Mining Temporal Rules from Program Execution Traces
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...
Saved in:
Main Authors: | , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2007
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/949 http://portal.acm.org/citation.cfm?id=1401838 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-1948 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-19482010-12-15T08:06:06Z Mining Temporal Rules from Program Execution Traces LO, David KHOO, Siau-Cheng LIU, Chao 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. 2007-10-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/949 info:doi/10.1145/1401827.1401838 http://portal.acm.org/citation.cfm?id=1401838 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Software Engineering |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
Software Engineering |
spellingShingle |
Software Engineering LO, David KHOO, Siau-Cheng LIU, Chao Mining Temporal Rules from Program Execution Traces |
description |
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. |
format |
text |
author |
LO, David KHOO, Siau-Cheng LIU, Chao |
author_facet |
LO, David KHOO, Siau-Cheng LIU, Chao |
author_sort |
LO, David |
title |
Mining Temporal Rules from Program Execution Traces |
title_short |
Mining Temporal Rules from Program Execution Traces |
title_full |
Mining Temporal Rules from Program Execution Traces |
title_fullStr |
Mining Temporal Rules from Program Execution Traces |
title_full_unstemmed |
Mining Temporal Rules from Program Execution Traces |
title_sort |
mining temporal rules from program execution traces |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2007 |
url |
https://ink.library.smu.edu.sg/sis_research/949 http://portal.acm.org/citation.cfm?id=1401838 |
_version_ |
1770570790287179776 |