Dynamically Discovering Likely Program Invariants to Support Program Evolution
IEEE Transactions on Software Engineering - Special issue on 1999 international conference on software engineering
Tracking down software bugs using automatic anomaly detection
Proceedings of the 24th International Conference on Software Engineering
A Fast Automaton-Based Method for Detecting Anomalous Program Behaviors
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
Dynamically discovering likely program invariants
Dynamically discovering likely program invariants
Active learning for automatic classification of software behavior
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Perracotta: mining temporal API rules from imperfect traces
Proceedings of the 28th international conference on Software engineering
Detecting object usage anomalies
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
The Daikon system for dynamic detection of likely invariants
Science of Computer Programming
Automatic generation of software behavioral models
Proceedings of the 30th international conference on Software engineering
Automated Identification of Failure Causes in System Logs
ISSRE '08 Proceedings of the 2008 19th International Symposium on Software Reliability Engineering
Mining Software Specifications: Methodologies and Applications
Mining Software Specifications: Methodologies and Applications
Leveraging existing instrumentation to automatically infer invariant-constrained models
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Mining temporal invariants from partially ordered logs
SLAML '11 Managing Large-scale Systems via the Analysis of System Logs and the Application of Machine Learning Techniques
Dynamic Analysis for Diagnosing Integration Faults
IEEE Transactions on Software Engineering
Eclat: automatic generation and classification of test inputs
ECOOP'05 Proceedings of the 19th European conference on Object-Oriented Programming
Understanding user understanding: determining correctness of generated program invariants
Proceedings of the 2012 International Symposium on Software Testing and Analysis
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Software testing is an effective, yet expensive, method to improve software quality. Test automation, a potential way to reduce testing cost, has received enormous research attention recently, but the so-called “oracle problem” (how to decide the PASS/FAIL outcome of a test execution) is still a major obstacle to such cost reduction. We have extensively investigated state-of-the-art works that contribute to address this problem, from areas such as specification mining and model inference. In this paper, we compare three types of automated oracles: Data invariants, Temporal invariants, and Finite State Automata. More specifically, we study the training cost and the false positive rate; we evaluate also their fault detection capability. Seven medium to large, industrial application subjects and real faults have been used in our empirical investigation.