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Empirical Software Engineering

Erschienen in:

01.11.2022

Code smells detection via modern code review: a study of the OpenStack and Qt communities

verfasst von: Xiaofeng Han, Amjed Tahir, Peng Liang, Steve Counsell, Kelly Blincoe, Bing Li, Yajing Luo

Erschienen in: Empirical Software Engineering | Ausgabe 6/2022

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Abstract

Code review plays an important role in software quality control. A typical review process involves a careful check of a piece of code in an attempt to detect and locate defects and other quality issues/violations. One type of issue that may impact the quality of software is code smells - i.e., bad coding practices that may lead to defects or maintenance issues. Yet, little is known about the extent to which code smells are identified during modern code review. To investigate the concept behind code smells identified in modern code review and what actions reviewers suggest and developers take in response to the identified smells, we conducted an empirical study of code smells in code reviews by analysing reviews from four, large open source projects from the OpenStack (Nova and Neutron) and Qt (Qt Base and Qt Creator) communities. We manually checked a total of 25,415 code review comments obtained by keywords search and random selection; this resulted in the identification of 1,539 smell-related reviews which then allowed the study of the causes of code smells, actions taken against identified smells, time taken to fix identified smells and reasons why developers ignored fixing identified smells. Our analysis found that 1) code smells were not commonly identified in code reviews, 2) smells were usually caused by violation of coding conventions, 3) reviewers usually provided constructive feedback, including fixing (refactoring) recommendations to help developers remove smells, 4) developers generally followed those recommendations and actioned the changes, 5) once identified by reviewers, it usually takes developers less than one week to fix the smells and 6) the main reason why developers chose to ignore the identified smells is that it is not worth fixing the smell. Our results suggest the following: 1) developers should closely follow coding conventions in their projects to avoid introducing code smells, 2) review-based detection of code smells is perceived to be a trustworthy approach by developers, mainly because reviews are context-sensitive (as reviewers are more aware of the context of the code given that they are part of the project’s development team) and 3) program context needs to be fully considered in order to make a decision of whether to fix the identified code smell immediately.

Vorheriger Artikel Pull request latency explained: an empirical overview

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https://pmd.github.io

https://www.sonarqube.org

https://www.designite-tools.com

https://www.gerritcodereview.com

https://www.openstack.org

https://wiki.openstack.org/wiki/Nova

https://wiki.openstack.org/wiki/Neutron

https://www.qt.io/

https://github.com/qt/qtbase

https://www.qt.io/product/development-tools

As of March 2022: https://www.openhub.net/p/openstack

https://www.gerritcodereview.com

Implemented using the NLTK package: https://www.nltk.org

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Abbes M, Khomh F, Gueheneuc Y G, Antoniol G (2011) An empirical study of the impact of two antipatterns blob and spaghetti code on program comprehension. In: Proceedings of the 15th European conference on software maintenance and reengineering (CSMR). IEEE, pp 181–190

Baker R A Jr (1997) Code reviews enhance software quality. In: Proceedings of the 19th international conference on software engineering (ICSE). ACM, pp 570–571

Bavota G, Qusef A, Oliveto R, De Lucia A, Binkley D (2015) Are test smells really harmful? An empirical study. Empir Softw Eng 20(4):1052–1094CrossRef

Bosu A, Carver J C, Hafiz M, Hilley P, Janni D (2014) Identifying the characteristics of vulnerable code changes: an empirical study. In: Proceedings of the 22nd ACM SIGSOFT international symposium on foundations of software engineering (FSE). ACM, pp 257–268

Braun V, Clarke V (2006) Using thematic analysis in psychology. Qual Res Psychol 3(2):77–101CrossRef

Cassee N, Vasilescu B, Serebrenik A (2020) The silent helper: the impact of continuous integration on code reviews. In: Proceedings of the 27th IEEE international conference on software analysis, evolution and reengineering (SANER). IEEE, pp 423–434

Chouchen M, Ouni A, Kula R G, Wang D, Thongtanunam P, Mkaouer M W, Matsumoto K (2021) Anti-patterns in modern code review: symptoms and prevalence. In: Proceedings of the 28th IEEE international conference on software analysis, evolution and reengineering (SANER). IEEE, pp 531–535

Coelho F, Tsantalis N, Massoni T, Alves E L G (2021) An empirical study on refactoring-inducing pull requests. In: Proceedings of the 15th ACM/IEEE international symposium on empirical software engineering and measurement (ESEM). ACM, pp 1–12

Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46CrossRef

Dou W, Cheung S C, Wei J (2014) Is spreadsheet ambiguity harmful? Detecting and repairing spreadsheet smells due to ambiguous computation. In: Proceedings of the 36th international conference on software engineering (ICSE). ACM, pp 848–858

Fontana F A, Dietrich J, Walter B, Yamashita A, Zanoni M (2016) Anti-pattern and code smell false positives: preliminary conceptualisation and classification. In: Proceedings of the 23rd international conference on software analysis, evolution, and reengineering (SANER). IEEE, pp 609–613

Fowler M (1999) Refactoring: improving the design of existing code. Addison-Wesley

Garcia J, Popescu D, Edwards G, Medvidovic N (2009) Identifying architectural bad smells. In: Proceedings of the 13th European conference on software maintenance and reengineering (CSMR). IEEE, pp 255–258

Hall T, Zhang M, Bowes D, Sun Y (2014) Some code smells have a significant but small effect on faults. ACM Trans Softw Eng Methodol 23 (4):1–39CrossRef

Han X, Tahir A, Liang P, Counsell S, Luo Y (2021) Understanding code smell detection via code review: a study of the openstack community. In: Proceedings of the 29th IEEE/ACM international conference on program comprehension (ICPC). IEEE, pp 323–334

Han X, Tahir A, Liang P, Counsell S, Blincoe K, Li B, Luo Y (2022) Replication package for the paper: code smells detection via modern code review: a study of the OpenStack and Qt communities. https://doi.org/10.5281/zenodo.5588454

Hirao T, McIntosh S, Ihara A, Matsumoto K (2020) Code reviews with divergent review scores: an empirical study of the openstack and qt communities. IEEE Trans Softw Eng. https://doi.org/10.1109/TSE.2020.2977907https://doi.org/10.1109/TSE.2020.2977907

Israel G D (1992) Determining sample size. Fact Sheet PEOD-6, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida, Florida

Kaplan E L, Meier P (1958) Nonparametric estimation from incomplete observations. J Am Stat Assoc 53(282):457–481MathSciNetCrossRef

Kemerer C F, Paulk M C (2009) The impact of design and code reviews on software quality: an empirical study based on psp data. IEEE Trans Softw Eng 35(4):534–550CrossRef

Khomh F, Di Penta M, Gueheneuc Y G (2009) An exploratory study of the impact of code smells on software change-proneness. In: Proceedings of the 16th working conference on reverse engineering (WCRE). IEEE, pp 75–84

Kononenko O, Baysal O, Guerrouj L, Cao Y, Godfrey M W (2015) Investigating code review quality: do people and participation matter?. In: Proceedings of the 31th IEEE international conference on software maintenance and evolution (ICSME). IEEE, pp 111–120

Kruskal W H, Wallis W A (1952) Use of ranks in one-criterion variance analysis. J Am Stat Assoc 47(260):583–621CrossRef

Li Z, Avgeriou P, Liang P (2015) A systematic mapping study on technical debt and its management. J Syst Softw 101:193–220CrossRef

Mann H B, Whitney D R (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 50–60

Martini A, Fontana F A, Biaggi A, Roveda R (2018) Identifying and prioritizing architectural debt through architectural smells: a case study in a large software company. In: Proceedings of the 12th European conference on software architecture (ECSA). Springer, pp 320–335

McConnell S (2004) Code complete. Pearson Education

McIntosh S, Kamei Y, Adams B, Hassan A E (2014) The impact of code review coverage and code review participation on software quality: a case study of the Qt, VTK, and ITK projects. In: Proceedings of the 11th working conference on mining software repositories (MSR). ACM, pp 192–201

McIntosh S, Kamei Y, Adams B, Hassan A E (2016) An empirical study of the impact of modern code review practices on software quality. Empir Softw Eng 21(5):2146–2189CrossRef

Meneely A, Tejeda A C R, Spates B, Trudeau S, Neuberger D, Whitlock K, Ketant C, Davis K (2014) An empirical investigation of socio-technical code review metrics and security vulnerabilities. In: Proceedings of the 6th international workshop on social software engineering (SSE). ACM, pp 37–44

Moha N, Gueheneuc Y G, Duchien L, Le Meur A F (2009) Decor: a method for the specification and detection of code and design smells. IEEE Trans Softw Eng 36(1):20–36CrossRef

Morales R, McIntosh S, Khomh F (2015) Do code review practices impact design quality? A case study of the Qt, VTK, and ITK projects. In: Proceedings of the 22nd IEEE international conference on software analysis, evolution, and reengineering (SANER). IEEE, pp 171–180

Nanthaamornphong A, Chaisutanon A (2016) Empirical evaluation of code smells in open source projects: preliminary results. In: Proceedings of the 1st international workshop on software refactoring (IWoR). ACM, pp 5–8

Palomba F, Bavota G, Di Penta M, Oliveto R, De Lucia A (2014) Do they really smell bad? A study on developers’ perception of bad code smells. In: Proceedings of the 30th international conference on software maintenance and evolution (ICSME). IEEE, pp 101–110

Palomba F, Bavota G, Di Penta M, Oliveto R, Poshyvanyk D, De Lucia A (2015) Mining version histories for detecting code smells. IEEE Trans Softw Eng 41(5):462–489CrossRef

Palomba F, Bavota G, Di Penta M, Fasano F, Oliveto R, De Lucia A (2018) On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation. Empir Softw Eng 23(3):1188–1221CrossRef

Panichella S, Zaugg N (2020) An empirical investigation of relevant changes and automation needs in modern code review. Empir Softw Eng 25(6):4833–4872CrossRef

Pascarella L, Spadini D, Palomba F, Bacchelli A (2020) On the effect of code review on code smells. In: Proceedings of the 27th IEEE international conference on software analysis, evolution and reengineering (SANER). IEEE

Pecorelli F, Palomba F, Khomh F, De Lucia A (2020) Developer-driven code smell prioritization. In: Proceedings of the 17th working conference on mining software repositories (MSR). ACM, pp 220–231

Porter MF (2001) Snowball: a language for stemming algorithms. Open Source Initiative OSI

Potdar A, Shihab E (2014) An exploratory study on self-admitted technical debt. In: Proceedings of the 30th IEEE international conference on software maintenance and evolution (ICSME). IEEE, pp 91–100

Sae-Lim N, Hayashi S, Saeki M (2018) Context-based approach to prioritize code smells for prefactoring. J Softw: Evol Process 30(6):1–24

Sharma T, Spinellis D (2018) A survey on software smells. J Syst Softw 138:158–173CrossRef

Sjøberg DI, Yamashita A, Anda B C, Mockus A, Dybå T (2013) Quantifying the effect of code smells on maintenance effort. IEEE Trans Softw Eng 39(8):1144–1156CrossRef

Soh Z, Yamashita A, Khomh F, Guéhéneuc Y G (2016) Do code smells impact the effort of different maintenance programming activities?. In: Proceedings of the 23rd international conference on software analysis, evolution, and reengineering (SANER). IEEE, pp 393–402

Tahir A, Counsell S, MacDonell S G (2016) An empirical study into the relationship between class features and test smells. In: Proceedings of the 23rd Asia-Pacific software engineering conference (APSEC). IEEE, pp 137–144

Tahir A, Yamashita A, Licorish S, Dietrich J, Counsell S (2018) Can you tell me if it smells? A study on how developers discuss code smells and anti-patterns in stack overflow. In: Proceedings of the 22nd international conference on evaluation and assessment in software engineering (EASE). ACM, pp 68–78

Tahir A, Dietrich J, Counsell S, Licorish S, Yamashita A (2020) A large scale study on how developers discuss code smells and anti-pattern in stack exchange sites. Inf Softw Technol 125:106333CrossRef

Taibi D, Janes A, Lenarduzzi V (2017) How developers perceive smells in source code: a replicated study. Inf Softw Technol 92:223–235CrossRef

Tan P N, Steinbach M, Kumar V (2016) Introduction to data mining. Pearson Education India

Tsantalis N, Chatzigeorgiou A (2009) Identification of move method refactoring opportunities. IEEE Trans Softw Eng 35(3):347–367CrossRef

Tufano M, Palomba F, Bavota G, Oliveto R, Di Penta M, De Lucia A, Poshyvanyk D (2015) When and why your code starts to smell bad. In: Proceedings of the IEEE/ACM 37th IEEE international conference on software engineering (ICSE), vol 1. IEEE, pp 403–414

Uchôa A, Barbosa C, Coutinho D, Oizumi W, Assunção W K G, Vergilio S R, Pereira J A, Oliveira A, Garcia A (2021) Predicting design impactful changes in modern code review: a large-scale empirical study. In: Proceedings of the 18th IEEE/ACM international conference on mining software repositories (MSR). IEEE, pp 471–482

Wessel M, Serebrenik A, Wiese I, Steinmacher I, Gerosa M A (2020) Effects of adopting code review bots on pull requests to oss projects. In: Proceedings of the 36th IEEE international conference on software maintenance and evolution (ICSME). IEEE, pp 1–11

Wohlin C, Runeson P, Höst M, Ohlsson M C, Regnell B, Wesslén A (2012) Experimentation in software engineering. Springer, BerlinCrossRef

Yamashita A, Moonen L (2013) Do developers care about code smells? An exploratory survey. In: Proceedings of the 20th working conference on reverse engineering (WCRE). IEEE, pp 242–251

Zanaty F E, Hirao T, McIntosh S, Ihara A, Matsumoto K (2018) An empirical study of design discussions in code review. In: Proceedings of the 12th ACM/IEEE international symposium on empirical software engineering and measurement (ESEM). ACM, pp 1–10

Zhang M, Hall T, Baddoo N (2011) Code bad smells: a review of current knowledge. J Softw Maint Evol: Res Pract 23(3):179–202CrossRef

Titel: Code smells detection via modern code review: a study of the OpenStack and Qt communities
verfasst von: Xiaofeng Han
Amjed Tahir
Peng Liang
Steve Counsell
Kelly Blincoe
Bing Li
Yajing Luo
Publikationsdatum: 01.11.2022
Verlag: Springer US
Erschienen in: Empirical Software Engineering / Ausgabe 6/2022
Print ISSN: 1382-3256
Elektronische ISSN: 1573-7616
DOI: https://doi.org/10.1007/s10664-022-10178-7

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