Bug classification using machine and Deep Learning Algorithms

Abstract DevOps is a method used to automate the process between development team and the IT team through which they can develop, test and release their software in a reliable way. Bugs during this stage slows the entire release cycle. To overcome this, Machine Learning and Deep Learning Algorithms are used to analyze and arrive at the possible cause of the bug. This reduces the dependency on the developers and in turn speeds up the release cycle. The bug dataset is fed to various classification algorithms like CNN, Random Forest, Decision Tree, SVM and Naive Bayes for bug classification.

[1]  Hideaki Hata,et al.  Bug or Not? Bug Report Classification Using N-Gram IDF , 2017, 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME).

[2]  Shaofu Lin,et al.  Research on Text Classification Based on CNN and LSTM , 2019, 2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA).

[3]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[4]  Anuja Arora,et al.  A bug Mining tool to identify and analyze security bugs using Naive Bayes and TF-IDF , 2014, 2014 International Conference on Reliability Optimization and Information Technology (ICROIT).

[5]  Shuo Xu,et al.  Bayesian Naïve Bayes classifiers to text classification , 2018, J. Inf. Sci..

[6]  Marti A. Hearst.  Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..

[7]  Muhammad Younus Javed,et al.  An Automated Approach for Software Bug Classification , 2012, 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems.

[8]  Chen Liu,et al.  R2Fix: Automatically Generating Bug Fixes from Bug Reports , 2013, 2013 IEEE Sixth International Conference on Software Testing, Verification and Validation.

[9]  Tao Zhang,et al.  A Bug Rule Based Technique with Feedback for Classifying Bug Reports , 2011, 2011 IEEE 11th International Conference on Computer and Information Technology.

[10]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.