A fuzzy logic expert system to predict module fault proneness using unlabeled data

被引:10
作者
Abaei, Golnoush [1 ,2 ]
Selamat, Ali [1 ,2 ,3 ,4 ]
Al Dallal, Jehad [5 ]
机构
[1] Univ Teknol Malaysia, Fac Comp, Software Engn Res Grp SERG, Dept Software Engn, Utm Johor Bahru 81310, Johor, Malaysia
[2] Univ Teknol Malaysia, Fac Engn, UTM&UTM & Media & Games Ctr Excellence MagicX, Sch Comp, Johor Baharu, Malaysia
[3] Univ Teknol Malaysia UTM, Malaysia Japan Int Inst Technol MJIIT, Johor Baharu, Malaysia
[4] Univ Hradec Kralove, Fac Informat & Management, Ctr Basic & Appl Res, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
[5] Kuwait Univ, Dept Informat Sci, POB 5969, Safat 13060, Kuwait
关键词
Fuzzy logic system; Genetic algorithm; Data-base; Rule-base; Threshold; ORIENTED CLASS COHESION; LINGUISTIC-SYNTHESIS;
D O I
10.1016/j.jksuci.2018.08.003
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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, 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. (C) 2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.
引用
收藏
页码:684 / 699
页数:16
相关论文
共 53 条
[1]   Increasing the Accuracy of Software Fault Prediction using Majority Ranking Fuzzy Clustering [J].
Abaei, Golnoush ;
Selamat, Ali .
INTERNATIONAL JOURNAL OF SOFTWARE INNOVATION, 2014, 2 (04) :60-71
[2]  
Abaei Golnoush., 2014, Vietnam Journal of Computer Science, V1, P79
[3]  
Adams ES, 1999, VANDERBILT LAW REV, V52, P1243
[4]   Accounting for data encapsulation in the measurement of object-oriented class cohesion [J].
Al Dallal, Jehad .
JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS, 2015, 27 (05) :373-400
[5]   Incorporating transitive relations in low-level design-based class cohesion measurement [J].
Al Dallal, Jehad .
SOFTWARE-PRACTICE & EXPERIENCE, 2013, 43 (06) :685-704
[6]   A Precise Method-Method Interaction-Based Cohesion Metric for Object-Oriented Classes [J].
Al Dallal, Jehad ;
Briand, Lionel C. .
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY, 2012, 21 (02)
[7]   The impact of accounting for special methods in the measurement of object-oriented class cohesion on refactoring and fault prediction activities [J].
Al Dallal, Jehad .
JOURNAL OF SYSTEMS AND SOFTWARE, 2012, 85 (05) :1042-1057
[8]   Fault prediction and the discriminative powers of connectivity-based object-oriented class cohesion metrics [J].
Al Dallal, Jehad .
INFORMATION AND SOFTWARE TECHNOLOGY, 2012, 54 (04) :396-416
[9]  
[Anonymous], 2016, Introduction to Time Series and Forecasting, DOI DOI 10.1007/978-3-319-29854-2
[10]  
Bilgiç T, 2000, HDB FUZZ SET SER, V7, P195