Abstract
In today’s world, software is the key element for the functionality of almost all engineered and automated systems. Due to this evolution, reliability and quality of software systems become crucial for the successful functioning of day-to-day operations.
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References
Adrion, W.R., Branstad, M.A., Cherniavsky, J.C.: Validation, verification, and testing of computer software. ACM Comput. Surv. 14(2), 159–192 (1982)
Arisholm, E., Briand, L., Johannessen, E.B.: A systematic and comprehensive investigation of methods to build and evaluate fault prediction models. J. Syst. Softw. 83(1), 2–17 (2010)
Boehm, B.W., Papaccio, P.N.: Understanding and controlling software costs. IEEE Trans. Softw. Eng. 14(10), 1462–1477 (1988)
Bridge, N., Miller, C.: Orthogonal defect classification using defect data to improve software development. Softw. Qual. 3(1), 1–8 (1998)
Catal, C.: Software fault prediction: A literature review and current trends. Expert Syst. Appl. 38(4), 4626–4636 (2011)
Catal, C., Diri, B.: A fault prediction model with limited fault data to improve test process. In: Proceedings of the Product-Focused Software Process Improvement, vol. 5089, pp. 244–257 (2008)
Grottke, M., Trivedi, K.S.: A classification of software faults. J. Reliab. Eng. Assoc. Jpn. 27(7), 425–438 (2005)
Hall, T., Beecham, S., Bowes, D., Gray, D., Counsell, S.: A systematic literature review on fault prediction performance in software engineering. IEEE Trans. Softw. Eng. 38(6), 1276–1304 (2012)
Huizinga, D., Kolawa, A.: Automated Defect Prevention: Best Practices in Software Management. Wiley (2007)
Hryszko, J., Madeyski, L.: Cost Effectiveness of software defect prediction in an industrial project. Found. Comput. Decis. Sci. 43(1), 7–35 (2018)
Jiang, Y., Cukic, B., Ma, Y.: Techniques for evaluating fault prediction models. Empir. Softw. Eng. 13(5), 561–595 (2008)
Kim, S., Zhang, H., Wu, R., and Gong, L.: Dealing with noise in defect prediction. In: Proceedings of the 33rd International Conference on Software Engineering, pp. 481–490 (2011)
Li, N., Li, Z., Sun, X.: Classification of software defect detected by black-box testing: an empirical study. In: Proceedings of IEEE Second World Congress on Software Engineering (WCSE), vol. 2, pp. 234–240 (2010)
Li, P. L., Herbsleb, J., Shaw, M., Robinson, B.: Experiences and results from initiating field defect prediction and product test prioritization efforts at ABB Inc. In: Proceedings of the 28th International Conference on Software Engineering, pp. 413–422 (2006)
Menzies, T., DiStefano, J., Orrego, A., Chapman, R.: Assessing predictors of software defects. In: Proceedings of the Workshop Predictive Software Models, pp. 1–5 (2004)
Menzies, T., Milton, Z., Turhan, B., Cukic, B., Jiang, Y., Bener, A.: Defect prediction from static code features: current results, limitations, new approaches. Autom. Softw. Eng. J. 17(4), 375–407 (2010)
Monden, A., Hayashi, T., Shinoda, S., Shirai, K., Yoshida, J., Barker, M., et al.: Assessing the cost effectiveness of fault prediction in acceptance testing. IEEE Trans. Softw. Eng. 39(10), 1345–1357 (2013)
PROMISE: The PROMISE repository of empirical software engineering data. http://openscience.us/repo (2015)
Spiros Mancoridis: A Taxonomy of Bugs. Drexel University. https://www.cs.drexel.edu/~jhk39/teaching/cs576su06/L8.pdf (2010)
Vipindeep, V., Jalote, P.: List of Common Bugs and Programming Practices to Avoid Them. March Electronics
Zhou, Y., Leung, H.: Empirical analysis of object-oriented design metrics for predicting high and low severity faults. IEEE Trans. Softw. Eng. 32(10), 771–789 (2006)
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Rathore, S.S., Kumar, S. (2019). Introduction. In: Fault Prediction Modeling for the Prediction of Number of Software Faults. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-13-7131-8_1
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DOI: https://doi.org/10.1007/978-981-13-7131-8_1
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