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...

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Main Authors: LO, David, KHOO, Siau-Cheng, LIU, Chao
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Language:English
Published: Institutional Knowledge at Singapore Management University 2008
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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
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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
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