Exhaustive and heuristic search approaches for learning a software defect prediction model |
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| Authors: | Parag C Pendharkar |
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| Affiliation: | 1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China;2. School of Information Systems, Singapore Management University, Singapore;1. School of Computer Science and Technology, Nantong University, Nantong, China;2. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, China;3. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;1. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, PR China;2. Intelligent Information Processing Key Laboratory of Shanxi Province, Shanxi University, Taiyuan, Shanxi 030006, PR China;3. Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan, Zhejiang, 316022 PR China;4. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, 210094 PR China |
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| Abstract: | In this paper, we propose a software defect prediction model learning problem (SDPMLP) where a classification model selects appropriate relevant inputs, from a set of all available inputs, and learns the classification function. We show that the SDPMLP is a combinatorial optimization problem with factorial complexity, and propose two hybrid exhaustive search and probabilistic neural network (PNN), and simulated annealing (SA) and PNN procedures to solve it. For small size SDPMLP, exhaustive search PNN works well and provides an (all) optimal solution(s). However, for large size SDPMLP, the use of exhaustive search PNN approach is not pragmatic and only the SA–PNN allows us to solve the SDPMLP in a practical time limit. We compare the performance of our hybrid approaches with traditional classification algorithms and find that our hybrid approaches perform better than traditional classification algorithms. |
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