It is Impossible to build a software which is completely tested or bug free. Manual bug fixing is very time taking, costly and clumsy task. To automate the process of software bug fixing various machine learning techniques are employed. Software bug prediction is implemented before testing phase of software development life cycle model while bug handling is a post testing phase arises after the failure of test cases. Software bug handling deals with the phases of software bug life cycle model. Bug reports are one of the most important software artifacts for handling of bugs. In recent years, due to release of thousands of open source software, large amount of repositories (like bug repositories) are available for software analytics. Analytics help software practitioners in taking decisions with logic instead of intuitions which make it more accurate and practical. Prediction and Handling of software bugs uses this analytics in automation with the help of machine learning techniques. In this paper we focused on predictive capability of different machine learning techniques in association with software bug prediction and handling. Findings and previous work is summarized with the help of tables (in association with attributes) and diagrams (in mapping with software bug life cycle model).

Software Bug, Computational Intelligence, Analytics, Naïve Bayes (NB), Support Vector Machine (SVM)

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