Identification of potential classes in procedural code using a genetic algorithm

Farshad Ghassemi Toosi; Goetz Botterweck; Asanka Wasala; Jim Buckley

doi:10.1145/3205651.3205720

Identification of potential classes in procedural code using a genetic algorithm

Farshad Ghassemi Toosi, Goetz Botterweck, Asanka Wasala, Jim Buckley

Department of Computer Science & Information Systems

University of Limerick

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We present a novel approach for discovering and suggesting classes/objects in legacy/procedural code, based on a genetic algorithm. Initially, a (procedures-accessing-variables) matrix is extracted from the code and converted into a square matrix. This matrix highlights the variable-relationships between procedures and is used as input to a genetic algorithm. The output of the genetic algorithm is then visually encoded using a heat-map. The developers can then (1) either manually identify objects in the presented heat-map or (2) use an automated detection algorithm that suggests objects. We compare our results with previous work.

Original language	English
Title of host publication	GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
Publisher	Association for Computing Machinery, Inc
Pages	314-315
Number of pages	2
ISBN (Electronic)	9781450357647
DOIs	https://doi.org/10.1145/3205651.3205720
Publication status	Published - 6 Jul 2018
Event	2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan Duration: 15 Jul 2018 → 19 Jul 2018

Publication series

Name	GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

Conference

Conference	2018 Genetic and Evolutionary Computation Conference, GECCO 2018
Country/Territory	Japan
City	Kyoto
Period	15/07/18 → 19/07/18

Access to Document

10.1145/3205651.3205720

Cite this

Toosi, F. G., Botterweck, G., Wasala, A., & Buckley, J. (2018). Identification of potential classes in procedural code using a genetic algorithm. In GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion (pp. 314-315). (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery, Inc. https://doi.org/10.1145/3205651.3205720

Toosi, Farshad Ghassemi ; Botterweck, Goetz ; Wasala, Asanka et al. / Identification of potential classes in procedural code using a genetic algorithm. GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, 2018. pp. 314-315 (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion).

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title = "Identification of potential classes in procedural code using a genetic algorithm",

abstract = "We present a novel approach for discovering and suggesting classes/objects in legacy/procedural code, based on a genetic algorithm. Initially, a (procedures-accessing-variables) matrix is extracted from the code and converted into a square matrix. This matrix highlights the variable-relationships between procedures and is used as input to a genetic algorithm. The output of the genetic algorithm is then visually encoded using a heat-map. The developers can then (1) either manually identify objects in the presented heat-map or (2) use an automated detection algorithm that suggests objects. We compare our results with previous work.",

author = "Toosi, {Farshad Ghassemi} and Goetz Botterweck and Asanka Wasala and Jim Buckley",

note = "Publisher Copyright: {\textcopyright} 2018 Copyright held by the owner/author(s).; 2018 Genetic and Evolutionary Computation Conference, GECCO 2018 ; Conference date: 15-07-2018 Through 19-07-2018",

year = "2018",

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doi = "10.1145/3205651.3205720",

language = "English",

series = "GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion",

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Toosi, FG, Botterweck, G, Wasala, A & Buckley, J 2018, Identification of potential classes in procedural code using a genetic algorithm. in GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, Inc, pp. 314-315, 2018 Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, 15/07/18. https://doi.org/10.1145/3205651.3205720

Identification of potential classes in procedural code using a genetic algorithm. / Toosi, Farshad Ghassemi; Botterweck, Goetz; Wasala, Asanka et al.
GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, 2018. p. 314-315 (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - We present a novel approach for discovering and suggesting classes/objects in legacy/procedural code, based on a genetic algorithm. Initially, a (procedures-accessing-variables) matrix is extracted from the code and converted into a square matrix. This matrix highlights the variable-relationships between procedures and is used as input to a genetic algorithm. The output of the genetic algorithm is then visually encoded using a heat-map. The developers can then (1) either manually identify objects in the presented heat-map or (2) use an automated detection algorithm that suggests objects. We compare our results with previous work.

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Toosi FG, Botterweck G, Wasala A, Buckley J. Identification of potential classes in procedural code using a genetic algorithm. In GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc. 2018. p. 314-315. (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion). doi: 10.1145/3205651.3205720

Identification of potential classes in procedural code using a genetic algorithm

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