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

TL;DR

This paper reviews and empirically evaluates IR-based trace recovery methods in software engineering, emphasizing the need for industrial case studies and analyzing the impact of dataset quality on trace accuracy.

Contribution

It provides a systematic review of previous evaluations, conducts new experiments with industrial artifacts, and proposes a taxonomy for evaluating IR-based trace recovery methods.

Findings

Most evaluations are technology-oriented with small datasets.

Student artifacts are considered only partly representative of industrial data.

Dataset quality significantly affects IR trace recovery accuracy.

Abstract

Successful development of software systems involves efficient navigation among software artifacts. One state-of-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 work contains a Systematic Literature Review (SLR) of previous evaluations of IR-based trace recovery. We show that a majority of previous evaluations 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 a majority consider student artifacts to be only partly representative to industrial counterparts. Our findings call for additional case studies to evaluate IR-based trace recovery within the full complexity of an industrial setting. Also, this thesis contributes to the body of empirical evidence of IR-based trace recovery in two experiments with industrial software artifacts. The technology-oriented experiment highlights the clear dependence between datasets and the accuracy of IR-based trace recovery, in line with findings from the SLR. The human-oriented experiment investigates how different quality levels of tool output affect the tracing accuracy of engineers. 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.