers to avoid the described pitfalls in their evaluation of IRFL approaches. • Discussion of common pitfalls in the evaluation of IRFL approaches. • Seven best practices that help to avoid these pitfalls. • Investigation of 135 IRFL papers w.r.t. these pitfalls and best practices.