Several techniques have been proposed to predict the fault proneness of software modules in the absence of fault data. However, the application of these techniques requires an expert assistant and is based on fixed thresholds and rules, which potentially prevents obtaining optimal prediction results. In this study, the development of a fuzzy logic expert system for predicting the fault proneness of software modules is demonstrated in the absence of fault data. The problem of strong dependability with the prediction model for expert assistance as well as deciding on the module fault proneness based on fixed thresholds and fixed rules have been solved in this study. In fact, involvement of experts is more relaxed or provides more support now. Two methods have been proposed and implemented using the fuzzy logic system. In the first method, the Takagi and Sugeno-based fuzzy logic system is developed manually. In the second method, the rule-base and data-base of the fuzzy logic system are adjusted using a genetic algorithm. The second method can determine the optimal values of the thresholds while recommending the most appropriate rules to guide the testing of activities by prioritizing the module's defects to improve the quality of software testing with a limited budget and limited time. Two datasets from NASA and the Turkish white-goods manufacturer that develops embedded controller software are used for evaluation. The results based on the second method show improvement in the false negative rate, f-measure, and overall error rate. To obtain optimal prediction results, developers and practitioners are recommended to apply the proposed fuzzy logic expert system for predicting the fault proneness of software modules in the absence of fault data.
Abaei, G., Selamat, A., & Al Dallal, J. (2018). A fuzzy logic expert system to predict module fault proneness using unlabeled data. Journal of King Saud University - Computer and Information Sciences. King Saud bin Abdulaziz University. https://doi.org/10.1016/j.jksuci.2018.08.003