Evaluating predictive quality models derived from software measures: lessons learned
Journal of Systems and Software
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Predicting Fault Incidence Using Software Change History
IEEE Transactions on Software Engineering
Comparing Software Prediction Techniques Using Simulation
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
An Enhanced Neural Network Technique for Software Risk Analysis
IEEE Transactions on Software Engineering
Identifying Reasons for Software Changes Using Historic Databases
ICSM '00 Proceedings of the International Conference on Software Maintenance (ICSM'00)
Populating a Release History Database from Version Control and Bug Tracking Systems
ICSM '03 Proceedings of the International Conference on Software Maintenance
Finding Latent Code Errors via Machine Learning over Program Executions
Proceedings of the 26th International Conference on Software Engineering
Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
Automatic Mining of Source Code Repositories to Improve Bug Finding Techniques
IEEE Transactions on Software Engineering
MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
The Top Ten List: Dynamic Fault Prediction
ICSM '05 Proceedings of the 21st IEEE International Conference on Software Maintenance
Building Defect Prediction Models in Practice
IEEE Software
Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction
IEEE Transactions on Software Engineering
Mining metrics to predict component failures
Proceedings of the 28th international conference on Software engineering
Bug Classification Using Program Slicing Metrics
SCAM '06 Proceedings of the Sixth IEEE International Workshop on Source Code Analysis and Manipulation
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
Classifying Software Changes: Clean or Buggy?
IEEE Transactions on Software Engineering
Analysing Bug Prediction Capabilities of Static Code Metrics in Open Source Software
IWSM/Metrikon/Mensura '08 Proceedings of the International Conferences on Software Process and Product Measurement
EQ-mine: predicting short-term defects for software evolution
FASE'07 Proceedings of the 10th international conference on Fundamental approaches to software engineering
Software metrics in static program analysis
ICFEM'10 Proceedings of the 12th international conference on Formal engineering methods and software engineering
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Reducing the number of bugs is a crucial issue during software development and maintenance. Software process and product metrics are good indicators of software complexity. These metrics have been used to build bug predictor models to help developers maintain the quality of software. In this paper we empirically evaluate the use of hunk metrics as predictor of bugs. We present a technique for bug prediction that works at smallest units of code change called hunks. We build bug prediction models using random forests, which is an efficient machine learning classifier. Hunk metrics are used to train the classifier and each hunk metric is evaluated for its bug prediction capabilities. Our classifier can classify individual hunks as buggy or bug-free with 86 % accuracy, 83 % buggy hunk precision and 77% buggy hunk recall. We find that history based and change level hunk metrics are better predictors of bugs than code level hunk metrics.