A generic approach to detect design patterns in model transformations using a string-matching algorithm

Maintaining software artifacts is a complex and time-consuming task. Like any other program, model transformations are subject to maintenance. In a maintenance process, much effort is dedicated to the comprehension of programs. To this end, several techniques are used, such as feature location and d...

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Bibliographic Details
Published in:Software and systems modeling Vol. 21; no. 3; pp. 1241 - 1269
Main Authors: Mokaddem, Chihab eddine, Sahraoui, Houari, Syriani, Eugene
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2022
Springer Nature B.V
Subjects:
Algorithms
Compilers
Computer Science
Design
Information Systems Applications (incl.Internet)
Interpreters
IT in Business
Maintenance
Programming Languages
Programming Techniques
Regular Paper
Software
Software Engineering
Software Engineering/Programming and Operating Systems
String matching
Transformations
Model-driven engineering
Pattern detection
Bit-vector
Model transformation
Design pattern
String matching
ISSN:1619-1366, 1619-1374
Online Access:Get full text
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Summary:Maintaining software artifacts is a complex and time-consuming task. Like any other program, model transformations are subject to maintenance. In a maintenance process, much effort is dedicated to the comprehension of programs. To this end, several techniques are used, such as feature location and design pattern detection. In the particular case of model transformations, detecting design patterns contributes to a better comprehension as they carry valuable information on the transformation structure. In this paper, we propose a generic approach to detect, semi-automatically, design patterns and their variations in model transformations. Our approach encodes both design patterns and transformations as strings and use a string-matching algorithm for the detection. The approach is able to detect complete and partial implementations of design patterns in transformations, which is useful to refactoring and improving model transformations.
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ISSN:1619-1366
1619-1374
DOI:10.1007/s10270-021-00936-4