Text analytics based severity prediction of software bugs for apache projects

Arvinder Kaur () and Shubhra Goyal Jindal ()
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Arvinder Kaur: Guru Gobind Singh Indraprastha University
Shubhra Goyal Jindal: Guru Gobind Singh Indraprastha University

International Journal of System Assurance Engineering and Management, 2019, vol. 10, issue 4, No 25, 765-782

Abstract: Abstract Severity i.e impact, extent and effect on software is a decisive attribute which decides how instantly the bug should be fixed. Predicting the severity of software bugs is important to improve the bug triaging and resolution process. To reduce the effort and time required in manual assessment of severity of newly reported bugs, many techniques and methods are used in past researches. To help software developers to utilize their resources efficiently, this study evaluates a number of machine learning techniques for predicting the severity of software bugs at system and component level. The techniques are evaluated on thirteen apache projects automatically extracted using the Bug Report Collection System tool. Severity is predicted based on the most frequent terms extracted from the summary of bugs using text mining. Performance metrics such as precision, recall and accuracy are used to interpret the results obtained from various techniques. The result of the study advocates that Boosting (an ensemble learner) technique outperforms other machine learning techniques such as Bayesian learners, decision tree, support vector machine applied in previous researches.

Keywords: Machine learning; Textual description; Empirical validation; Severity prediction; Statistical methods (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13198-019-00807-8

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