论文信息 - Building effective defect-prediction models in practice

Building effective defect-prediction models in practice

Defective software modules cause software failures, increase development and maintenance costs, and decrease customer satisfaction. Effective defect prediction models can help developers focus quality assurance activities on defect-prone modules and thus improve software quality by using resources more efficiently. These models often use static measures obtained from source code, mainly size, coupling, cohesion, inheritance, and complexity measures, which have been associated with risk factors, such as defects and changes.

Hongfang Liu | Akif Günes Koru | Hongfang Liu | A. G. Koru

[1] Ramanath Subramanyam,et al. Empirical Analysis of CK Metrics for Object-Oriented Design Complexity: Implications for Software Defects , 2003, IEEE Trans. Software Eng..

[2] Taghi M. Khoshgoftaar,et al. Application of neural networks to software quality modeling of a very large telecommunications system , 1997, IEEE Trans. Neural Networks.

[3] Akif Günes Koru,et al. An empirical comparison and characterization of high defect and high complexity modules , 2003, J. Syst. Softw..

[4] Bojan Cukic,et al. Robust prediction of fault-proneness by random forests , 2004, 15th International Symposium on Software Reliability Engineering.

[5] Taghi M. Khoshgoftaar,et al. The necessity of assuring quality in software measurement data , 2004, 10th International Symposium on Software Metrics, 2004. Proceedings..

[6] Standard Glossary of Software Engineering Terminology , 1990 .

[7] Tim Menzies,et al. Mining Repositories to Assist in Project Planning and Resource Allocation , 2004, MSR.

[8] Tim Menzies,et al. The \{PROMISE\} Repository of Software Engineering Databases. , 2005 .

[9] Ian Witten,et al. Data Mining , 2000 .

[10] Elaine J. Weyuker,et al. Predicting the location and number of faults in large software systems , 2005, IEEE Transactions on Software Engineering.