Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2008
Abstract
Specification mining is a process of extracting specifications, often from program execution traces. These specifications can in turn be used to aid program understanding, monitoring and verification. There are a number of dynamic-analysis-based specification mining tools in the literature, however none so far extract past time temporal expressions in the form of rules stating: whenever a series of events occurs, previously another series of events has happened. Rules of this format are commonly found in practice and useful for various purposes. Most rule-based specification mining tools only mine future-time temporal expression. Many past-time temporal rules like whenever a resource is used, it was allocated before are asymmetric as the other direction does not holds. Hence, there is a need to mine past-time temporal rules. In this paper, we describe an approach to mine significant rules of the above format occurring above a certain statistical thresholds from program execution traces. The approach start from a set of traces, each being a sequence of events (i.e., method invocations) and resulting in a set of significant rules obeying minimum thresholds of support and confidence. A rule compaction mechanism is employed to reduce the number of reported rules significantly. Experiments on traces of JBoss Application Server shows the utility of our approach in inferring interesting past-time temporal rules.
Discipline
Software Engineering
Research Areas
Software Systems
Publication
Proceedings of the 6th International Workshop on Dynamic Analysis (WODA)
Identifier
10.1145/1401827.1401838
Publisher
ACM
Citation
LO, David; KHOO, Siau-Cheng; and LIU, Chao.
Mining Past-Time Temporal Rules from Execution Traces. (2008). Proceedings of the 6th International Workshop on Dynamic Analysis (WODA).
Available at: https://ink.library.smu.edu.sg/sis_research/417
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/10.1145/1401827.1401838