My short paper entitled got accepted to . The paper describes some of the research I did at Agitar last summer.
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%.
I posted the and other supporting information at the paper's .
Unfortunately, Agitar despite having a mature product, many talented engineers, and a strong involvement in research. As I understand it, the company had difficulty getting developers to adopt their software, and the recent economic troubles were too much for it. Without Agitar to support a trip, it is unlikely that I will be able to go to Italy to present the paper in person. I am nevertheless very pleased with the work and am glad that it will get wider exposure.