The nature of statistical learning theory
The nature of statistical learning theory
An approach to fault modeling and fault seeding using the program dependence graph
Journal of Systems and Software
Tracking down software bugs using automatic anomaly detection
Proceedings of the 24th International Conference on Software Engineering
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Finding Latent Code Errors via Machine Learning over Program Executions
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
SOBER: statistical model-based bug localization
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
Cooperative bug isolation
On-line anomaly detection of deployed software: a statistical machine learning approach
Proceedings of the 3rd international workshop on Software quality assurance
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In this paper the use of Support Vector Machines to build programs behavioral models predicting misbehaviors while executing the programs, is described. Misbehaviors can be detected more precisely if the model is built considering both the failing and passing runs. It is desirable to create a model which even after fixing the detected bugs is still applicable. To achieve this, the use of a bug seeding technique to test all different execution paths of the program in both failing and passing executions is suggested. Our experiments with a test suite, EXIF, demonstrate the applicability of our proposed approach.