Open Access Open Access  Restricted Access Subscription Access

A State of the art review on Software Defect Prediction Techniques

Vijay Kumar R

Abstract


Predicting defects is one of the most difficult parts of the software development process, but it can help cut down on testing time and money while also improving software quality. Past surveys zeroed in on imperfection forecast as a rule, and none have explicitly tended to desert expectation in light of the semantic portrayal of projects from source code. This review examines the motivations, datasets, cutting-edge techniques, difficulties, and potential future research directions of software defect research over the past three decades. Semantic-based methods based on source code are a particular focus of ours. Because they represent the current state of the art in the field, semantic feature-based techniques also receive our special attention. Cross-project defect prediction (CPDP), within-project defect prediction (WPDP), and the most recent datasets are our primary focus. Present and analyzed are defect datasets for sixty projects written in various programming languages (C, Java, and C++). In order to provide the reader with a reference for significant topics that require investigation, open issues are examined and potential research directions in defect prediction are proposed.

Full Text:

PDF

References


Z. Wan, X. Xia, A.E. Hassan, D. Lo, J. Yin, X. Yang, ”Perceptions, expectations, and challenges in defect prediction,” IEEE Transactions on Software Engineering, vol. 46, pp. 1241-1266, 2018.

B. Boehm, V.R. Basili, ”Software defect reduction top 10 list,” Software engineering: Barry W. Boehm's lifetime contributions to software development, management, and research, vol. 34, pp. 75, 2007.

R. Subramanyam, M.S. Krishnan, ”Empirical analysis of ck metrics for object-oriented design complexity: Implications for software defects,” IEEE Transactions on software engineering, vol. 29, pp. 297-310, 2003.

N. Nagappan, T. Ball, A. Zeller, “Mining metrics to predict component failures,” in: Proceedings of the 28th international conference on Software engineering, 2006, pp. 452-461.

R. Moser, W. Pedrycz, G. Succi, “A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction,” in: Proceedings of the 30th international conference on Software engineering, 2008, pp. 181-190.

A.E. Hassan, “Predicting faults using the complexity of code changes,” in: 2009 IEEE 31st international conference on software engineering, IEEE, 2009, pp. 78-88.

N.E. Fenton, N. Ohlsson, ”Quantitative analysis of faults and failures in a complex software system,” IEEE Transactions on Software engineering, vol. 26, pp. 797-814, 2000.

T. Gyimóthy, R. Ferenc, I. Siket, ”Empirical validation of object-oriented metrics on open source software for fault prediction,” IEEE Transactions on Software engineering, vol. 31, pp. 897-910, 2005.

H. Liang, Y. Yu, L. Jiang, Z. Xie, ”Seml: A semantic LSTM model for software defect prediction,” IEEE Access, vol. 7, pp. 83812-83824, 2019.

N.E. Fenton, M. Neil, ”A critique of software defect prediction models,” IEEE Transactions on software engineering, vol. 25, pp. 675-689, 1999.


Refbacks

  • There are currently no refbacks.