Pattern matching for clone and concept detection

Pattern matching for clone and concept detection (1996)

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by K. A. Kontogiannis , R. Demori , E. Merlo , M. Galler , M. Bernstein

Venue:	Journal of Automated Software Engineering
Citations:	66 - 14 self

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Datum	Value	Source
TITLE	Pattern matching for clone and concept detection	INFERENCE
AUTHOR NAME	K. A. Kontogiannis	SVM HeaderParse 0.2
AUTHOR ADDR	McGill University School of Computer Science 3480 University St., Room 318, Montréal, Canada H3A 2A7	SVM HeaderParse 0.2
AUTHOR NAME	R. Demori	SVM HeaderParse 0.2
AUTHOR NAME	E. Merlo	SVM HeaderParse 0.2
AUTHOR NAME	M. Galler	SVM HeaderParse 0.2
AUTHOR NAME	M. Bernstein	SVM HeaderParse 0.2
AUTHOR AFFIL	Abstract.	SVM HeaderParse 0.2
ABSTRACT	A legacy system is an operational, large-scale software system that is maintained beyond its first generation of programmers. It typically represents a massive economic investment and is critical to the mission of the organization it serves. As such systems age, they become increasingly complex and brittle, and hence harder to maintain. They also become even more critical to the survival of their organization because the business rules encoded within the system are seldom documented elsewhere. Our research is concerned with developing a suite of tools to aid the maintainers of legacy systems in recovering the knowledge embodied within the system. The activities, known collectively as “program understanding”, are essential preludes for several key processes, including maintenance and design recovery for reengineering. In this paper we present three pattern-matching techniques: source code metrics, a dynamic programming algorithm for finding the best alignment between two code fragments, and a statistical matching algorithm between abstract code descriptions represented in an abstract language and actual source code. The methods are applied to detect instances of code cloning in several moderately-sized production systems including tcsh, bash, and CLIPS. The programmer’s skill and experience are essential elements of our approach. Selection of particular tools and analysis methods depends on the needs of the particular task to be accomplished. Integration of the tools provides opportunities for synergy, allowing the programmer to select the most appropriate tool for a given task.	SVM HeaderParse 0.2
YEAR	1996	INFERENCE
VENUE	Journal of Automated Software Engineering	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	108	INFERENCE
VOLUME	3	INFERENCE
CITATIONS	22 found	ParsCit 1.0