Abstract During software development and maintenance phases, the fixing of severe bugs are mostly very challenging and needs more efforts to fix them on a priority basis. Several research works have been performed using software metrics and predict fault-prone software module. In this paper, we propose an approach to categorize different types of bugs according to their severity and priority basis and then use them to label software metrics’ data. Finally, we used labeled data to train the supervised machine learning models for the prediction of fault prone software modules. Moreover, to build an effective prediction model, we used genetic algorithm to search those sets of metrics which are highly correlated with severe bugs.

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[29] Zimmermann T., Nagappan N., Guo P., and Murphy B., “Characterizing and Predicting Which Bugs Get Reopened, ” in Proceedings 34th International Conference on Software Engineering, Zurich, pp. 1074-1083, 2012. Ishrat-Un-Nisa Uqaili is a Final year student of M.S (Computer Science) at Faculty of Engineering Science and Technology (FEST), Iqra University (IU), Defence View (Main Campus), Shaheed-e-Millat Road (Ext.) Karachi-75500, Pakistan. She did B.E in Computer Systems from Mehran University of Engineering & Technology, Jamshoro, Pakistan. Her research interests include machine learning application in Software Engineering, and build models for automatic software maintenance. She has recently completed her MS final year thesis on Fault Prediction Model for Software using Soft Computing Techniques. Syed Nadeem Ahsan did his Ph.D. in Computer Science from GRAZ University of Technology, Austria. Currently, he is doing R&D work in software engineering and machine learning applications, and also associated with FEST, IU, Main Campus, Karachi. His Research interest includes software maintenance & evolution, software testing, formal methods in software engineering, modeling and simulation of complex system, and computational intelligence.