Abstract
Existing defect management tools provide little information on how important/urgent for developers to fix defects reported. Manually prioritizing defects is time-consuming and inconsistent among different people. To improve the efficiency of troubleshooting, the paper proposes to employ neural network techniques to predict the priorities of defects, adopt evolutionary training process to solve error problems associated with new features, and reuse data sets from similar software systems to speed up the convergence of training. A framework is built up for the model evaluation, and a series of experiments on five different software products of an international healthcare company to demonstrate the feasibility and effectiveness.
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References
Rothermel, G., Untch, R.H., Chu, C., Harrold, M.J.: Prioritizing Test Cases for Regression Testing. IEEE Transactions on Software Engineering (2001)
Dawson, E., David, Y.C., Seth, H.: Bugs as Deviant Behavior: A General Approach to Inferring Errors in Systems Code (2003)
Miguel, R., Paulo, C., José, N.: Evolution of neural networks for classification and regression. Neurocomputing (2007)
David, H., William, P.: Finding Bugs is Easy. ACM SIGPLAN Notices (2004)
Michael, D.: Static and dynamic analysis: synergy and duality. IEEE Computer Society, Los Alamitos (2005)
The Byte Code Engineering Library, http://jakarta.apache.org/bcel/2003
Ted, K., Dawson, E.: Using Statistical Analysis to Counter the Impact of Static Analysis Approximations. In: 10th International Symposium (2003)
Thomas, J., Ostrand, E.J., Weyuker, R.M.: Predicting the Location and Number of Faults in Large Software Systems. IEEE Transactions on Software Engineering (2005)
Khoshgoftaar, T.M., Allen, E.B., Kalaichelva, K.S.: Early Quality Prediction: A Case Study in Telecommunications, IEEE Software, pp. 65–71 (1996)
Graves, T.L., Karr, A.F., Marron, J.S.: Predicting Fault Incidence Using Software Change History. IEEE Tran., Software Reliability Eng. 26, 653–661 (2000)
Mark, L., Menahem, F., Abraham, K.: The Data Mining Approach to Automated Software Testing. ACM, New York (2003)
Kremenek, T., Ashcraft, K., Yang, J.: Correlation Exploitation in Error Ranking. In: Proceedings of the 12th ACM SIGSOFT Twelfth International (2004)
Bush, W., Pincus, J., Sielaff, D.: A static analyzer for finding dynamic programming errors. In: Software: Practice and Experience, pp. 775–802 (2000)
Hallem, S., Chelf, B., Xie, Y.: A system and language for building system-specific, static analysis. In: PLDI (2002)
Hallem, S., Lam, M.S.: Tracking down software bugs using automatic anomaly detection. In: International Conference on Software Engineering (2002)
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Yu, L., Tsai, WT., Zhao, W., Wu, F. (2010). Predicting Defect Priority Based on Neural Networks. In: Cao, L., Zhong, J., Feng, Y. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17313-4_35
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DOI: https://doi.org/10.1007/978-3-642-17313-4_35
Publisher Name: Springer, Berlin, Heidelberg
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