Abstract:
In bug repositories, we receive a large number of bug reports on daily basis. Managing such a large repository is a challenging job. Priority of a bug tells that how impo...Show MoreMetadata
Abstract:
In bug repositories, we receive a large number of bug reports on daily basis. Managing such a large repository is a challenging job. Priority of a bug tells that how important and urgent it is for us to fix. Priority of a bug can be classified into 5 levels from PI to P5 where PI is the highest and P5 is the lowest priority. Correct prioritization of bugs helps in bug fix scheduling/assignment and resource allocation. Failure of this will result in delay of resolving important bugs. This requires a bug prediction system which can predict the priority of a newly reported bug. Cross project validation is also an important concern in empirical software engineering where we train classifier on one project and test it for prediction on other projects. In the available literature, we found very few papers for bug priority prediction and none of them dealt with cross project validation. In this paper, we have evaluated the performance of different machine learning techniques namely Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN) and Neural Network (NNet) in predicting the priority of the newly coming reports on the basis of different performance measures. We performed cross project validation for 76 cases of five data sets of open office and eclipse projects. The accuracy of different machine learning techniques in predicting the priority of a reported bug within and across project is found above 70% except Naive Bayes technique.
Published in: 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)
Date of Conference: 27-29 November 2012
Date Added to IEEE Xplore: 24 January 2013
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