A Literature Review of Automatic Traceability Links Recovery for Software Change Impact Analysis

© 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM. In large-scale software development projects, change impact analysis (CIA) plays an important role in controlling software designevolution. Identifying and accessing the effects of software changesusing traceability links between various software artifacts is a common practice during the software development cycle. Recently,research in automated traceability-link recovery has received broadattention in the software maintenance community to reduce themanual maintenance cost of trace links by developers. In this study,we conducted a systematic literature review related to automatictraceability link recovery approaches with a focus on CIA. We identified 33 relevant studies and investigated the following aspects ofCIA: traceability approaches, CIA sets, degrees of evaluation, tracedirection and methods for recovering traceability link between artifacts of different types. Our review indicated that few traceabilitystudies focused on designing and testing impact analysis sets, presumably due to the scarcity of datasets. Based on the findings, weurge further industrial case studies. Finally, we suggest developingtraceability tools to support fully automatic traceability approaches,such as machine learning and deep learning.