论文信息 - Software Defect Prediction using Adaptive Neural Networks

Software Defect Prediction using Adaptive Neural Networks

We present a system which gives prior idea about the defective module. The task is accomplished using Adaptive Resonance Neural Network (ARNN), a special case of unsupervised learning. A vigilance parameter (θ) in ARNN defines the stopping criterion and hence helps in manipulating the accuracy of the trained network. To demonstrate the usefulness of ARNN, we used dataset from promisedata.org. This dataset contains 121 modules out of which 112 are not defected and 9 are defected. In this dataset modules are termed as defected on the basis of three measures that are LOC, HALSTEAD, MCCABE measures that have been normalized in the range of 0-1.We see that at θ=0.1858 the network has maximum Recall (i.e. true negative rate) is 100% and average Precision=54%.In case of ART n/w shortfalls are seen for Accuracy as this is a subjective measure. Keyword: Resonance, Clustering, Unsupervised learning,

Mandeep Singh | Seema Singh

[1] Felix Kossak,et al. Key Questions in Building Defect Prediction Models in Practice , 2009, PROFES.

[2] Kishan G. Mehrotra,et al. Elements of artificial neural networks , 1996 .

[3] Mian M. Awais,et al. An FIS for Early Detection of Defect Prone Modules , 2009, ICIC.

[4] N. Nagappan,et al. Static analysis tools as early indicators of pre-release defect density , 2005, Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005..

[5] E. Salahi. Anomaly Intrusion Detection Design Using Hybrid of Unsupervised and Supervised Neural Network , 2009 .

[6] Harvey P. Siy,et al. Predicting Fault Incidence Using Software Change History , 2000, IEEE Trans. Software Eng..

[7] Venkata U. B. Challagulla,et al. Empirical assessment of machine learning based software defect prediction techniques , 2005, 10th IEEE International Workshop on Object-Oriented Real-Time Dependable Systems.

[8] Tong-Seng Quah,et al. Application of neural networks for software quality prediction using object-oriented metrics , 2005, J. Syst. Softw..

[9] Tong-Seng Quah,et al. Application of neural networks for software quality prediction using object-oriented metrics , 2003, International Conference on Software Maintenance, 2003. ICSM 2003. Proceedings..