Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
11-2013
Abstract
Specification mining extracts candidate specification from existing systems, to be used for downstream tasks such as testing and verification. Specifically, we are interested in the extraction of behavior models from execution traces. In this paper we introduce mining of branching-time scenarios in the form of existential, conditional Live Sequence Charts, using a statistical data-mining algorithm. We show the power of branching scenarios to reveal alternative scenario-based behaviors, which could not be mined by previous approaches. The work contrasts and complements previous works on mining linear-time scenarios. An implementation and evaluation over execution trace sets recorded from several real-world applications shows the unique contribution of mining branching-time scenarios to the state-of-the-art in specification mining.
Keywords
data mining, formal verification, program testing, statistical analysis
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE) Proceedings: 11-15 November 2013, Silicon Valley, CA
First Page
443
Last Page
453
ISBN
9781479902156
Identifier
10.1109/ASE.2013.6693102
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
FAHLAND, Dirk; LO, David; and MAOZ, Shahar.
Mining Branching-Time Scenarios. (2013). 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE) Proceedings: 11-15 November 2013, Silicon Valley, CA. 443-453.
Available at: https://ink.library.smu.edu.sg/sis_research/2029
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://dx.doi.org/10.1109/ASE.2013.6693102