Abstract
Accurately predicting the number of faults in program modules is a major problem in the quality control of large software development efforts. Some software complexity metrics are closely related to the distribution of faults across program modules. Using these relationships, software engineers develop models that provide early estimates of quality metrics that do not become available until late in the development cycle. By considering these early estimates, software engineers can take actions to avoid or prepare for emerging quality problems. Most often, the predictive models are based upon multiple regression analysis. However, measures of software quality and complexity exhibit systematic departures from the assumptions of these analyses. With extreme violations of these assumptions, multiple regression models become unstable and lose most of their predictive quality. Since neural network models carry no data assumptions, these models could be more appropriate than regression models for modeling software faults. In this paper, we explore a neural network methodology for developing models that predict the number of faults in program modules. We apply this methodology to develop neural network models based upon data collected during the development of two commercial software systems. After developing neural network models, we apply multiple linear regression methods to develop regression models on the same data. For the data sets considered, the neural network methodology produced better predictive models in terms of both quality of fit and predictive quality.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Briand, L.C. and V.R. Basili (1992), “A Classification Procedure for the Effective Management of Changes during the Maintenance Process”, InProceedings of the IEEE Conference on Software Maintenance, Orlando, Florida, pp. 328–336.
Dillon, W.R. and M. Goldstein (1984),Multivariate Analysis: Methods and Applications, Wiley, New York.
Fahlman, S. (1988), “An Empirical Study of Learning in Back-Propagation Networks”, Technical Report CMU-CS-88-162, Computer Science Deartment, Carnegie-Mellon University, Pittsburgh, PA.
Fahlman, S. and C. Lebiere (1990), “The Cascaded-Correlation Learning Architecture”, Technical Report CMU-CS-90-100, Computer Science Department, Carnegie-Mellon University, Pittsburgh, PA.
Gaffney, J.E. (1984), “Estimating the Number of Faults in Code”,IEEE Transactions on Software Engineering SE-10, 459–464.
Halstead, M.H. (1977),Elements of Software Science, Elsevier North-Holland, New York.
Henry, S.M. and S. Wake (1991), “Predicting Maintainability with Software Quality Metrics”,Software Maintenance: Research and Practice 3, 129–143.
Jacobs, R. (1988), “Increases Rates of Convergence Through Learning Rate Adaptation”,Neural Networks 1, 295–307.
Khoshgoftaar, T.M., D.L. Lanning, and A.S. Pandya (1993a), “A Neural Network Modeling Methodology for the Detection of High-Risk Programs”, InProceedings of the Fourth IEEE International Symposium on Software Reliability Engineering, Denver, Colorado, pp. 302–309.
Khoshgoftaar, T.M., D.L. Lanning, and A.S. Pandya (1994), “A Comparative Study of Pattern Recognition Techniques for Quality Evaluation of Telecommunications Software”,IEEE Journal of Selected Areas in Communications 12, 2, 279–291.
Khoshgoftaar, T.M. and J.C. Munson (1990), “Predicting Software Development Errors Using Complexity Metrics”,IEEE Journal of Selected Areas in Communications 8, 2, 253–261.
Khoshgoftaar, T.M., J.C. Munson, B.B. Bhattacharya, and G.D. Richardson (1992a), “Predictive Modeling Techniques of Software Quality from Software Measures”,IEEE Transactions on Software Engineering 18, 11, 979–987.
Khoshgoftaar, T.M., J.C. Munson, and D.L. Lanning (1993b), “A Comparative Study of Predictive Models for Program Changes During System Testing and Maintenance”, InProceedings of the IEEE Conference on Software Maintenance, Montreal, Quebec, Canada, pp. 72–79.
Khoshgoftaar, T.M., A.S. Pandya, and H.B. More (1992b), “A Neural Network Approach for Predicting Software Development Faults”, InProceedings of the Third IEEE International Symposium on Software Reliability Engineering, Research Triangle Park, North Carolina, pp. 83–89.
Levitin A. (1989), “TheL 1 Criteria in Data Analysis and the Problem of Software Size Estimation”, InProceedings of 21st Symposium on the Interface Computing Science and Statistics, pp. 382–383.
Lind, R.K. and K. Vairavan (1989), “An Experimental Investigation of Software Metrics and their Relationship to Software Development Effort”,IEEE Transactions on Software Engineering 15, 5, 649–651.
McCabe, T.J. (1976), “A Complexity Metric”,IEEE Transactions on Software Engineering 2, 4, 308–320.
Munson, J.C. and T.M. Khoshgoftaar (1991), “Some Primitive Control Flow Metrics”, InProceedings of the Annual Oregon Workshop on Software Metrics, Silver Falls, Oregon.
Myers, R.H. (1990),Classical and Modern Regression with Applications, Duxbury Press, Boston, MA.
Narula, S.C. and J.F. Wellington (1977), “Prediction, Linear Regression and the Minimum Sum of Relative Errors,Technometrics 19, 185–190.
Ooyen, A.V. and B. Niemhuis (1992), “Improving the Convergence of the Back-Propagation Algorithm”,Neural Networks 5, 465–471.
Pedone, R. and D. Parisi (1991), “Learning the Learning Parameters”, InProceedings of the IJCNN, Singapore, pp. 2033–2037.
Rumelhart, D.E., G.E. Hinton, and R.J. Williams (1986),Parallel Distributed Processing: Explorations in the Microstructure of Cognition, volume 1, chapter Learning Internal Representations by Error Propagation, MIT Press, Cambridge, MA, pp. 318–362.
Selby, R.W. and V.R. Basili (1991), “Analyzing Error-Prone System Structure”,IEEE Transactions on Software Engineering 17, 2, 141–152.
Shen, V.Y., T. Yu, S.M. Thebaut, and L.R. Paulsen (1985), “Identifying Error-Prone Software — An Empirical Study”,IEEE Transactions on Software Engineering SE-11, 317–323.
Tolleneare, T. (1990), “SuperSAB: Fast Adaptive Back-Propogation with Good Scaling Properties”,Neural Networks 3, 561–573.
Vogl, T.P., J.K. Mangis, A.K. Rigler, W.T. Zink, and D.L. Alkon (1988), “Accelerating the Convergence of the Back-Propogation Method”,Biological Cybernetics 59, 257–263.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Khoshgoftaar, T.M., Pandya, A.S. & Lanning, D.L. Application of neural networks for predicting program faults. Ann Software Eng 1, 141–154 (1995). https://doi.org/10.1007/BF02249049
Issue Date:
DOI: https://doi.org/10.1007/BF02249049