Mining Past-Time Temporal Rules from 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
2008
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/417 https://ink.library.smu.edu.sg/context/sis_research/article/1416/viewcontent/woda08.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-1416 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-14162011-11-02T09:47:12Z Mining Past-Time Temporal Rules from 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. 2008-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/417 info:doi/10.1145/1401827.1401838 https://ink.library.smu.edu.sg/context/sis_research/article/1416/viewcontent/woda08.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ 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 Past-Time Temporal Rules from 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 Past-Time Temporal Rules from Execution Traces |
title_short |
Mining Past-Time Temporal Rules from Execution Traces |
title_full |
Mining Past-Time Temporal Rules from Execution Traces |
title_fullStr |
Mining Past-Time Temporal Rules from Execution Traces |
title_full_unstemmed |
Mining Past-Time Temporal Rules from Execution Traces |
title_sort |
mining past-time temporal rules from execution traces |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2008 |
url |
https://ink.library.smu.edu.sg/sis_research/417 https://ink.library.smu.edu.sg/context/sis_research/article/1416/viewcontent/woda08.pdf |
_version_ |
1770570416462495744 |