research-article

What if i had no smells?

Authors:

Davide Falessi,

Kathleen MullenAuthors Info & Claims

ESEM '17: Proceedings of the 11th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

Pages 78 - 84

https://doi.org/10.1109/ESEM.2017.14

Published: 09 November 2017 Publication History

Abstract

What would have happened if I did not have any code smell? This is an interesting question that no previous study, to the best of our knowledge, has tried to answer. In this paper, we present a method for implementing a what-if scenario analysis estimating the number of defective files in the absence of smells. Our industrial case study shows that 20% of the total defective files were likely avoidable by avoiding smells. Such estimation needs to be used with the due care though as it is based on a hypothetical history (i.e., zero number of smells and same process and product change characteristics). Specifically, the number of defective files could even increase for some types of smells. In addition, we note that in some circumstances, accepting code with smells might still be a good option for a company.

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Cited By

Falessi DAhluwalia APenta M(2022)The Impact of Dormant Defects on Defect Prediction: A Study of 19 Apache ProjectsACM Transactions on Software Engineering and Methodology10.1145/346789531:1(1-26)Online publication date: 31-Jan-2022
https://dl.acm.org/doi/10.1145/3467895
Vandehei BCosta DFalessi D(2021)Leveraging the Defects Life Cycle to Label Affected Versions and Defective ClassesACM Transactions on Software Engineering and Methodology10.1145/343392830:2(1-35)Online publication date: 10-Feb-2021
https://dl.acm.org/doi/10.1145/3433928
Lujan SPecorelli FPalomba FDe Lucia ALenarduzzi VKhomh FSalza PCatolino G(2020)A preliminary study on the adequacy of static analysis warnings with respect to code smell predictionProceedings of the 4th ACM SIGSOFT International Workshop on Machine-Learning Techniques for Software-Quality Evaluation10.1145/3416505.3423559(1-6)Online publication date: 13-Nov-2020
https://dl.acm.org/doi/10.1145/3416505.3423559
Show More Cited By

What if i had no smells?
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
2. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Software management

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Information & Contributors

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Published In

cover image ACM Conferences

ESEM '17: Proceedings of the 11th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

November 2017

481 pages

ISBN:9781509040391

General Chair:
Ayse Bener
Ryerson University, Canada
,
Program Chairs:
Burak Turhan
Brunel University, UK
,
Stefan Biffl
Technische Universität Wien, Austria

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SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS\DATC: IEEE Computer Society

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IEEE Press

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Published: 09 November 2017

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ESEM '17

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SIGSOFT
IEEE-CS\DATC

ESEM '17: ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

November 9 - 10, 2017

Ontario, Markham, Canada

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Overall Acceptance Rate 130 of 594 submissions, 22%

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Cited By

Falessi DAhluwalia APenta M(2022)The Impact of Dormant Defects on Defect Prediction: A Study of 19 Apache ProjectsACM Transactions on Software Engineering and Methodology10.1145/346789531:1(1-26)Online publication date: 31-Jan-2022
https://dl.acm.org/doi/10.1145/3467895
Vandehei BCosta DFalessi D(2021)Leveraging the Defects Life Cycle to Label Affected Versions and Defective ClassesACM Transactions on Software Engineering and Methodology10.1145/343392830:2(1-35)Online publication date: 10-Feb-2021
https://dl.acm.org/doi/10.1145/3433928
Lujan SPecorelli FPalomba FDe Lucia ALenarduzzi VKhomh FSalza PCatolino G(2020)A preliminary study on the adequacy of static analysis warnings with respect to code smell predictionProceedings of the 4th ACM SIGSOFT International Workshop on Machine-Learning Techniques for Software-Quality Evaluation10.1145/3416505.3423559(1-6)Online publication date: 13-Nov-2020
https://dl.acm.org/doi/10.1145/3416505.3423559
Baldassarre MLenarduzzi VRomano SSaarimäki N(2020)On the diffuseness of technical debt items and accuracy of remediation time when using SonarQubeInformation and Software Technology10.1016/j.infsof.2020.106377128:COnline publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1016/j.infsof.2020.106377
Lenarduzzi VMartini ATaibi DTamburri DFontana FWalter BAmpatzoglou APalomba FPerrouin GAcher MCordy MDevroey X(2019)Towards surgically-precise technical debt estimation: early results and research roadmapProceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation10.1145/3340482.3342747(37-42)Online publication date: 27-Aug-2019
https://dl.acm.org/doi/10.1145/3340482.3342747
Falessi DMoede MBaysal OMenzies T(2018)Facilitating feasibility analysis: the pilot defects prediction dataset makerProceedings of the 4th ACM SIGSOFT International Workshop on Software Analytics10.1145/3278142.3278147(15-18)Online publication date: 5-Nov-2018
https://dl.acm.org/doi/10.1145/3278142.3278147

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