@inproceedings{638163ad3d344d6392c3116dab6dea59,
title = "Classifying test suite effectiveness via model inference and ROBBDs",
abstract = "Deciding whether a given test suite is effective enough is certainly a challenging task. Focusing on a software program{\textquoteright}s functionality, we propose in this paper a new method that leverages Boolean functions as abstract reasoning format. That is, we use machine learning in order to infer a special binary decision diagram from the considered test suite and extract a total variable order, if possible. Intuitively, if an ROBDD derived from the Boolean functions representing the program under test{\textquoteright}s specification actually coincides with that of the test suite (using the same variable order), we conclude that the test suite is effective enough. That is, any program that passes such a test suite should clearly show the desired input-output behavior. In our paper, we provide the corresponding algorithms of our approach and their respective proofs. Our first experimental results illustrate our approach{\textquoteright}s practicality and viability.",
keywords = "BDD, Machine learning, ROBDD, Software testing, Testing",
author = "Hermann Felbinger and Ingo Pill and Franz Wotawa",
year = "2016",
doi = "10.1007/978-3-319-41135-4_5",
language = "English",
isbn = "9783319411347",
volume = "9762",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag Italia",
pages = "76--93",
booktitle = "Tests and Proofs - 10th International Conference, TAP 2016 Held as Part of STAF 2016, Proceedings",
address = "Italy",
note = "10th International Conference on Tests & Proofs : TAP 2016 ; Conference date: 05-07-2016 Through 07-07-2016",
}