A static analyzer for finding dynamic programming errors
Software—Practice & Experience
Dynamically Discovering Likely Program Invariants to Support Program Evolution
IEEE Transactions on Software Engineering - Special issue on 1999 international conference on software engineering
Finding failures by cluster analysis of execution profiles
ICSE '01 Proceedings of the 23rd International Conference on Software Engineering
Extended static checking for Java
PLDI '02 Proceedings of the ACM SIGPLAN 2002 Conference on Programming language design and implementation
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Feature Engineering for Text Classification
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Ordering Fault-Prone Software Modules
Software Quality Control
Finding Latent Code Errors via Machine Learning over Program Executions
Proceedings of the 26th International Conference on Software Engineering
Mining Version Histories to Guide Software Changes
Proceedings of the 26th International Conference on Software Engineering
Active learning for automatic classification of software behavior
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
OOPSLA '04 Companion to the 19th annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications
Chianti: a tool for change impact analysis of java programs
OOPSLA '04 Proceedings of the 19th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
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
Applying classification techniques to remotely-collected program execution data
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
Facilitating software evolution research with kenyon
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
Lightweight bug localization with AMPLE
Proceedings of the sixth international symposium on Automated analysis-driven debugging
The Top Ten List: Dynamic Fault Prediction
ICSM '05 Proceedings of the 21st IEEE International Conference on Software Maintenance
Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction
IEEE Transactions on Software Engineering
Jimpa: An Eclipse Plug-in for Impact Analysis
CSMR '06 Proceedings of the Conference on Software Maintenance and Reengineering
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)
Classifying Software Changes: Clean or Buggy?
IEEE Transactions on Software Engineering
Fault-prone module detection using large-scale text features based on spam filtering
Empirical Software Engineering
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We present a tool that predicts whether the software under development inside an IDE has a bug. An IDE plugin performs this prediction, using the Change Classification technique to classify source code changes as buggy or clean during the editing session. Change Classification uses Support Vector Machines (SVM), a machine learning classifier algorithm, to classify changes to projects mined from their configuration management repository. This technique, besides being language independent and relatively accurate, can (a) classify a change immediately upon its completion and (b) use features extracted solely from the change delta (added, deleted) and the source code to predict buggy changes. Thus, integrating change classification within an IDE can predict potential bugs in the software as the developer edits the source code, ideally reducing the amount of time spent on fixing bugs later. To this end, we have developed a Change Classification plugin for Eclipse based on client-server architecture, described in this paper.