Mining Quantified Temporal Rules: Formalism, Algorithms, and Evaluation

Libraries usually impose constraints on how clients should use them. Often these constraints are not well-documented. In this paper, we address the problem of recovering such constraints automatically, a problem referred to as specification mining. Given some client programs that use a given library...

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Main Authors: LO, David, RAMALINGAM, Ganesan, RANGANATH, Venkatesh Prasad, VASWANI, Kapil
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Language:English
Published: Institutional Knowledge at Singapore Management University 2012
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Online Access:https://ink.library.smu.edu.sg/sis_research/1345
https://ink.library.smu.edu.sg/context/sis_research/article/2344/viewcontent/Mining_quantified_temporal_rules_Formalism__algorithms__and_evaluation.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-23442018-02-07T08:34:54Z Mining Quantified Temporal Rules: Formalism, Algorithms, and Evaluation LO, David RAMALINGAM, Ganesan RANGANATH, Venkatesh Prasad VASWANI, Kapil Libraries usually impose constraints on how clients should use them. Often these constraints are not well-documented. In this paper, we address the problem of recovering such constraints automatically, a problem referred to as specification mining. Given some client programs that use a given library, we identify constraints on the library usage that are (almost) satisfied by the given set of clients.The class of rules we target for mining combines simple binary temporal operators with state predicates (composed of equality constraints) and quantification. This is a simple yet expressive subclass of temporal properties (LTL formulae) that allows us to capture many common API usage rules. We focus on recovering rules from execution traces and apply classical data mining concepts to be robust against bugs (API usage rule violations) in clients. We present new algorithms for mining rules from execution traces. We show how a propositional rule mining algorithm can be generalized to treat quantification and state predicates in a unified way. Our approach enables the miner to be complete (i.e. , mine all rules within the targeted class that are satisfied by the given traces) while avoiding an exponential blowup.We have implemented these algorithms and used them to mine API usage rules for several Windows APIs. Our experiments show the efficiency and effectiveness of our approach. 2012-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1345 info:doi/10.1016/j.scico.2010.10.003 https://ink.library.smu.edu.sg/context/sis_research/article/2344/viewcontent/Mining_quantified_temporal_rules_Formalism__algorithms__and_evaluation.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 Specification mining Temporal rules Quantification Dynamic analysis Reverse engineering Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Specification mining
Temporal rules
Quantification
Dynamic analysis
Reverse engineering
Software Engineering
spellingShingle Specification mining
Temporal rules
Quantification
Dynamic analysis
Reverse engineering
Software Engineering
LO, David
RAMALINGAM, Ganesan
RANGANATH, Venkatesh Prasad
VASWANI, Kapil
Mining Quantified Temporal Rules: Formalism, Algorithms, and Evaluation
description Libraries usually impose constraints on how clients should use them. Often these constraints are not well-documented. In this paper, we address the problem of recovering such constraints automatically, a problem referred to as specification mining. Given some client programs that use a given library, we identify constraints on the library usage that are (almost) satisfied by the given set of clients.The class of rules we target for mining combines simple binary temporal operators with state predicates (composed of equality constraints) and quantification. This is a simple yet expressive subclass of temporal properties (LTL formulae) that allows us to capture many common API usage rules. We focus on recovering rules from execution traces and apply classical data mining concepts to be robust against bugs (API usage rule violations) in clients. We present new algorithms for mining rules from execution traces. We show how a propositional rule mining algorithm can be generalized to treat quantification and state predicates in a unified way. Our approach enables the miner to be complete (i.e. , mine all rules within the targeted class that are satisfied by the given traces) while avoiding an exponential blowup.We have implemented these algorithms and used them to mine API usage rules for several Windows APIs. Our experiments show the efficiency and effectiveness of our approach.
format text
author LO, David
RAMALINGAM, Ganesan
RANGANATH, Venkatesh Prasad
VASWANI, Kapil
author_facet LO, David
RAMALINGAM, Ganesan
RANGANATH, Venkatesh Prasad
VASWANI, Kapil
author_sort LO, David
title Mining Quantified Temporal Rules: Formalism, Algorithms, and Evaluation
title_short Mining Quantified Temporal Rules: Formalism, Algorithms, and Evaluation
title_full Mining Quantified Temporal Rules: Formalism, Algorithms, and Evaluation
title_fullStr Mining Quantified Temporal Rules: Formalism, Algorithms, and Evaluation
title_full_unstemmed Mining Quantified Temporal Rules: Formalism, Algorithms, and Evaluation
title_sort mining quantified temporal rules: formalism, algorithms, and evaluation
publisher Institutional Knowledge at Singapore Management University
publishDate 2012
url https://ink.library.smu.edu.sg/sis_research/1345
https://ink.library.smu.edu.sg/context/sis_research/article/2344/viewcontent/Mining_quantified_temporal_rules_Formalism__algorithms__and_evaluation.pdf
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