Mining Patterns and Rules for Software Specification Discovery

Software specifications are often lacking, incomplete and outdated in the industry. Lack and incomplete specifications cause various software engineering problems. Studies have shown that program comprehension takes up to 45% of software development costs. One of the root causes of the high cost is...

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Main Authors: LO, David, KHOO, Siau-Cheng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2008
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Online Access:https://ink.library.smu.edu.sg/sis_research/425
https://ink.library.smu.edu.sg/context/sis_research/article/1424/viewcontent/1454234.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-14242018-04-30T03:36:42Z Mining Patterns and Rules for Software Specification Discovery LO, David KHOO, Siau-Cheng Software specifications are often lacking, incomplete and outdated in the industry. Lack and incomplete specifications cause various software engineering problems. Studies have shown that program comprehension takes up to 45% of software development costs. One of the root causes of the high cost is the lack-of documented specification. Also, outdated and incomplete specification might potentially cause bugs and compatibility issues. In this paper, we describe novel data mining techniques to mine or reverse engineer these specifications from the pool of software engineering data. A large amount of software data is available for analysis. One form of software data is program execution traces. A program trace can be viewed as a sequence of events collected when a program is run. A set of program traces in turn can be viewed as a sequence database. In this paper, we present some novel work in mining software specifications by employing novel pattern mining and rule mining techniques. Performance studies show the scalability of our technique. Case studies on traces of a real industrial application show the utility of our technique in recovering program specifications from execution traces. 2008-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/425 info:doi/10.14778/1454159.1454234 https://ink.library.smu.edu.sg/context/sis_research/article/1424/viewcontent/1454234.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
Mining Patterns and Rules for Software Specification Discovery
description Software specifications are often lacking, incomplete and outdated in the industry. Lack and incomplete specifications cause various software engineering problems. Studies have shown that program comprehension takes up to 45% of software development costs. One of the root causes of the high cost is the lack-of documented specification. Also, outdated and incomplete specification might potentially cause bugs and compatibility issues. In this paper, we describe novel data mining techniques to mine or reverse engineer these specifications from the pool of software engineering data. A large amount of software data is available for analysis. One form of software data is program execution traces. A program trace can be viewed as a sequence of events collected when a program is run. A set of program traces in turn can be viewed as a sequence database. In this paper, we present some novel work in mining software specifications by employing novel pattern mining and rule mining techniques. Performance studies show the scalability of our technique. Case studies on traces of a real industrial application show the utility of our technique in recovering program specifications from execution traces.
format text
author LO, David
KHOO, Siau-Cheng
author_facet LO, David
KHOO, Siau-Cheng
author_sort LO, David
title Mining Patterns and Rules for Software Specification Discovery
title_short Mining Patterns and Rules for Software Specification Discovery
title_full Mining Patterns and Rules for Software Specification Discovery
title_fullStr Mining Patterns and Rules for Software Specification Discovery
title_full_unstemmed Mining Patterns and Rules for Software Specification Discovery
title_sort mining patterns and rules for software specification discovery
publisher Institutional Knowledge at Singapore Management University
publishDate 2008
url https://ink.library.smu.edu.sg/sis_research/425
https://ink.library.smu.edu.sg/context/sis_research/article/1424/viewcontent/1454234.pdf
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