Japan's software factories: a challenge to U.S. management
Japan's software factories: a challenge to U.S. management
Orthogonal Defect Classification-A Concept for In-Process Measurements
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
C4.5: programs for machine learning
C4.5: programs for machine learning
Selected papers of the sixth annual Oregon workshop on Software metrics
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Knowledge discovery in databases terminology
Advances in knowledge discovery and data mining
Data mining solutions: methods and tools for solving real-world problems
Data mining solutions: methods and tools for solving real-world problems
Predicting Fault Incidence Using Software Change History
IEEE Transactions on Software Engineering
Data mining: concepts and techniques
Data mining: concepts and techniques
Principles of data mining
Evaluating Capture-Recapture Models with Two Inspectors
IEEE Transactions on Software Engineering
Modelling fault-proneness statistically over a sequence of releases: a case study
Journal of Software Maintenance: Research and Practice
Software Engineering: A Practitioner's Approach
Software Engineering: A Practitioner's Approach
PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning
Data Mining and Knowledge Discovery
Classification and evaluation of defects in a project retrospective
Journal of Systems and Software
Scoring the Data Using Association Rules
Applied Intelligence
Mining Optimized Association Rules with Categorical and Numeric Attributes
IEEE Transactions on Knowledge and Data Engineering
A Modified Chi2 Algorithm for Discretization
IEEE Transactions on Knowledge and Data Engineering
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
Multi-interval Discretization Methods for Decision Tree Learning
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
BNCOD 14 Proceedings of the 14th British National Conference on Databases: Advances in Databases
A Dynamic Method for Discretization of Continuous Attributes
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Measurement Modeling Technology
IEEE Software
Defect Prevention through Defect Prediction: A Case Study at Infosys
ICSM '01 Proceedings of the IEEE International Conference on Software Maintenance (ICSM'01)
Operational anomalies as a cause of safety-critical requirements evolution
Journal of Systems and Software
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Mining Informative Rule Set for Prediction
Journal of Intelligent Information Systems
Khiops: A Statistical Discretization Method of Continuous Attributes
Machine Learning
Analyzing Software Measurement Data with Clustering Techniques
IEEE Intelligent Systems
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Experiences with defect prevention
IBM Systems Journal
Application of neural networks for software quality prediction using object-oriented metrics
Journal of Systems and Software
Software faults: a quantifiable definition
Advances in Engineering Software
Predicting software defects in varying development lifecycles using Bayesian nets
Information and Software Technology
Defect prevention in software processes: An action-based approach
Journal of Systems and Software
The class imbalance problem: A systematic study
Intelligent Data Analysis
Regression via Classification applied on software defect estimation
Expert Systems with Applications: An International Journal
Handling continuous-valued attributes in incremental first-order rules learning
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Review: Software fault prediction: A literature review and current trends
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Deciphering the story of software development through frequent pattern mining
Proceedings of the 2013 International Conference on Software Engineering
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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.