Two case studies of open source software development: Apache and Mozilla
ACM Transactions on Software Engineering and Methodology (TOSEM)
Predicting the fix time of bugs
Proceedings of the 2nd International Workshop on Recommendation Systems for Software Engineering
Fine-grained incremental learning and multi-feature tossing graphs to improve bug triaging
ICSM '10 Proceedings of the 2010 IEEE International Conference on Software Maintenance
Bug-fix time prediction models: can we do better?
Proceedings of the 8th Working Conference on Mining Software Repositories
Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction
WCRE '12 Proceedings of the 2012 19th Working Conference on Reverse Engineering
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Software bug is a buzz word now a day. A software bug has many attributes, some of which are filled at the time of reporting and others are filled during the process of fixing. Some attributes are qualitative in nature but some are quantitative. A clear understanding of bug attributes, their interdependence and their contribution in predicting the other attributes will help in improving the quality of software. In the literature, prediction models based on linear regression have been proposed to predict the bug attributes and to determine their linear relationships. cc list (manpower involved in monitoring the progress of bug fix) is an important bug attribute for which no prediction model has been developed in literature. We investigated the contribution of bug attributes in predicting the bug cc list i.e. the manpower involved in monitoring the progress of bug fix based on multiple linear regression (MLR), support vector regression (SVR) and fuzzy linear regression (FLR). We conducted the experiments to develop prediction models for 21,424 bug reports of Firefox, Thunderbird, Seamonkey, Boot2Gecko, Add-on SDK, Bugzilla, Webtools and addons.mozilla.org products of the Mozilla open source project. We have also investigated a linear relation among bug attributes. The empirical results conclude that the value of R2 in predicting cc list across all datasets lies in the range of 0.31 to 0.70, 0.54 to 0.88, 0.25 to 0.68 and 0.69 to 0.93 for multiple linear regression, support vector regression, fuzzy linear regression(robust off) and fuzzy linear regression (robust bisquare) respectively.