Code Smell Detection: Towards a Machine Learning-Based Approach

Fontana, Francesca Arcelli; Zanoni, Marco; Marino, Alessandro; Mäntylä, Martti

doi:10.1109/icsm.2013.56

Cited by 131 publications

(52 citation statements)

References 15 publications

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“…Most of them are based on the analysis of the structural properties of source code (e.g., method calls) and on the combination of structural metrics [51], [56], [65], [67], [69], [72], [88], [95], [99], [103], while in recent years the use of alternative sources of information (i.e., historical and textual analysis) have been explored [74], [75], together with methodologies based on machine learning [3], [33] and search-based algorithms [17], [46], [47], [87].…”

Section: Textual and Structural Code Smell Detectionmentioning

confidence: 99%

The Scent of a Smell: An Extensive Comparison Between Textual and Structural Smells

Palomba

Panichella

Zaidman

et al. 2018

IIEEE Trans. Software Eng.

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Abstract-Code smells are symptoms of poor design or implementation choices that have a negative effect on several aspects of software maintenance and evolution, such as program comprehension or change-and fault-proneness. This is why researchers have spent a lot of effort on devising methods that help developers to automatically detect them in source code. Almost all the techniques presented in literature are based on the analysis of structural properties extracted from source code, although alternative sources of information (e.g., textual analysis) for code smell detection have also been recently investigated. Nevertheless, some studies have indicated that code smells detected by existing tools based on the analysis of structural properties are generally ignored (and thus not refactored) by the developers. In this paper, we aim at understanding whether code smells detected using textual analysis are perceived and refactored by developers in the same or different way than code smells detected through structural analysis. To this aim, we set up two different experiments. We have first carried out a software repository mining study to analyze how developers act on textually or structurally detected code smells. Subsequently, we have conducted a user study with industrial developers and quality experts in order to qualitatively analyze how they perceive code smells identified using the two different sources of information. Results indicate that textually detected code smells are easier to identify and for this reason they are considered easier to refactor with respect to code smells detected using structural properties. On the other hand, the latter are often perceived as more severe, but more difficult to exactly identify and remove.

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Section: Textual and Structural Code Smell Detectionmentioning

confidence: 99%

The Scent of a Smell: An Extensive Comparison Between Textual and Structural Smells

Palomba

Panichella

Zaidman

et al. 2018

IIEEE Trans. Software Eng.

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“…Different approaches for detecting anti-patterns and code smells have been proposed, and are currently in use. Some examples of the most recent efforts are detection strategies [17], which have been implemented in commercial and open source tools (e.g., inFusion 1 and PMD 2 ), the DECOR method proposed by Moha et al, [18], JDeodorant 3 , which matches different code attributes with refactoring opportunities, and machine-learning algorithms to discover relations between metrics and code smells (i.e., Arcelli et al [19], Khomh et al [20]). Recent approaches also consider code evolution via repository mining, e.g., Palomba et al [21].…”

Section: Background and Related Workmentioning

confidence: 99%

Antipattern and Code Smell False Positives: Preliminary Conceptualization and Classification

Fontana

Dietrich

Walter

et al. 2016

2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

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Abstract-Anti-patterns and code smells are archetypes used for describing software design shortcomings that can negatively affect software quality, in particular maintainability. Tools, metrics and methodologies have been developed to identify these archetypes, based on the assumption that they can point at problematic code. However, recent empirical studies have shown that some of these archetypes are ubiquitous in real world programs, and many of them are found to not be as detrimental to quality as previously conjectured. We are therefore interested on revisiting common anti-patterns and code smells, and build a catalogue of cases that constitute candidates for "false positives". We propose a preliminary classification of such false positives with the aim of facilitating a better understanding of the effects of anti-patterns and code smells in practice. We hope that the development and further refinement of such a classification can support researchers and tool vendors in their endeavor to develop more pragmatic, context-relevant detection and analysis tools for anti-patterns and code smells.

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“…The experimented algorithms obtained high performances, regardless of the type of code smell. This paper extends our previous work (Arcelli Fontana et al 2013b), where we described our preliminary results. The main contributions of this paper are: & a methodology for the application of machine learning to address code smell detection tasks; & an extensive experimentation (on 74 systems) for selecting the best algorithm and the respective parameters, for the detection of each of the considered smells.…”

Section: Introductionmentioning

confidence: 50%

Comparing and experimenting machine learning techniques for code smell detection

et al. 2015

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Several code smell detection tools have been developed providing different results, because smells can be subjectively interpreted, and hence detected, in different ways. In this paper, we perform the largest experiment of applying machine learning algorithms to code smells to the best of our knowledge. We experiment 16 different machine-learning algorithms on four code smells (Data Class, Large Class, Feature Envy, Long Method) and 74 software systems, with 1986 manually validated code smell samples. We found that all algorithms achieved high performances in the cross-validation data set, yet the highest performances were obtained by J48 and Random Forest, while the worst performance were achieved by support vector machines. However, the lower prevalence of code smells, i.e., imbalanced data, in the entire data set caused varying performances that need to be addressed in the future studies. We conclude that the application of machine learning to the detection of these code smells can provide high accuracy (>96 %), and only a hundred training examples are needed to reach at least 95 % accuracy.

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Code Smell Detection: Towards a Machine Learning-Based Approach

Cited by 131 publications

References 15 publications

The Scent of a Smell: An Extensive Comparison Between Textual and Structural Smells

The Scent of a Smell: An Extensive Comparison Between Textual and Structural Smells

Antipattern and Code Smell False Positives: Preliminary Conceptualization and Classification

Comparing and experimenting machine learning techniques for code smell detection

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