A Fuzzy Logic Expert System to Predict Module Fault Proneness using Unlabeled Data

چکیده مقاله :
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 whitegoods
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, develop

توضیحات تکمیلی :

مقاله ISI انگلیسی اصلی
سال انتشار:2018
فایل ISI انگلیسی اصلی ، با فرمت Pdf
تعداد صفحات فایل ISI انگلیسی اصلی: 32 صفحه


نویسندگان/توضیحات : مقاله ISI سال انتشار2018 \ Golnoush Abaei, Ali Selamat, Jehad Al Dallal
تاریخ ارسال : 1397/10/22
تعداد بازدید : 298
کلمات کلیدی : Fuzzy logic system, Genetic algorithm, Data-base, Rule-base, Threshold.
تعداد صفحات : 32
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