Abstract
There are numerous methods for envisaging bugs in software practices. A widespread method for bug estimation is applying entropy of alterations as recommended by Hassan [5]. In the existing literature, investigators have recommended and executed a surplus of bug extrapolation methodologies, which change in relation to precision, difficulty and the input files they need, nonetheless actually rarely any of them has anticipated the amount of bugs in the software established on the entropy. A standard for bug estimation is showed, in the way of a widely accessible data set comprising of numerous software techniques, and a huge divergence of the comprehensive and analytical ability of familiar bug estimation methods, collectively with innovative methods is offered. Based on the outcomes, the working and constancy of the methods with regard to our standard are examined and a number of understandings on bug estimation models are determined. In the present communication, we will acquire a method by taking into concern the amount of bugs logged in numerous announcements of Bugzilla software and compute the distinctive measures of entropy, namely Shannon entropy [18] and Kapur entropy [9] for the variations in several software updates in these time intervals. Eventually, this work has the possibility to shed light on how to utilize simple linear regression to envisage the bugs which are still impending.
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References
Ambros, M.D., Lanza, M., Robbes, R.: An extensive comparison of bug prediction ap proaches. In: MSR’10: Proceedings of the 7th International Working Conference on Mining Software Repositories, pp. 31–41. (2010)
Ambros, M., Lanza, M., Robbes, R.: Evaluating defect prediction approaches: A benchmark and an extensive comparison. Empirical Softw. Eng. 17(4–5), 571–737 (2012)
Chaturvedi, K.K., Bedi, P., Mishra, S., Singh, V.B.: An empirical validation of the complexity of code changes and bugs in predicting the release time of open source software. In: Proceedings of the IEEE 16th International Conference on Computational Science and Engineering, pp. 1201–1206. Sydney, Australia (2013)
Chaturvedi, K.K., Kapur, P.K., Anand, S., Singh, V.B.: Predicting the complexity of code changes using entropy based measures. Int. J. Syst. Assur. Eng. Manag. 5(2), 155–164 (2014)
Hassan, A.E.: Predicting faults based on complexity of code change. In: Proceedings of the 31st International Conference on Software Engineering, pp. 78–88. Vancover, Canada (2009)
Hassan, A.E., Holt, R.C.: Studying the chaos in code development. In Proceedings of 10th Working Conference on Reverse Engineering (2003)
Hassan, A.E., Holt, R.C.: The chaos of software development. In: Proceedings of the 6th IEEE International Workshop on Principles of Software Evolution (2003)
Hassan, A.E., Holt, R.C.: The top ten list: dynamic fault prediction. In: Proceedings of ICSM, pp. 263–272. (2005)
Kapur, J.N.,: Generalized entropy of order α and β. The Maths Semi, pp. 79–84. (1967)
Kapur, P.K., Chanda, U., Kumar, V.: Dynamic allocation of testing effort when testing and debugging are done concurrently. Commun. Depend. Qual. Management 13(3), 14–28 (2010)
Kapur, P.K., Pham, H., Chanda, U., Kumar, V.: Optimal allocation of testing effort during testing and debugging phases: a control theoretic approach. Int. J. Syst. Sci. 44(9), 1639–1650 (2013)
Kapur, P.K., Singh, J.N.P., Sachdeva, N., Kumar, V.: Application of multi attribute utility theory in multiple releases of software. In: International Conference on Reliability, Infocom Technologies and Optimization, pp. 123–132. (2013)
Kaur, A., Kaur, K., Chopra, D.: An empirical study of software entropy based bug prediction using machine learning. Int. J. Syst Assur. Eng. Manage. 599–616 (2017)
Kaur, A., Chopra, D.: Entropy Churn metrics for fault prediction in software systems. Entropy (2018)
Kumari, M., Misra, A., Misra, S., Sanz, L., Damasevicius, R., Singh, V.: Quantity quality evaluation of software products by considering summary and comments entropy of a reported bug. Entropy (2019)
Pachauri, B., Kumar, A., Dhar, J.: Modeling optimal release policy under fuzzy paradigm in imperfect debugging environment. Elsevier, (2013). 10.1016/j.infsof.2013.06.001
Peng, R., Li, Y.F., Zhang, W.J., Hu, Q.P.: Testing effort dependent software reliability model for imperfect debugging process considering both detection and correction. Reliability Eng. Syst. Saf 126, 37–43 (2014)
Shannon, C.E.: A Mathematical Theory of Communication. Bell Syst. Tech. J. 27(379–423), 623–656 (1948)
Singh, V.B., Chaturvedi, K. K.: Entropy based bug prediction using support vector regression. In: Proceedings of 12th International Conference on Intelligent Systems Design and Applications during 27–29 Nov. 2012 at CUSAT, Kochi (India). IEEE Explore, pp. 746–751. (2012)
Singh, V.B., Chaturvedi, K.K.: Improving the quality of software by quantifying the code change metric and predicting the bugs. In: Murgante, B., et al. (eds.) ICCSA 2013, Part II, LNCS 7972, pp. 408–426. Springer, Berlin, Heidelberg (2013)
Singh, V.B., Chaturvedi, K.K., Khatri, S.K., Kumar, V.: Bug prediction modelling using complexity of code changes. Int. J. Syst Assur. Eng. Management 6(1), 44–60 (2014)
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Munde, A. (2021). Envisaging Bugs by Means of Entropy Measures. In: Senjyu, T., Mahalle, P.N., Perumal, T., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems. ICTIS 2020. Smart Innovation, Systems and Technologies, vol 196. Springer, Singapore. https://doi.org/10.1007/978-981-15-7062-9_15
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