C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
A case study of open source software development: the Apache server
Proceedings of the 22nd international conference on Software engineering
Predicting Fault Incidence Using Software Change History
IEEE Transactions on Software Engineering
Future Generation Computer Systems
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
Credit Scoring and Its Applications
Credit Scoring and Its Applications
On Building Prediction Systems for Software Engineers
Empirical Software Engineering
Quantitative Analysis of Faults and Failures in a Complex Software System
IEEE Transactions on Software Engineering
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A MAX-MIN Ant System for the University Course Timetabling Problem
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Pattern Detection and Discovery
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Analogy-Based Practical Classification Rules for Software Quality Estimation
Empirical Software Engineering
Combining techniques to optimize effort predictions in software project management
Journal of Systems and Software
Classification Rule Discovery with Ant Colony Optimization
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Analyzing Software Measurement Data with Clustering Techniques
IEEE Intelligent Systems
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Predicting Source Code Changes by Mining Change History
IEEE Transactions on Software Engineering
Mining Version Histories to Guide Software Changes
IEEE Transactions on Software Engineering
Automatic Mining of Source Code Repositories to Improve Bug Finding Techniques
IEEE Transactions on Software Engineering
Using Grey Relational Analysis to Predict Software Effort with Small Data Sets
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
An Empirical Analysis of Software Productivity over Time
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
Ant-Based Clustering and Topographic Mapping
Artificial Life
Software Defect Association Mining and Defect Correction Effort Prediction
IEEE Transactions on Software Engineering
Software field failure rate prediction before software deployment
Journal of Systems and Software
Computers and Operations Research
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Protection or privacy? data mining and personal data
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Ant-based approach to the knowledge fusion problem
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Classification With Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Building acceptable classification models for financial engineering applications: thesis summary
ACM SIGKDD Explorations Newsletter
Journal of Systems and Software
Discovering classification rules for email spam filtering with an ant colony optimization algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A symbolic fault-prediction model based on multiobjective particle swarm optimization
Journal of Systems and Software
Practical development of an Eclipse-based software fault prediction tool using Naive Bayes algorithm
Expert Systems with Applications: An International Journal
Building comprehensible customer churn prediction models with advanced rule induction techniques
Expert Systems with Applications: An International Journal
Software metrics reduction for fault-proneness prediction of software modules
NPC'10 Proceedings of the 2010 IFIP international conference on Network and parallel computing
Advances in Engineering Software
Searching for rules to detect defective modules: A subgroup discovery approach
Information Sciences: an International Journal
Information Sciences: an International Journal
Performance of classification models from a user perspective
Decision Support Systems
A study of subgroup discovery approaches for defect prediction
Information and Software Technology
Data stream mining for predicting software build outcomes using source code metrics
Information and Software Technology
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Software managers are routinely confronted with software projects that contain errors or inconsistencies and exceed budget and time limits. By mining software repositories with comprehensible data mining techniques, predictive models can be induced that offer software managers the insights they need to tackle these quality and budgeting problems in an efficient way. This paper deals with the role that the Ant Colony Optimization (ACO)-based classification technique AntMiner+ can play as a comprehensible data mining technique to predict erroneous software modules. In an empirical comparison on three real-world public datasets, the rule-based models produced by AntMiner+ are shown to achieve a predictive accuracy that is competitive to that of the models induced by several other included classification techniques, such as C4.5, logistic regression and support vector machines. In addition, we will argue that the intuitiveness and comprehensibility of the AntMiner+ models can be considered superior to the latter models.