Solving the Class Responsibility Assignment Problem in Object-Oriented Analysis with Multi-Objective Genetic Algorithms

Bowman, M. Hamish E.; Briand, Lionel C.; Labiche, Yvan

doi:10.1109/tse.2010.70

Cited by 100 publications

(79 citation statements)

References 17 publications

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“…This approach is described in more detail elsewhere [48] and was first proposed as the 'best' way to handle multiple objectives for all SBSE problems by Harman in 2007 [36]. Since then, there has been a rapid uptake of Pareto optimal SBSE to requirements [27,31,84,90,113], planning [5,98], design [17,88,95], coding [9,99], testing [33,35,47,76,90,96,107], and refactoring [52].…”

Section: Multiple Objectivesmentioning

confidence: 99%

Search Based Software Engineering: Techniques, Taxonomy, Tutorial

Harman

McMinn

Souza

et al. 2012

Empirical Software Engineering and Verification

224

257

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Abstract. The aim of Search Based Software Engineering (SBSE) research is to move software engineering problems from human-based search to machine-based search, using a variety of techniques from the metaheuristic search, operations research and evolutionary computation paradigms. The idea is to exploit humans' creativity and machines' tenacity and reliability, rather than requiring humans to perform the more tedious, error prone and thereby costly aspects of the engineering process. SBSE can also provide insights and decision support. This tutorial will present the reader with a step-by-step guide to the application of SBSE techniques to Software Engineering. It assumes neither previous knowledge nor experience with Search Based Optimisation. The intention is that the tutorial will cover sufficient material to allow the reader to become productive in successfully applying search based optimisation to a chosen Software Engineering problem of interest. IntroductionSearch Based Software Engineering (SBSE) is the name given to a body of work in which Search Based Optimisation is applied to Software Engineering. This approach to Software Engineering has proved to be very successful and generic. It has been a subfield of software engineering for ten years [45], the past five of which have been characterised by an explosion of interest and activity [48]. New application areas within Software Engineering continue to emerge and a body of empirical evidence has now accrued that demonstrates that the search based approach is definitely here to stay. SBSE seeks to reformulate Software Engineering problems as 'search problems' [45,48]. This is not to be confused with textual or hypertextual searching. Rather, for Search Based Software Engineering, a search problem is one in which optimal or near optimal solutions are sought in a search space of candidate solutions, guided by a fitness function that distinguishes between better and worse solutions. The term SBSE was coined by Harman and Jones [45] in 2001, which was the first paper to advocate Search Based Optimisation as a general approach to Software Engineering, though there were other authors who had previously applied search based optimisation to aspects of Software Engineering.SBSE has been applied to many fields within the general area of Software Engineering, some of which are already sufficiently mature to warrant their own surveys. For example, there are surveys and overviews, covering SBSE for requirements [111], design [78] and testing [3,4,65], as well as general surveys of the whole field of SBSE [21,36,48]. This paper does not seek to duplicate these surveys, though some material is repeated from them (with permission), where it is relevant and appropriate. Rather, this paper aims to provide those unfamiliar with SBSE with a tutorial and practical guide. The aim is that, having read this paper, the reader will be able to begin to develop SBSE solutions to a chosen software engineering problem and will be able to collect and analyse the results of the applicati...

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Section: Multiple Objectivesmentioning

confidence: 99%

Search Based Software Engineering: Techniques, Taxonomy, Tutorial

Harman

McMinn

Souza

et al. 2012

Empirical Software Engineering and Verification

224

257

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“…GRASP guidelines help software designers to balance the trade-offs and give advantages for writing class methods with behaviours that affect multiple classes. In solving the CRA problem, there were current works done by Bowman and Glavas by using the Genetic algorithm [6][10] and Metaheuristic approach [11] [12]. Some of their approaches have been adapted and applied in this project in much more simplified manner.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Requirements analysis will then include an automated Natural Language Processing (NLP) [10][11] process. In turn, the requirements analysis to design phase will involve the extraction of object model elements such as classes, attributes, methods and relationships derived from the NLP [12] [13]. The inclusion of knowledge in related to domain ontologies that will help to refine the object and properties candidates.…”

Section: Proposed Aproachesmentioning

confidence: 99%

Class Responsibility Assignment (CRA) for Use Case Specification to Sequence Diagrams (UC2SD)

Jali

Greer

Hanna

2014

2014 8th. Malaysian Software Engineering Conference (MySEC)

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“…An algorithm receives a UML diagram and a fitness function as an input and gives a result diagram as an output [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Algorithms of the UML Class Diagram Analysis and Their Effectiveness: Strategy and Interface Insertion Transformations

Deryugina

Nikulchev

2017

ITM Web Conf.

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Abstract.A number of studies have investigated different means of the software refactoring. The question is whether there are any effective methods that could provide automated refactoring of the architecture on the design stage with no source code available yet. With the aim of evaluating this possibility, the algorithms of the UML class diagrams analysis were proposed. In addition, the computational experiment was conducted to evaluate the algorithms' performance depending on the initial diagram size.

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Solving the Class Responsibility Assignment Problem in Object-Oriented Analysis with Multi-Objective Genetic Algorithms

Cited by 100 publications

References 17 publications

Search Based Software Engineering: Techniques, Taxonomy, Tutorial

Search Based Software Engineering: Techniques, Taxonomy, Tutorial

Class Responsibility Assignment (CRA) for Use Case Specification to Sequence Diagrams (UC2SD)

Algorithms of the UML Class Diagram Analysis and Their Effectiveness: Strategy and Interface Insertion Transformations

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