Regression modelling of software quality: empirical investigation
Journal of Electronic Materials
Prediction and control of ADA software defects
Journal of Systems and Software - An Oregon workshop on software metrics
C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Assessing Software Designs Using Capture-Recapture Methods
IEEE Transactions on Software Engineering - Special issue on software reliability
In-process improvement through defect data interpretation
IBM Systems Journal
IEEE Transactions on Software Engineering
Defect content estimations from review data
Proceedings of the 20th international conference on Software engineering
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
A Comprehensive Evaluation of Capture-Recapture Models for Estimating Software Defect Content
IEEE Transactions on Software Engineering
Growing decision trees on support-less association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Technical Note: Naive Bayes for Regression
Machine Learning
Evaluating Capture-Recapture Models with Two Inspectors
IEEE Transactions on Software Engineering
Mining web logs for prediction models in WWW caching and prefetching
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Software Metrics: A Rigorous and Practical Approach
Software Metrics: A Rigorous and Practical Approach
Empirical Software Engineering
A Case Study of Software Process Improvement During Development
IEEE Transactions on Software Engineering
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Building Hierarchical Classifiers Using Class Proximity
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Frequent-subsequence-based prediction of outer membrane proteins
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Using Machine Learning for Estimating the Defect Content After an Inspection
IEEE Transactions on Software Engineering
Mining Version Histories to Guide Software Changes
Proceedings of the 26th International Conference on Software Engineering
Predicting Source Code Changes by Mining Change History
IEEE Transactions on Software Engineering
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Integrating a model of analytical quality assurance into the V-Modell XT
Proceedings of the 3rd international workshop on Software quality assurance
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MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
Defect Data Analysis Based on Extended Association Rule Mining
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
Mining software repositories for comprehensible software fault prediction models
Journal of Systems and Software
An approach to mining bundled commodities
Knowledge-Based Systems
A hybrid faulty module prediction using association rule mining and logistic regression analysis
Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement
Analysis of Naive Bayes' assumptions on software fault data: An empirical study
Data & Knowledge Engineering
A defect prediction method for software versioning
Software Quality Control
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Journal of Software Maintenance and Evolution: Research and Practice
A pattern-based outlier detection method identifying abnormal attributes in software project data
Information and Software Technology
An effort prediction framework for software defect correction
Information and Software Technology
Correlation-based interestingness measure for video semantic concept detection
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
BUGINNINGS: identifying the origins of a bug
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Recurring bug fixes in object-oriented programs
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
Detection of recurring software vulnerabilities
Proceedings of the IEEE/ACM international conference on Automated software engineering
Prediction of defect distribution based on project characteristics for proactive project management
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A systematic literature review of software quality cost research
Journal of Systems and Software
MACs: Mining API code snippets for code reuse
Expert Systems with Applications: An International Journal
Defect prediction using social network analysis on issue repositories
Proceedings of the 2011 International Conference on Software and Systems Process
Studying the fix-time for bugs in large open source projects
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
Information Sciences: an International Journal
The bug report duplication problem: an exploratory study
Software Quality Control
Open Source Software Systems: Understanding Bug Prediction and Software Developer Roles
International Journal of Open Source Software and Processes
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Proceedings of the 2013 International Conference on Software Engineering
A study of subgroup discovery approaches for defect prediction
Information and Software Technology
Is lines of code a good measure of effort in effort-aware models?
Information and Software Technology
Software defect prediction using Bayesian networks
Empirical Software Engineering
Comparison and evaluation of source code mining tools and techniques: A qualitative approach
Intelligent Data Analysis
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Much current software defect prediction work focuses on the number of defects remaining in a software system. In this paper, we present association rule mining based methods to predict defect associations and defect correction effort. This is to help developers detect software defects and assist project managers in allocating testing resources more effectively. We applied the proposed methods to the SEL defect data consisting of more than 200 projects over more than 15 years. The results show that, for defect association prediction, the accuracy is very high and the false-negative rate is very low. Likewise, for the defect correction effort prediction, the accuracy for both defect isolation effort prediction and defect correction effort prediction are also high. We compared the defect correction effort prediction method with other types of methods—PART, C4.5, and Naïve Bayes—and show that accuracy has been improved by at least 23 percent. We also evaluated the impact of support and confidence levels on prediction accuracy, false-negative rate, false-positive rate, and the number of rules. We found that higher support and confidence levels may not result in higher prediction accuracy, and a sufficient number of rules is a precondition for high prediction accuracy.