An effective fault prediction model developed using an extreme learning machine with various kernel methods
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System analysts often use software fault prediction models to identify fault-prone modules during the design phase of the software development life cycle. The models help predict faulty modules based on the software metrics that are input to the models. In this study, we consider 20 types of metrics to develop a model using an extreme learning machine associated with various kernel methods. We evaluate the effectiveness of the mode using a proposed framework based on the cost and efficiency in the testing phases. The evaluation process is carried out by considering case studies for 30 object-oriented software systems. Experimental results demonstrate that the application of a fault prediction model is suitable for projects with the percentage of faulty classes below a certain threshold, which depends on the efficiency of fault identification (low: 47.28%; median: 39.24%; high: 25.72%). We consider nine feature selection techniques to remove the irrelevant metrics and to select the best set of source code metrics for fault prediction.
Key wordsCK metrics Cost analysis Extreme learning machine Feature selection techniques Object-oriented software
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The researchers are grateful to the FIST project, of DST, government of India for sponsoring the work on web engineering and cloud based computing. The researchers are thankful to the Computer Science & Engineering Department, NIT Rourkela, for providing all facilities and guidance.
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