C4.5: programs for machine learning
C4.5: programs for machine learning
Test Driven Development: By Example
Test Driven Development: By Example
Generating Test Data for Branch Coverage
ASE '00 Proceedings of the 15th IEEE international conference on Automated software engineering
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Generating Tests from Counterexamples
Proceedings of the 26th International Conference on Software Engineering
Test input generation with java PathFinder
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
DART: directed automated random testing
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
CUTE: a concolic unit testing engine for C
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
From daikon to agitator: lessons and challenges in building a commercial tool for developer testing
Proceedings of the 2006 international symposium on Software testing and analysis
Compositional dynamic test generation
Proceedings of the 34th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Feedback-Directed Random Test Generation
ICSE '07 Proceedings of the 29th international conference on Software Engineering
IEEE Transactions on Software Engineering
Evacon: a framework for integrating evolutionary and concolic testing for object-oriented programs
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
Symstra: a framework for generating object-oriented unit tests using symbolic execution
TACAS'05 Proceedings of the 11th international conference on Tools and Algorithms for the Construction and Analysis of Systems
On test repair using symbolic execution
Proceedings of the 19th international symposium on Software testing and analysis
Puzzle-based automatic testing: bringing humans into the loop by solving puzzles
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
Component survivability at runtime for mission-critical distributed systems
The Journal of Supercomputing
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Automatic white-box test generation is a challenging problem. Many existing tools rely on complex code analyses and heuristics. As a result, structural features of an input program may impact tool effectiveness in ways that tool users and designers may not expect or understand. We develop a technique that uses structural program metrics to predict the test coverage achieved by three automatic test generation tools. We use coverage and structural metrics extracted from 11 software projects to train several decision tree classifiers. Our experiments show that these classifiers can predict high or low coverage with success rates of 82% to 94%.