Application of Adaptive Neuro-Fuzzy Inference System for Predicting Software Change Proneness

被引：0

作者：

Peer, Akshit ^{[1
]}

Malhotra, Ruchika ^{[1
]}

机构：

[1] Delhi Technol Univ, Dept Comp Engn, Delhi 110042, India

来源：

2013 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI) | 2013年

关键词：

ANFIS; bagging; change proneness; logistic regression; random forest; receiver operating characteristic (ROC) curve; sensitivity; specificity; METRICS; VALIDATION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we model the relationship between object-oriented metrics and software change proneness. We use adaptive neuro-fuzzy inference system (ANFIS) to calculate the change proneness for the two commercial open source software systems. The performance of ANFIS is compared with other techniques like bagging, logistic regression and decision trees. We use the area under receiver operating characteristic (ROC) curve to determine the effectiveness of the model. The present analysis shows that of all the techniques investigated, ANFIS gives the best results for both the software systems. We also calculate the sensitivity and specificity for each technique and use it as a measure to evaluate the model effectiveness. The aim of the study is to know the change prone classes in the early phases of software development so as to plan the allocation of testing resources effectively and thus improve software maintainability.

引用

页码：2026 / 2031

页数：6

共 29 条

[21] The ability of object-oriented metrics to predict change-proneness: a meta-analysis [J].

Lu, Hongmin ;

Zhou, Yuming ;

Xu, Baowen ;

Leung, Hareton ;

Chen, Lin .

EMPIRICAL SOFTWARE ENGINEERING, 2012, 17 (03) :200-242

[22]

Malhotra R., 2012, INT J MACHINE LEARNI, V137

[23] Comparative study on classification performance between support vector machine and logistic regression [J].

Musa, Abdallah Bashir .

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2013, 4 (01) :13-24

[24]

Sharafat AR, 2007, CSMR 2007: 11TH EUROPEAN CONFERENCE ON SOFTWARE MAINTENANCE AND REENGINEERING, PROCEEDINGS, P27

[25]

Sidorov G, 2011, INT J MACH LEARN CYB, V2, P123, DOI 10.1007/s13042-011-0033-0

[26]

Singh Y., 2012, OBJECT ORIENTED SOFT

[27] Empirical validation of object-oriented metrics for predicting fault proneness models [J].

Singh, Yogesh ;

Kaur, Arvinder ;

Malhotra, Ruchika .

SOFTWARE QUALITY JOURNAL, 2010, 18 (01) :3-35

[28] An application of fuzzy clustering to software quality prediction [J].

Yuan, XH ;

Khoshgoftaar, TM ;

Allen, EB ;

Ganesan, K .

3RD IEEE SYMPOSIUM ON APPLICATION SPECIFIC SYSTEMS AND SOFTWARE ENGINEERING TECHNOLOGY, PROCEEDINGS, 2000, :85-90

[29] Examining the Potentially Confounding Effect of Class Size on the Associations between Object-Oriented Metrics and Change-Proneness [J].

Zhou, Yuming ;

Leung, Hareton ;

Xu, Baowen .

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2009, 35 (05) :607-623

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