  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.
