The Detection of Fault-Prone Programs
IEEE Transactions on Software Engineering
A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
Predicting Fault Incidence Using Software Change History
IEEE Transactions on Software Engineering
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
Quantitative Analysis of Faults and Failures in a Complex Software System
IEEE Transactions on Software Engineering
Detection of software modules with high debug code churn in a very large legacy system
ISSRE '96 Proceedings of the The Seventh International Symposium on Software Reliability Engineering
Populating a Release History Database from Version Control and Bug Tracking Systems
ICSM '03 Proceedings of the International Conference on Software Maintenance
An Empirical Study of Software Reuse vs. Defect-Density and Stability
Proceedings of the 26th International Conference on Software Engineering
ICSM '04 Proceedings of the 20th IEEE International Conference on Software Maintenance
Use of relative code churn measures to predict system defect density
Proceedings of the 27th international conference on Software engineering
Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
Mining Version Histories to Guide Software Changes
IEEE Transactions on Software Engineering
The Top Ten List: Dynamic Fault Prediction
ICSM '05 Proceedings of the 21st IEEE International Conference on Software Maintenance
EvoLens: Lens-View Visualizations of Evolution Data
IWPSE '05 Proceedings of the Eighth International Workshop on Principles of Software Evolution
Data Mining
Improving defect prediction using temporal features and non linear models
Ninth international workshop on Principles of software evolution: in conjunction with the 6th ESEC/FSE joint meeting
Proceedings of the 30th international conference on Software engineering
A metric for software readability
ISSTA '08 Proceedings of the 2008 international symposium on Software testing and analysis
Mining Edge-Weighted Call Graphs to Localise Software Bugs
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Can developer-module networks predict failures?
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
Cross-project defect prediction: a large scale experiment on data vs. domain vs. process
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
EQ-mine: predicting short-term defects for software evolution
FASE'07 Proceedings of the 10th international conference on Fundamental approaches to software engineering
Comparing fine-grained source code changes and code churn for bug prediction
Proceedings of the 8th Working Conference on Mining Software Repositories
Time variance and defect prediction in software projects
Empirical Software Engineering
Bug prediction based on fine-grained module histories
Proceedings of the 34th International Conference on Software Engineering
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
Tool Assisted Analysis of Open Source Projects: A Multi-Faceted Challenge
International Journal of Open Source Software and Processes
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With the advent of open source software repositories the data available for defect prediction in source files increased tremendously. Although traditional statistics turned out to derive reasonable results the sheer amount of data and the problem context of defect prediction demand sophisticated analysis such as provided by current data mining and machine learning techniques.In this work we focus on defect density prediction and present an approach that applies a decision tree learner on evolution data extracted from the Mozilla open source web browser project. The evolution data includes different source code, modification, and defect measures computed from seven recent Mozilla releases. Among the modification measures we also take into account the change coupling, a measure for the number of change-dependencies between source files. The main reason for choosing decision tree learners, instead of for example neural nets, was the goal of finding underlying rules which can be easily interpreted by humans. To find these rules, we set up a number of experiments to test common hypotheses regarding defects in software entities. Our experiments showed, that a simple tree learner can produce good results with various sets of input data.