A search-based approach for detecting circular dependency bad smell in goal-oriented models

Abstract

Goal-oriented models are gaining significant attention from researchers and practitioners in various domains, especially in software requirements engineering. Similar to other software engineering models, goal models are subject to bad practices (i.e., bad smells). Detecting and rectifying these bad smells would improve the quality of these models. In this paper, we formally define the circular dependency bad smell and then develop an approach based on the simulated annealing (SA) search-based algorithm to detect its instances. Furthermore, we propose two mechanisms (namely, pruning and pairing) to improve the effectiveness of the proposed approach. We empirically evaluate three algorithm combinations, i.e., (1) the base SA search algorithm, (2) the base SA search algorithm augmented with pruning mechanism, and (3) the base SA search algorithm augmented with pruning and pairing mechanisms, using several case studies. Results show that simulated annealing augmented with pruning and pairing is the most effective approach, while the simulated annealing augmented with pruning mechanism is more effective than the base SA search algorithm. We also found that the proposed pruning and pairing mechanisms provide a significant improvement in the detection of circular dependency bad smell, in terms of computation time and accuracy.