research-article

Deviance from perfection is a better criterion than closeness to evil when identifying risky code

Authors:

Marouane Kessentini,

Stéphane Vaucher,

Houari SahraouiAuthors Info & Claims

ASE '10: Proceedings of the 25th IEEE/ACM International Conference on Automated Software Engineering

Pages 113 - 122

https://doi.org/10.1145/1858996.1859015

Published: 20 September 2010 Publication History

Abstract

We propose an approach for the automatic detection of potential design defects in code. The detection is based on the notion that the more code deviates from good practices, the more likely it is bad. Taking inspiration from artificial immune systems, we generated a set of detectors that characterize different ways that a code can diverge from good practices. We then used these detectors to measure how far code in assessed systems deviates from normality.

We evaluated our approach by finding potential defects in two open-source systems (Xerces-J and Gantt). We used the library JHotDraw as the code base representing good design/programming practices. In both systems, we found that 90% of the riskiest classes were defects, a precision far superiour to state of the art rule-based approaches.

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Cited By

Kalhor SKeyvanpour MSalajegheh A(2024)A systematic review of refactoring opportunities by software antipattern detectionAutomated Software Engineering10.1007/s10515-024-00443-y31:2Online publication date: 15-May-2024
https://doi.org/10.1007/s10515-024-00443-y
Abid CGaaloul KKessentini MAlizadeh V(2022)What Refactoring Topics Do Developers Discuss? A Large Scale Empirical Study Using Stack OverflowIEEE Access10.1109/ACCESS.2021.314003610(56362-56374)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2021.3140036
Durán FGogolla MGuerra ELara JSahraoui HZschaler S(2021)Exploiting Results of Model-Based Analysis ToolsComposing Model-Based Analysis Tools10.1007/978-3-030-81915-6_7(129-158)Online publication date: 18-Jul-2021
https://doi.org/10.1007/978-3-030-81915-6_7
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Index Terms

Deviance from perfection is a better criterion than closeness to evil when identifying risky code
1. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Software reverse engineering

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cover image ACM Conferences

ASE '10: Proceedings of the 25th IEEE/ACM International Conference on Automated Software Engineering

September 2010

534 pages

ISBN:9781450301169

DOI:10.1145/1858996

General Chair:
Charles Pecheur
Université catholique de Louvain, Belgium
,
Program Chairs:
Jamie Andrews
University of Western Ontario, Canada
,
Elisabetta Di Nitto
Politecnico di Milano, Italy

Copyright © 2010 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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Published: 20 September 2010

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ASE10: IEEE/ACM International Conference on Automated Software Engineering

September 20 - 24, 2010

Antwerp, Belgium

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Overall Acceptance Rate 82 of 337 submissions, 24%

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Cited By

Kalhor SKeyvanpour MSalajegheh A(2024)A systematic review of refactoring opportunities by software antipattern detectionAutomated Software Engineering10.1007/s10515-024-00443-y31:2Online publication date: 15-May-2024
https://doi.org/10.1007/s10515-024-00443-y
Abid CGaaloul KKessentini MAlizadeh V(2022)What Refactoring Topics Do Developers Discuss? A Large Scale Empirical Study Using Stack OverflowIEEE Access10.1109/ACCESS.2021.314003610(56362-56374)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2021.3140036
Durán FGogolla MGuerra ELara JSahraoui HZschaler S(2021)Exploiting Results of Model-Based Analysis ToolsComposing Model-Based Analysis Tools10.1007/978-3-030-81915-6_7(129-158)Online publication date: 18-Jul-2021
https://doi.org/10.1007/978-3-030-81915-6_7
Trindade Rda Silva Bigonha MFerreira KCavalcante EDantas FBatista T(2020)Oracles of Bad SmellsProceedings of the XXXIV Brazilian Symposium on Software Engineering10.1145/3422392.3422415(62-71)Online publication date: 21-Oct-2020
https://dl.acm.org/doi/10.1145/3422392.3422415
Catolino GPalomba FFontana FDe Lucia AZaidman AFerrucci F(2020)Improving change prediction models with code smell-related informationEmpirical Software Engineering10.1007/s10664-019-09739-025:1(49-95)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1007/s10664-019-09739-0
Palomba FZanoni MFontana FDe Lucia AOliveto R(2019)Toward a Smell-Aware Bug Prediction ModelIEEE Transactions on Software Engineering10.1109/TSE.2017.277012245:2(194-218)Online publication date: 1-Feb-2019
https://doi.org/10.1109/TSE.2017.2770122
Beranič TPodgorelec VHeričko M(2018)Towards a Reliable Identification of Deficient Code with a Combination of Software MetricsApplied Sciences10.3390/app81019028:10(1902)Online publication date: 12-Oct-2018
https://doi.org/10.3390/app8101902
Palomba FPanichella AZaidman AOliveto RDe Lucia A(2018)The Scent of a Smell: An Extensive Comparison Between Textual and Structural SmellsIEEE Transactions on Software Engineering10.1109/TSE.2017.275217144:10(977-1000)Online publication date: 1-Oct-2018
https://doi.org/10.1109/TSE.2017.2752171
Simao Monteiro SFernandes Lima ACristina Venturini Fde Oliveira W(2018)Continuous Improvement of Systems in Maintenance Using a Proactive Quality Management2018 11th International Conference on the Quality of Information and Communications Technology (QUATIC)10.1109/QUATIC.2018.00017(47-55)Online publication date: Sep-2018
https://doi.org/10.1109/QUATIC.2018.00017
Palomba FBavota GPenta MFasano FOliveto RLucia A(2018)On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigationEmpirical Software Engineering10.1007/s10664-017-9535-z23:3(1188-1221)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s10664-017-9535-z
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