Software defect prediction based on fuzzy clustering weighted class association rule mining

Software defect prediction (SDP) is an effective means to assist software testing for resource allocation. Especially, it is recommended that association classification rule for SDP considers both comprehensibility and performance. However, due to class imbalance of software defect data, there is a preference to discover more non-defective class rules than defective ones in rule generation. Classical associative classification rule algorithms usually are assumed that metrics are of equal importance, which may lead to the elimination of useful rules. On the other hand, they adopt discretization methods to transform the continuous metric data into several disjoint hard intervals, ignoring the variation of interval boundary values leading to boundary sharpening. This promotes the emergence of fuzzy weighted associative classification. However, customized membership functions based on domain knowledge often fail to achieve self-adjustment of different data intervals. Therefore, we propose an innovative SDP model based on fuzzy clustering weighted class association rule mining (FCWCAR), which is considered to address the class imbalance, feature importance, and boundary sharpening problems simultaneously. The fuzzy clustering approach based on objective function can be employed to achieve smooth transition between fuzzy intervals, the multi-fuzzy weighted supports-based framework can be implemented to solve the class imbalance, and the Pearson correlation-based approach can be utilized to assign different weights to metrics. In addition, the rule ranking, pruning and prediction stages are optimized according to fuzzy weighted support. We conduct experiments on 27 open-source datasets and results show the validity and feasibility of FCWCAR in terms of Balance, (Formula presented), MCC, and (Formula presented) .