Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
10-2007
Abstract
Specification mining is a dynamic analysis process aimed at automatically inferring suggested specifications of a program from its execution traces. We describe a method, a framework, and a tool, for mining inter-object scenario-based specifications in the form of a UML2-compliant variant of Damm and Harel's Live Sequence Charts (LSC), which extends the classical partial order semantics of sequence diagrams with temporal liveness and symbolic class level lifelines, in order to generate compact and expressive specifications. Moreover, we use previous research work and tools developed for LSC to visualize, analyze, manipulate, test, and thus evaluate the scenario-based specifications we mine. Our mining framework is supported by statistically sound metrics. Its effectiveness and the usefulness of the mined scenarios are further improved by an array of extensions to the basic mining algorithm, which include various user-guided filters and abstraction mechanisms. We demonstrate and evaluate our work using a case study.
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
OOPSLA '07: Companion to the 22nd ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications, Montreal, Canada, October 21-25
First Page
777
Last Page
778
ISBN
9781595938657
Identifier
10.1145/1297846.1297883
Publisher
ACM
City or Country
New York
Citation
LO, David; MAOZ, Shahar; and KHOO, Siau-Cheng.
Mining Modal Scenarios from Execution Traces. (2007). OOPSLA '07: Companion to the 22nd ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications, Montreal, Canada, October 21-25. 777-778.
Available at: https://ink.library.smu.edu.sg/sis_research/944
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/1297846.1297883