Automatic detection of bad smells in code: An experimental assessment.

Fontana, Francesca Arcelli; Braione, Pietro; Zanoni, Marco

doi:10.5381/jot.2012.11.2.a5

Cited by 86 publications

(37 citation statements)

References 23 publications

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“…The lower average agreements are once again in pairs with JDeodorant. JDeodorant detects God Method using slicing techniques (Fontana et al 2012), while inFusion and JSpIRIT use the detection strategy of Marinescu (Lanza and Marinescu 2006), and PMD uses the metric LOC. The usage of different techniques explains the lower agreement between the other tools with JDeodorant.…”

Section: God Methodsmentioning

confidence: 99%

“…Therefore, the high agreement between these tools was expected. On the other hand, JDeodorant detects Feature Envy by searching for refactoring opportunities (Fontana et al 2012). The different detection techniques lead to a lower agreement between JDeodorant and the other two tools.…”

Section: Feature Envymentioning

confidence: 99%

“…In fact, the evaluation of the effectiveness of tools for detecting code smells presents some problems (Fontana et al 2012). For instance, the ambiguity and sometimes vagueness of code smell definitions lead to different interpretations for each code smell.…”

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confidence: 99%

“…indicates an awareness of the software engineering community about the importance of controlling the structural quality of features under development (Fontana et al 2012). On the other hand, it brings a new challenge on how to assess and compare tools and to select the most efficient tool in specific development contexts.…”

mentioning

confidence: 99%

“…In fact, some preliminary studies (Mäntylä 2005) (Moha et al 2010) (Murphy-Hill and Black 2010) tried to address some of these problems only for small systems and few code smells. Fontana et al (2012), for instance, investigated six code smells in one software system, named GanttProject. Their study showed that a more effective evaluation of code smell detection techniques requires a greater understanding of the system as well as its code smells.…”

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confidence: 99%

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On the evaluation of code smells and detection tools

Paiva

Damasceno

Figueiredo

et al. 2017

J Softw Eng Res Dev

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Code smells refer to any symptom in the source code of a program that possibly indicates a deeper problem, hindering software maintenance and evolution. Detection of code smells is challenging for developers and their informal definition leads to the implementation of multiple detection techniques and tools. This paper evaluates and compares four code smell detection tools, namely inFusion, JDeodorant, PMD, and JSpIRIT. These tools were applied to different versions of the same software systems, namely MobileMedia and Health Watcher, to calculate the accuracy and agreement of code smell detection tools. We calculated the accuracy of each tool in the detection of three code smells: God Class, God Method, and Feature Envy. Agreement was calculated among tools and between pairs of tools. One of our main findings is that the evaluated tools present different levels of accuracy in different contexts. For MobileMedia, for instance, the average recall varies from 0 to 58% and the average precision from 0 to 100%, while for Health Watcher the variations are 0 to 100% and 0 to 85%, respectively. Regarding the agreement, we found that the overall agreement between tools varies from 83 to 98% among all tools and from 67 to 100% between pairs of tools. We also conducted a secondary study of the evolution of code smells in both target systems and found that, in general, code smells are present from the moment of creation of a class or method in 74.4% of the cases of MobileMedia and 87.5% of Health Watcher.

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Section: God Methodsmentioning

confidence: 99%

Section: Feature Envymentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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On the evaluation of code smells and detection tools

Paiva

Damasceno

Figueiredo

et al. 2017

J Softw Eng Res Dev

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Behind the scenes: On the relationship between developer experience and refactoring

AlOmar

Peruma

Mkaouer

et al. 2021

J Software Evolu Process

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Refactoring is widely recognized as one of the efficient techniques to manage technical debt and maintain a healthy software project through enforcing best design practices, or coping with design defects. Previous refactoring surveys have shown that code refactoring activities are mainly executed by developers who have sufficient knowledge of the system's design, and disposing of leadership roles in their development teams. However, these surveys were mainly limited to specific projects and companies. In this paper, we explore the generalizability of the previous results by analyzing 800 open‐source projects. We mine their refactoring activities, and we identify their corresponding contributors. Then, we associate an experience score to each contributor in order to test various hypotheses related to whether developers with higher scores tend to (1) perform a higher number of refactoring operations, (2) exhibit different motivations behind their refactoring, and (3) better document their refactoring activity. We found that (1) although refactoring is not restricted to a subset of developers, those with higher contribution scores tend to perform more refactorings than others; (2) although there is no correlation between experience and motivation behind refactoring, top contributed developers are found to perform a wider variety of refactoring operations, regardless of their complexity; and (3) top contributed developer tend to document less their refactoring activity. Our qualitative analysis of three randomly sampled projects shows that the developers who are responsible for the majority of refactoring activities are typically in advanced positions in their development teams, demonstrating their extensive knowledge of the design of the systems they contribute to.

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Code smells in pull requests: An exploratory study

Azeem,

Shafiq,

Mashkoor

et al. 2023

Softw Pract Exp

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The quality of a pull request is the primary factor integrators consider for its acceptance or rejection. Code smells indicate sub‐optimal design or implementation choices in the source code that often lead to a fault‐prone outcome, threatening the quality of pull requests. This study explores code smells in 21k pull requests from 25 popular Java projects. We find that both accepted (37%) and rejected (44%) pull requests have code smells, affected mainly by god classes and long methods. Besides, we observe that smelly pull requests are more complex and challenging to understand as they have significantly large sizes, long latency times, more discussion and review comments, and are submitted by contributors with less experience. Our results show that features used in previous studies for pull request acceptance prediction could be potentially employed to predict smell in incoming pull requests. We propose a dynamic approach to predict the presence of such code smells in the newly added pull requests. We evaluate our approach on a dataset of 25 Java projects extracted from GitHub. We further conduct a benchmark study to compare the performance of eight machine learning classifiers. Results of the benchmark study show that XGBoost is the best‐performing classifier for smell prediction.

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Automatic detection of bad smells in code: An experimental assessment.

Cited by 86 publications

References 23 publications

On the evaluation of code smells and detection tools

On the evaluation of code smells and detection tools

Behind the scenes: On the relationship between developer experience and refactoring

Code smells in pull requests: An exploratory study

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