Managing the software process
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Software process modeling and execution within virtual environments
ACM Transactions on Software Engineering and Methodology (TOSEM)
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Data mining: concepts and techniques
Data mining: concepts and techniques
Classification and evaluation of defects in a project retrospective
Journal of Systems and Software
Learning from Our Mistakes with Defect Causal Analysis
IEEE Software
Practical Applications of Statistical Process Control
IEEE Software
Implementing a Software Metrics Program at Nokia
IEEE Software
Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
CMMI Guidlines for Process Integration and Product Improvement
CMMI Guidlines for Process Integration and Product Improvement
Measurement Modeling Technology
IEEE Software
Automated support for classifying software failure reports
Proceedings of the 25th International Conference on Software Engineering
Defect Prevention through Defect Prediction: A Case Study at Infosys
ICSM '01 Proceedings of the IEEE International Conference on Software Maintenance (ICSM'01)
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Experiences with defect prevention
IBM Systems Journal
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
A process-integrated approach to defect prevention
IBM Systems Journal
Integrating in-process software defect prediction with association mining to discover defect pattern
Information and Software Technology
Towards adopting ODC in automation application development projects
Proceedings of the 5th India Software Engineering Conference
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In addition to degrading the quality of software products, software defects also require additional efforts in rewriting software and jeopardize the success of software projects. Software defects should be prevented to reduce the variance of projects and increase the stability of the software process. Factors causing defects vary according to the different attributes of a project, including the experience of the developers, the product complexity, the development tools and the schedule. The most significant challenge for a project manager is to identify actions that may incur defects before the action is performed. Actions performed in different projects may yield different results, which are hard to predict in advance. To alleviate this problem, this study proposes an Action-Based Defect Prevention (ABDP) approach, which applies the classification and Feature Subset Selection (FSS) technologies to project data during execution. Accurately predicting actions that cause many defects by mining records of performed actions is a challenging task due to the rarity of such actions. To address this problem, the under-sampling is applied to the data set to increase the precision of predictions for subsequence actions. To demonstrate the efficiency of this approach, it is applied to a business project, revealing that under-sampling with FSS successfully predicts the problematic actions during project execution. The main advantage utilizing ABDP is that the actions likely to produce defects can be predicted prior to their execution. The detected actions not only provide the information to avoid possible defects, but also facilitate the software process improvement.