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...

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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 2007
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Online Access:https://ink.library.smu.edu.sg/sis_research/949
http://portal.acm.org/citation.cfm?id=1401838
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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
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