Abstract
In upgrade project development, Enhancement Requirements (ER, e.g. requirement additions and modifications) introduce new defects to the project. We need to evaluate this impact to help plan later project schedule and resources. Typically, many of the existing prediction technologies estimate defects based on software size or process performance baselines. However, they are limited in estimating the impact of ER on product quality. This paper proposes a novel ER-based defect prediction method using information retrieval (IR) technique and support vector machines (SVM). We analyze the historical data of defects and requirement specifications of actual upgrade projects to establish multiple prediction models to estimate new defects introduced by ER. Then we design two experiments to validate the method and report some preliminary results. The results indicate that our method can provide useful support for impact analysis of requirement evolution in upgrade projects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lanning, D.L., Khoshgoftaar, T.M.: The Impact of Software Enhancement on Software Reliability. IEEE Transactions on Reliability 44(4), 677–682 (1995)
Koru, A.G., Liu, H.: Building Effective Defect-prediction Models in Practice. Software, IEEE 22(6), 23–29 (2005)
Menzies, T., Greenwald, J., Frank, A.: Data Mining Static Code Attributes to Learn Defect Predictors. IEEE Transactions on Software Engineering 33(1), 2–13 (2007)
Boehm, B.W., Horowitz, E., Madachy, R., et al.: Software Cost Estimation with COCOMO II. Prentice Hall PTR, Upper Saddle River (2000)
Gou, L., Wang, Q., Yuan, J., Yang, Y., Li, M., Jiang, N.: Quantitative Managing Defects for Iterative Projects: An Industrial Experience Report in China. In: Wang, Q., Pfahl, D., Raffo, D.M. (eds.) ICSP 2008. LNCS, vol. 5007, pp. 369–380. Springer, Heidelberg (2008)
Wang, Q., Wu, S.J., Li, M.S.: Software Defect Prediction. Journal of Software 19(7), 1565–1580 (2008)
Fenton, N., Neil, M., Marsh, W., Hearty, P., Radlinski, Ł.: On the Effectiveness of Early Life Cycle Defect Prediction with Bayesian Nets. mpir Software Eng. 13, 499–537 (2008)
Malaiya, Y.K., Denton, J.: Requirements Volatility and Defect Density. In: 10th International Symposium on Software Reliability Engineering, p. 285 (1999)
Xing, F., Guo, P., Lyu, M.: A novel method for early software quality prediction based on support vector machine. In: Proc. of the 16th IEEE Int’l Symp. on Software Reliability Engineering (ISSRE 2005), pp. 213–222 (2005)
Gospodnetic, O., Hatcher, E.: Lucene in Action. Maning Publication (2006)
Qin, J., Lu, R.Z.: Feature Extraction in Text Categorization. Journal of Computer Applications 23(2), 45–46 (2003)
Elish, K.O., Elish, M.O.: Predicting Defect-prone Software Modules using Support Vector Machines. The Journal of Systems and Software 81, 49–660 (2008)
Gunn, S.R.: Support Vector Machines for Classification and Regression, Technical Report. Faculty of Engineering, Science and Mathematics School of Electronics and Computer Science, University of Southampton (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
He, L., Li, J., Wang, Q., Yang, Y. (2009). Predicting Upgrade Project Defects Based on Enhancement Requirements: An Empirical Study. In: Wang, Q., Garousi, V., Madachy, R., Pfahl, D. (eds) Trustworthy Software Development Processes. ICSP 2009. Lecture Notes in Computer Science, vol 5543. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01680-6_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-01680-6_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01679-0
Online ISBN: 978-3-642-01680-6
eBook Packages: Computer ScienceComputer Science (R0)