research-article

Smells are sensitive to developers!: on the efficiency of (un)guided customized detection

Authors:

Alessandro Garcia,

Baldoino Fonseca,

Evandro CostaAuthors Info & Claims

ICPC '17: Proceedings of the 25th International Conference on Program Comprehension

Pages 110 - 120

https://doi.org/10.1109/ICPC.2017.32

Published: 20 May 2017 Publication History

Abstract

Code smells indicate poor implementation choices that may hinder program comprehension and maintenance. Their informal definition allows developers to follow different heuristics to detect smells in their projects. Machine learning has been used to customize smell detection according to the developer's perception. However, such customization is not guided (i.e. constrained) to consider alternative heuristics used by developers when detecting smells. As a result, their customization might not be efficient, requiring a considerable effort to reach high effectiveness. In fact, there is no empirical knowledge yet about the efficiency of such unguided approaches for supporting developer-sensitive smell detection. This paper presents Histrategy, a guided customization technique to improve the efficiency on smell detection. Histrategy considers a limited set of detection strategies, produced from different detection heuristics, as input of a customization process. The output of the customization process consists of a detection strategy tailored to each developer. The technique was evaluated in an experimental study with 48 developers and four types of code smells. The results showed that Histrategy is able to outperform six widely adopted machine learning algorithms - used in unguided approaches - both in effectiveness and efficiency. It was also confirmed that most developers benefit from using alternative heuristics to: (i) build their tailored detection strategies, and (ii) achieve efficient smell detection.

References

[1]

M. Abbes, F. Khomh, Y.-G. Gueheneuc, and G. Antoniol, "An empirical study of the impact of two antipatterns, blob and spaghetti code, on program comprehension," in 15th European Conference on Software Maintenance and Reengineering (CSMR). IEEE, 2011, pp. 181--190.

Digital Library

[2]

M. Fowler, Refactoring: Improving the Design of Existing Code. Boston, MA, USA: Addison-Wesley, 1999.

Digital Library

[3]

W. Oizumi, A. Garcia, L. da Silva Sousa, B. Cafeo, and Y. Zhao, "Code anomalies flock together: Exploring code anomaly agglomerations for locating design problems," in Proceedings of the 38th International Conference on Software Engineering, ser. ICSE '16. New York, NY, USA: ACM, 2016, pp. 440--451. {Online}. Available

Digital Library

[4]

F. Khomh, M. D. Penta, Y.-G. Guéhéneuc, and G. Antoniol, "An exploratory study of the impact of antipatterns on class change- and fault-proneness," Empirical Software Engineering, vol. 17, no. 3, pp. 243--275, Aug. 2011.

Digital Library

[5]

M. V. Mäntylä, "An experiment on subjective evolvability evaluation of object-oriented software: explaining factors and interrater agreement," in International Symposium on Empirical Software Engineering, 2005.

[6]

M. V. Mäntylä and C. Lassenius, Subjective evaluation of software evolvability using code smells: An empirical study. Springer, May 2006, vol. 11.

[7]

J. Schumacher, N. Zazworka, F. Shull, C. Seaman, and M. Shaw, "Building empirical support for automated code smell detection," Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement - ESEM '10, p. 1, 2010.

Digital Library

[8]

J. A. M. Santos, M. G. de Mendonça, and C. V. A. Silva, "An exploratory study to investigate the impact of conceptualization in god class detection," in Proceedings of the 17th International Conference on Evaluation and Assessment in Software Engineering, ser. EASE '13. New York, NY, USA: ACM, 2013, pp. 48--59. {Online}. Available

Digital Library

[9]

R. Marinescu, "Detection Strategies: Metrics-Based Rules for Detecting Design Flaws," in Proceedings of the 20th IEEE International Conference on Software Maintenance, ser. ICSM '04. Washington, DC, USA: IEEE Computer Society, 2004, pp. 350--359. {Online}. Available: http://dl.acm.org/citation.cfm?id=1018431.1021443

Digital Library

[10]

M. Lanza, R. Marinescu, and S. Ducasse, Object-Oriented Metrics in Practice. Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2005.

Digital Library

[11]

M. Munro, "Product Metrics for Automatic Identification of "Bad Smell" Design Problems in Java Source-Code," 11th IEEE International Software Metrics Symposium (METRICS'05), pp. 15--15, 2005.

Digital Library

[12]

F. Khomh, S. Vaucher, Y.-G. Guéhéneuc, and H. Sahraoui, "BDTEX: A GQM-based Bayesian Approach for the Detection of Antipatterns," Journal of Systems and Software, vol. 84, no. 4, pp. 559--572, Apr. 2011. {Online}. Available

Digital Library

[13]

N. Moha, Y.-G. Gueheneuc, L. Duchien, and A. F. Le Meur, "DECOR: A Method for the Specification and Detection of Code and Design Smells," IEEE Transactions on Software Engineering, vol. 36, no. 1, pp. 20--36, Jan. 2010.

Digital Library

[14]

F. A. Fontana, E. Mariani, A. Mornioli, R. Sormani, and A. Tonello, "An Experience Report on Using Code Smells Detection Tools," 2011 IEEE Fourth International Conference on Software Testing, Verification and Validation Workshops, pp. 450--457, Mar. 2011. {Online}. Available: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5954446

Digital Library

[15]

F. Palomba, G. Bavota, M. Di Penta, R. Oliveto, D. Poshyvanyk, and A. De Lucia, "Mining Version Histories for Detecting Code Smells," IEEE Transactions on Software Engineering, vol. 41, no. 5, 2015.

[16]

F. A. Fontana, V. Ferme, and S. Spinelli, "Investigating the impact of code smells debt on quality code evaluation," 2012 Third International Workshop on Managing Technical Debt (MTD), pp. 15--22, Jun. 2012. {Online}. Available: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6225993

Digital Library

[17]

R. Marinescu, "Detecting Design Flaws via Metrics in Object-Oriented Systems A Metrics-Based Approach for Problem Detection," International Conference and Technology of Object-Oriented Languages and Systems (TOOLS), pp. 173--182, 2001.

Digital Library

[18]

M. Ferreira, E. Barbosa, I. Macia, R. Arcoverde, and A. Garcia, "Detecting architecturally-relevant code anomalies: a case study of effectiveness and effort," in Proceedings of the 29th Annual ACM Symposium on Applied Computing. ACM, 2014, pp. 1158--1163.

Digital Library

[19]

"PMD," http://pmd.sourceforge.net/, March, 2017.

[20]

"Checkstyle," http://checkstyle.sourceforge.net/, March, 2017.

[21]

"inFusion," https://www.intooitus.com/products/infusion, March, 2017.

[22]

"JDeodorant," http://www.jdeodorant.com/, March, 2017.

[23]

F. Arcelli Fontana, M. V. Mäntylä, M. Zanoni, and A. Marino, "Comparing and experimenting machine learning techniques for code smell detection," Empirical Software Engineering, Jun. 2015.

Digital Library

[24]

A. Maiga, N. Ali, N. Bhattacharya, A. Sabane, Y.-G. Gueheneuc, and E. Aimeur, "SMURF: A SVM-based Incremental Anti-pattern Detection Approach," 2012 19th Working Conference on Reverse Engineering, pp. 466--475, Oct. 2012.

Digital Library

[25]

F. A. Fontana, P. Braione, and M. Zanoni, "Automatic detection of bad smells in code: An experimental assessment," Journal of Object Technology, vol. 11, no. 2, 2012.

[26]

F. Khomh, S. Vaucher, Y. G. Guéhéneuc, and H. Sahraoui, "A bayesian approach for the detection of code and design smells," in Quality Software, 2009. QSIC'09. 9th International Conference on. IEEE, 2009, pp. 305--314.

Digital Library

[27]

L. Amorim, E. Costa, N. Antunes, B. Fonseca, and M. Ribeiro, "Experience report: Evaluating the effectiveness of decision trees for detecting code smells," in IEEE 26th International Symposium on Software Reliability Engineering (ISSRE), ser. ISSRE '15. Washington, DC, USA: IEEE Computer Society, 2015, pp. 261--269.

Digital Library

[28]

T. M. Mitchell, Machine learning, ser. McGraw-Hill series in computer science. Boston (Mass.), Burr Ridge (Ill.), Dubuque (Iowa): McGraw-Hill, 1997. {Online}. Available: http://opac.inria.fr/record=b1093076

Digital Library

[29]

H. Liu, L. Yang, Z. Niu, Z. Ma, and W. Shao, "Facilitating software refactoring with appropriate resolution order of bad smells," in Proceedings of the the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on The Foundations of Software Engineering, ser. ESEC/FSE '09. New York, NY, USA: ACM, 2009, pp. 265--268. {Online}. Available

Digital Library

[30]

M. Hozano, N. Antunes, B. Fonseca, and E. Costa, "Evaluating the accuracy of machine learning algorithms on detecting code smells for different developers," in ICEIS 2017 - Proceedings of the 19th International Conference on Enterprise Information Systems, Porto, Portugal, April 26--29, 2017 (in press).

[31]

G. Rasool and Z. Arshad, "A review of code smell mining techniques," Journal of Software: Evolution and Process, pp. n/a--n/a, sep 2015. {Online}. Available:

Digital Library

[32]

N. Roperia, JSmell: A Bad Smell detection tool for Java systems. California State University, 2009.

[33]

M. Hozano, "ICPC 2017 - Replication Package," http://goo.gl/tuvxUy, March, 2017.

[34]

R. Baeza-Yates, B. Ribeiro-Neto et al., Modern information retrieval. ACM press New York, 1999, vol. 463.

Digital Library

[35]

N. Maneerat and P. Muenchaisri, "Bad-smell prediction from software design model using machine learning techniques," 2011 Eighth International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 331--336, May 2011. {Online}. Available: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5930143

[36]

C. Wohlin, P. Runeson, M. Höst, M. C. Ohlsson, B. Regnell, and A. Wesslén, Experimentation in Software Engineering: An Introduction. Norwell, MA, USA: Kluwer Academic Publishers, 2000.

Digital Library

[37]

F. Palomba, G. Bavota, M. D. Penta, R. Oliveto, and A. D. Lucia, "Do They Really Smell Bad? A Study on Developers' Perception of Bad Code Smells," 2014 IEEE International Conference on Software Maintenance and Evolution, pp. 101--110, Sep. 2014.

Digital Library

Cited By

Oliveira AOizumi WSousa LAssunção WGarcia ALucena CCedrim D(2022)Smell Patterns as Indicators of Design DegradationProceedings of the XXXVI Brazilian Symposium on Software Engineering10.1145/3555228.3555243(311-320)Online publication date: 5-Oct-2022
https://dl.acm.org/doi/10.1145/3555228.3555243
Oliveira DAssunção WSouza LOizumi WGarcia AFonseca BCavalcante EDantas FBatista T(2020)Applying Machine Learning to Customized Smell DetectionProceedings of the XXXIV Brazilian Symposium on Software Engineering10.1145/3422392.3422427(233-242)Online publication date: 21-Oct-2020
https://dl.acm.org/doi/10.1145/3422392.3422427
Oizumi WBibiano ACedrim DOliveira ASousa LGarcia AOliveira DCavalcante EDantas FBatista T(2020)Recommending Composite Refactorings for Smell RemovalProceedings of the XXXIV Brazilian Symposium on Software Engineering10.1145/3422392.3422423(72-81)Online publication date: 21-Oct-2020
https://dl.acm.org/doi/10.1145/3422392.3422423
Show More Cited By

Recommendations

Developers’ perception matters: machine learning to detect developer-sensitive smells
Abstract
Code smells are symptoms of poor design that hamper software evolution and maintenance. Hence, code smells should be detected as early as possible to avoid software quality degradation. However, the notion of whether a design and/or implementation ...
Are architectural smells independent from code smells? An empirical study
Highlights
- Case study analyzing the correlations among code smells, groups of code smells and architectural smells.
Abstract
Background. Architectural smells and code smells are symptoms of bad code or design that can cause different quality problems, such as faults, technical debt, or difficulties with maintenance and evolution. Some studies ...
Are you smelling it? Investigating how similar developers detect code smells

ContextA code smell indicates a poor implementation choice that often worsens software quality. Thus, code smell detection is an elementary technique to identify refactoring opportunities in software systems. Unfortunately, there is limited knowledge on ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ICPC '17: Proceedings of the 25th International Conference on Program Comprehension

May 2017

399 pages

ISBN:9781538605356

General Chair:
Giuseppe Scanniello
University of Basilicata, Italy
,
Program Chairs:
David Lo
Singapore Management University, Singapore
,
Alexander Serebrenik
Eindhoven University of Technology, The Netherlands

Sponsors

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

Publisher

IEEE Press

Publication History

Published: 20 May 2017

Check for updates

Qualifiers

Research-article

Conference

ICSE '17

Sponsor:

SIGSOFT
IEEE-CS
SADIO

ICSE '17: 39th International Conference on Software Engineering

May 20 - 28, 2017

Buenos Aires, Argentina

Upcoming Conference

ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
102
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Oliveira AOizumi WSousa LAssunção WGarcia ALucena CCedrim D(2022)Smell Patterns as Indicators of Design DegradationProceedings of the XXXVI Brazilian Symposium on Software Engineering10.1145/3555228.3555243(311-320)Online publication date: 5-Oct-2022
https://dl.acm.org/doi/10.1145/3555228.3555243
Oliveira DAssunção WSouza LOizumi WGarcia AFonseca BCavalcante EDantas FBatista T(2020)Applying Machine Learning to Customized Smell DetectionProceedings of the XXXIV Brazilian Symposium on Software Engineering10.1145/3422392.3422427(233-242)Online publication date: 21-Oct-2020
https://dl.acm.org/doi/10.1145/3422392.3422427
Oizumi WBibiano ACedrim DOliveira ASousa LGarcia AOliveira DCavalcante EDantas FBatista T(2020)Recommending Composite Refactorings for Smell RemovalProceedings of the XXXIV Brazilian Symposium on Software Engineering10.1145/3422392.3422423(72-81)Online publication date: 21-Oct-2020
https://dl.acm.org/doi/10.1145/3422392.3422423
Cedrim DGarcia AMongiovi MGheyi RSousa Lde Mello RFonseca BRibeiro MChávez ABodden ESchäfer WDeursen AZisman A(2017)Understanding the impact of refactoring on smells: a longitudinal study of 23 software projectsProceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering10.1145/3106237.3106259(465-475)Online publication date: 21-Aug-2017
https://dl.acm.org/doi/10.1145/3106237.3106259

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents