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On the use of time series and search based software engineering for refactoring recommendation

Authors:

Marouane Kessentini,

William Grosky,

Haythem MeddebAuthors Info & Claims

MEDES '15: Proceedings of the 7th International Conference on Management of computational and collective intElligence in Digital EcoSystems

Pages 35 - 42

https://doi.org/10.1145/2857218.2857224

Published: 25 October 2015 Publication History

Abstract

To improve the quality of software systems, one of the widely used techniques is refactoring, defined as the process of improving the design of an existing system by changing its internal structure without altering the external behavior. The majority of existing refactoring works do not consider the impact of recommended refactorings on the quality of future releases of a system. In this paper, we propose to combine the use of search-based software engineering with time series to recommend good refactoring strategies in order to manage technical debt. We used a multi-objective algorithm to generate refactoring solutions that maximize the correction of important quality issues and minimize the effort. For these two fitness functions, we adapted time series forecasting to estimate the impact of the generated refactorings solution on future next releases of the system by predicting the evolution of the remaining code smells in the system, after refactoring, using different quality metrics. We evaluated our approach on one industrial project and a benchmark of 4 open source systems. The results confirm the efficiency of our technique to provide better refactoring management comparing to several existing refactoring techniques.

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Cited By

Albuquerque DGuimarães ETonin GRodríguezs PPerkusich MAlmeida HPerkusich AChagas F(2023)Managing Technical Debt Using Intelligent Techniques - A Systematic Mapping StudyIEEE Transactions on Software Engineering10.1109/TSE.2022.321476449:4(2202-2220)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TSE.2022.3214764
Saarimaki NMoreschini SLomio FPenaloza RLenarduzzi V(2022)Towards a Robust Approach to Analyze Time-Dependent Data in Software Engineering2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER53432.2022.00015(36-40)Online publication date: Mar-2022
https://doi.org/10.1109/SANER53432.2022.00015
Mumtaz MAhmad NUsman Ashraf MAlghamdi ABahaddad AAlmarhabi K(2022)Iteration Causes, Impact, and Timing in Software Development Lifecycle: An SLRIEEE Access10.1109/ACCESS.2022.318270310(65355-65375)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3182703
Show More Cited By

Index Terms

On the use of time series and search based software engineering for refactoring recommendation
1. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language features
        Abstract data types
        Control structures
        Polymorphism

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cover image ACM Other conferences

MEDES '15: Proceedings of the 7th International Conference on Management of computational and collective intElligence in Digital EcoSystems

October 2015

271 pages

ISBN:9781450334808

DOI:10.1145/2857218

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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The French Chapter of ACM Special Interest Group on Applied Computing
IFSP: Federal Institute of São Paulo

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Association for Computing Machinery

New York, NY, United States

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Published: 25 October 2015

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MEDES '15

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IFSP

MEDES '15: The 7th International Conference on Management of computational and collective IntElligence in Digital EcoSystems

October 25 - 29, 2015

Caraguatatuba, Brazil

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MEDES '15 Paper Acceptance Rate 13 of 64 submissions, 20%;

Overall Acceptance Rate 267 of 682 submissions, 39%

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Cited By

Albuquerque DGuimarães ETonin GRodríguezs PPerkusich MAlmeida HPerkusich AChagas F(2023)Managing Technical Debt Using Intelligent Techniques - A Systematic Mapping StudyIEEE Transactions on Software Engineering10.1109/TSE.2022.321476449:4(2202-2220)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TSE.2022.3214764
Saarimaki NMoreschini SLomio FPenaloza RLenarduzzi V(2022)Towards a Robust Approach to Analyze Time-Dependent Data in Software Engineering2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER53432.2022.00015(36-40)Online publication date: Mar-2022
https://doi.org/10.1109/SANER53432.2022.00015
Mumtaz MAhmad NUsman Ashraf MAlghamdi ABahaddad AAlmarhabi K(2022)Iteration Causes, Impact, and Timing in Software Development Lifecycle: An SLRIEEE Access10.1109/ACCESS.2022.318270310(65355-65375)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3182703
Mariani TKessentini MVergilio S(2022)Generation of refactoring algorithms by grammatical evolutionEmpirical Software Engineering10.1007/s10664-022-10151-427:5Online publication date: 30-May-2022
https://doi.org/10.1007/s10664-022-10151-4
Ozturk Kiyak E(2020)Data Mining and Machine Learning for Software EngineeringData Mining - Methods, Applications and Systems [Working Title]10.5772/intechopen.91448Online publication date: 5-Mar-2020
https://doi.org/10.5772/intechopen.91448
Kaur SAwasthi LSangal A(2020)A Brief Review on Multi-objective Software Refactoring and a New Method for Its RecommendationArchives of Computational Methods in Engineering10.1007/s11831-020-09491-5Online publication date: 17-Sep-2020
https://doi.org/10.1007/s11831-020-09491-5
Rafique MAlam KIqbal U(2019)Multi-Objective Optimization Techniques for Software Refactoring: A Systematic Literature Review2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)10.1109/MACS48846.2019.9024773(1-7)Online publication date: Dec-2019
https://doi.org/10.1109/MACS48846.2019.9024773
Razani ZKeyvanpour M(2019)SBSR Solution Evaluation: Methods and Challenges Classification2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI)10.1109/KBEI.2019.8734937(181-188)Online publication date: Feb-2019
https://doi.org/10.1109/KBEI.2019.8734937
Kessentini MOuni AGrundy JHalfond WBuhnova BAutili MMuccini H(2017)Detecting Android smells using multi-objective genetic programmingProceedings of the 4th International Conference on Mobile Software Engineering and Systems10.1109/MOBILESoft.2017.29(122-132)Online publication date: 20-May-2017
https://dl.acm.org/doi/10.1109/MOBILESoft.2017.29

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