Integrating in-process software defect prediction with association mining to discover defect pattern |
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| Authors: | Ching-Pao Chang Chih-Ping Chu Yu-Fang Yeh |
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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 Technology, Beijing Institute of Technology, Beijing 100081, China;2. Beijing Key Laboratory of Software Security Engineering Technology, School of Software, Beijing Institute of Technology, Beijing 100081, China;3. Information Security Center, Beijing University of Posts and Telecommunications, Beijing 100876, China;4. China Information Technology Security Evaluation Center, Beijing 100085, China;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;4. Computer School, Beijing Information Science and Technology University, Beijing, China;1. School of Computer Science, Wuhan University, China;2. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China;3. College of Computer Science, Nankai University, China;4. Key Laboratory of Network Assessment Technology, Institute of Information Engineering, Chinese Academy of Sciences, China;5. Faculty of Information and Technology, Macau University of Science and Technology, Macao, China;6. Department of Computer Science, City University of Hong Kong, Hong Kong, China;1. School of Computer Science, Wuhan University, Wuhan, China;2. Department of Computing, The Hong Kong Polytechnic University, Hong Kong;3. Department of Computer Science, Western Michigan University, Michigan, USA;4. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China;5. College of Computer Science and Technology, Harbin Engineering University, China;6. Key Laboratory of Network Assessment Technology, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China. |
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| Abstract: | ![]()
Rather than detecting defects at an early stage to reduce their impact, defect prevention means that defects are prevented from occurring in advance. Causal analysis is a common approach to discover the causes of defects and take corrective actions. However, selecting defects to analyze among large amounts of reported defects is time consuming, and requires significant effort. To address this problem, this study proposes a defect prediction approach where the reported defects and performed actions are utilized to discover the patterns of actions which are likely to cause defects. The approach proposed in this study is adapted from the Action-Based Defect Prediction (ABDP), an approach uses the classification with decision tree technique to build a prediction model, and performs association rule mining on the records of actions and defects. An action is defined as a basic operation used to perform a software project, while a defect is defined as software flaws and can arise at any stage of the software process. The association rule mining finds the maximum rule set with specific minimum support and confidence and thus the discovered knowledge can be utilized to interpret the prediction models and software process behaviors. The discovered patterns then can be applied to predict the defects generated by the subsequent actions and take necessary corrective actions to avoid defects.The proposed defect prediction approach applies association rule mining to discover defect patterns, and multi-interval discretization to handle the continuous attributes of actions. The proposed approach is applied to a business project, giving excellent prediction results and revealing the efficiency of the proposed approach. The main benefit of using this approach is that the discovered defect patterns can be used to evaluate subsequent actions for in-process projects, and reduce variance of the reported data resulting from different projects. Additionally, the discovered patterns can be used in causal analysis to identify the causes of defects for software process improvement. |
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