Abstract
Bug severity defines that to what extent the bug influence the software. Bug reports are misclassified affecting the performance of prediction and lead to biasness. Multitude relevant and irrelevant bugs are reported through bug-tracking systems, and it becomes necessary for the developer to give precedence to the bugs reported. The critical bugs need to be fixed immediately, and minor bugs can be fixed later on the basis of available resources. In this paper, we define different severity levels and assort them on the basis of their severity levels. It becomes problematic to assort tons of bugs manually, so we have used text mining approach to congregate words pointing severity of the reports obtained from Bugzilla for different components of ECLIPSE. The TF-IDF method is used to extract features and make dictionary of critical words. We have adopted neural network for the classification of bug reports and measured its performance.
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References
Menzies T, Marcus A (2008) Automated severity assessment of software defect reports. In: IEEE international conference on software maintenance, 2008. ICSM 2008. IEEE, pp. 346–355
Lamkanfi A, Demeyer S, Giger E, Goethals B (2010) Predicting the severity of a reported bug. In: 2010 7th IEEE working conference on mining software repositories (MSR). IEEE, pp 1–10
Lamkanfi A, Demeyer S, Soetens QD, Verdonck T (2011) Comparing mining algorithms for predicting the severity of a reported bug. In: CSMR, Carl von Ossietzky University, Oldenburg, Germany. IEEE, New York, pp 249–258
Gegick M, Rotella P, Xie T (2010) Identifying security bug reports via text mining: an industrial case study. In: 2010 7th IEEE working conference on mining software repositories (MSR). IEEE, pp 11–20
Ghaluh Indah Permata S (2012) An attribute selection for severity level determination according to the support vector machine classification result. In: Proceedings of the international conference on information system business competitiveness
Neelofar, Javed MY, Mohsin H (2012) An automated approach for software bug classification. In: 2012 sixth international conference on complex, intelligent and software intensive systems (CISIS). IEEE, pp 414–419
Sharma M, Bedi P, Chaturvedi KK, Singh VB (2012) Predicting the priority of a reported bug using machine learning techniques and cross project validation. In: 2012 12th international conference on intelligent systems design and applications (ISDA). IEEE, pp 539–545
Chaturvedi KK, Singh VB (2012) An empirical comparison of machine learning techniques in predicting the bug severity of open and closed source projects. Int J Open Source Softw Process (IJOSSP) 4(2):32–59
Chaturvedi KK, Singh VB (2012) Determining bug severity using machine learning techniques. In: 2012 CSI sixth international conference on software engineering (CONSEG). IEEE, pp 1–6
Tian Y, Lo D, Sun C (2012) Information retrieval based nearest neighbor classification for fine-grained bug severity prediction. In: 2012 19th working conference on reverse engineering (WCRE). IEEE, pp 215–224
Tian Y, Lo D, Sun C (2013) Drone: predicting priority of reported bugs by multi-factor analysis. In: 2013 IEEE international conference on software maintenance. IEEE, pp 200–209
Nagwani NK, Verma S, Mehta KK (2013) Generating taxonomic terms for software bug classification by utilizing topic models based on Latent Dirichlet allocation. In: 2013 11th international conference on ICT and knowledge engineering (ICT&KE). IEEE, pp 1–5
Roy NKS, Rossi B (2014) Towards an improvement of bug severity classification. In: 2014 40th EUROMICRO conference on software engineering and advanced applications (SEAA). IEEE, pp 269–276
15 most popular bug tracking software to ease your defect management process. http://www.softwaretestinghelp.com/popular-bugtracking-software/, 12 Feb 2015
Tian Y, Ali N, Lo D, Hassan AE (2016) On the unreliability of bug severity data. Empirical Softw Eng 21(6):2298–2323
Zhang T, Yang G, Lee B, Chan AT (2015) Predicting severity of bug report by mining bug repository with concept profile. In: Proceedings of the 30th annual ACM symposium on applied computing. ACM, pp 1553–1558
Jin K, Dashbalbar A, Yang G, Lee JW, Lee B (2016) Bug severity prediction by classifying normal bugs with text and meta-field information. Adv Sci Technol Lett 129:19–24
Pandey N, Sanyal DK, Hudait A, Sen A (2017) Automated classification of software issue reports using machine learning techniques: an empirical study. Innov Syst Softw Eng 13(4):279–297
Liu W, Wang S, Chen X, Jiang H (2018) Predicting the severity of bug reports based on feature selection. Int J Softw Eng Knowl Eng 28(04):537–558
Yang G, Zhang T, & Lee B (2014) Towards semi-automatic bug triage and severity prediction based on topic model and multi-feature of bug reports. In: 2014 IEEE 38th annual computer software and applications conference (COMPSAC). IEEE, pp 97–106
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Bibyan, R., Anand, S., Jaiswal, A. (2020). Assessing the Severity of Software Bug Using Neural Network. In: Kapur, P.K., Singh, O., Khatri, S.K., Verma, A.K. (eds) Strategic System Assurance and Business Analytics. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-15-3647-2_35
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DOI: https://doi.org/10.1007/978-981-15-3647-2_35
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