Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
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
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Modern Information Retrieval
Recovering Traceability Links between Code and Documentation
IEEE Transactions on Software Engineering
Ordering Fault-Prone Software Modules
Software Quality Control
Identifying Reasons for Software Changes Using Historic Databases
ICSM '00 Proceedings of the International Conference on Software Maintenance (ICSM'00)
CVS Release History Data for Detecting Logical Couplings
IWPSE '03 Proceedings of the 6th International Workshop on Principles of Software Evolution
Mining Version Histories to Guide Software Changes
Proceedings of the 26th International Conference on Software Engineering
An Automatic Approach to identify Class Evolution Discontinuities
IWPSE '04 Proceedings of the Principles of Software Evolution, 7th International Workshop
Machine Learning
Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
HATARI: raising risk awareness
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
DynaMine: finding common error patterns by mining software revision histories
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction
IEEE Transactions on Software Engineering
Automatic Identification of Bug-Introducing Changes
ASE '06 Proceedings of the 21st IEEE/ACM International Conference on Automated Software Engineering
Extracting Change-patterns from CVS Repositories
WCRE '06 Proceedings of the 13th Working Conference on Reverse Engineering
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)
Predicting Faults from Cached History
ICSE '07 Proceedings of the 29th international conference on Software Engineering
The Future of Programming Environments: Integration, Synergy, and Assistance
FOSE '07 2007 Future of Software Engineering
Identifying Changed Source Code Lines from Version Repositories
ICSEW '07 Proceedings of the 29th International Conference on Software Engineering Workshops
Spam Filter Based Approach for Finding Fault-Prone Software Modules
ICSEW '07 Proceedings of the 29th International Conference on Software Engineering Workshops
An extension of fault-prone filtering using precise training and a dynamic threshold
Proceedings of the 2008 international working conference on Mining software repositories
Fault-prone module detection using large-scale text features based on spam filtering
Empirical Software Engineering
Dealing with noise in defect prediction
Proceedings of the 33rd International Conference on Software Engineering
Faster issue resolution with higher technical quality of software
Software Quality Control
EvoJava: a tool for measuring evolving software
ACSC '11 Proceedings of the Thirty-Fourth Australasian Computer Science Conference - Volume 113
A study of variability spaces in open source software
Proceedings of the 2013 International Conference on Software Engineering
Linux variability anomalies: what causes them and how do they get fixed?
Proceedings of the 10th Working Conference on Mining Software Repositories
| Hi-index | 0.00 |
A version control system, such as CVS/SVN, can provide the history of software changes performed during the evolution of a software project. Among all the changes performed there are some which cause the introduction of bugs, often resolved later with other changes. In this paper we use a technique to identify bug-introducing changes to train a model that can be used to predict if a new change may introduces or not a bug. We represent software changes as elements of a n-dimensional vector space of terms coordinates extracted from source code snapshots. The evaluation of various learning algorithms on a set of open source projects looks very promising, in particular for KNN (K-Nearest Neighbor algorithm) where a significant tradeoff between precision and recall has been obtained.