Comparing and experimenting machine learning techniques for code smell detection

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

doi:10.1007/s10664-015-9378-4

Cited by 302 publications

(147 citation statements)

References 54 publications

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“…To get a high number of conflicts we selected with the "Law of Demeter" a rather rigid design smell definition, ignored weaker variations and did not invest in perfecting the definition upfront. As an evaluation of the "Law" it would be utterly 3 We use here a term first suggested by Karl R. Popper in 1939. He states: "Instead of discussing the 'probability' of a hypothesis we should try to assess what tests, what trials, it has withstood; that is, we should try to assess how far it has been able to prove its fitness to survive by standing up to tests.…”

Section: The "Law Of Demeter" / "Law Of Good Style"mentioning

confidence: 99%

“…We relied on the Sharma and Spinellis survey as our key witness for the relevance of the question of high rates of false positives. A more thorough discussion of the work on false positives would be very desirable, especially with respect to those publications that made the effort to construct manually validated datasets [3,28,29].…”

Section: Related Workmentioning

confidence: 99%

“…Since we are already confident of finding evidence for our work there is not much corroboration for it in actually finding such evidence. 3 Let us now introduce the two "opponents" of our exploratory case study in the following two sections. Section 3 presents the "Law of Demeter" and section 4 the "JHotDraw" framework.…”

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confidence: 99%

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Did JHotDraw respect the Law of Good Style? - An exploratory deep dive into the nature of false positives of bad code smells

Speicher

2020

Programming

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Developers need to make a constant effort to improve the quality of their code if they want to stay productive. Tools that highlight code locations that could benefit from refactoring are thus highly desirable. The most common name for such locations is "bad code smell". A number of tools offer such quality feedback and there is a substantial body of related research.However, all these tools, including those based on Machine Learning, still produce false positives. Every single false positive shown to the developer places a cognitive burden on her and should thus be avoided. The literature discusses the choice of metric thresholds, the general subjectivity of such a judgment and the relation to conscious design choices, "design ideas".To examine false positives and the relation between bad smells and design ideas, we designed and conducted an exploratory case study. While previous research presented a broad overview, we have chosen a narrow setting to reach for even deeper insights: The framework JHotDraw had been designed so thoughtfully that most smell warnings are expected to be false positives. Nevertheless, the "Law of Good Style", better known as the "Law of Demeter", is a rather restrictive design rule so that we still expected to find some potential bad smells, i.e. violations of this "Law".This combination led to 1215 potential smells of which at most 42 are true positives. We found generic as well as specific design ideas that were traded for the smell. Our confidence in that decision ranged from high enough to very high. We were surprised to realize that the smell definition itself required the formulation of constructive design ideas. Finally we found some smells to be the result of the limitation of the language and one could introduce auxiliary constructive design ideas to compensate for them.The decision whether a potential smell occurrence is actually a true positive was made very meticulously. For that purpose we took three qualities that the smell could affect negatively into account and we discussed the result of the recommended refactorings. If we were convinced that we had found a false positive, we described the relationships with design ideas.The realization that not only general design ideas but also specific design ideas have an influence on whether a potential smell is a true positive turns the problem of false positives from a scientific problem ("What is the true definition of the smell?") to a engineering problem ("How can we incorporate design ideas into smell definitions?"). We recommend to add adaptation points to the smell definitions. Higher layers may then adapt the smell for specific contexts. After adaptation the tool may continuously provide distinct and precise quality feedback, reducing the cognitive load for the developer and preventing habituation. Furthermore, the schema for the discussion of potential smells may be used to elaborate more sets of true and false smell occurrences. Finally, it follows that smell detection based on machine learning should also take signs of...

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Section: The "Law Of Demeter" / "Law Of Good Style"mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Did JHotDraw respect the Law of Good Style? - An exploratory deep dive into the nature of false positives of bad code smells

Speicher

2020

Programming

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“…Furthermore, Arcelli Fontana et al [5,31] and Kessentini et al [16,48,49,97] used machine learning and search-based algorithms to discover code smells, pointing out that a training set composed of one hundred instances is sufficient to reach very high values of accuracy. Nevertheless, Di Nucci et al [25] recently showed that the performance of such techniques may vary depending on the exploited dataset.…”

Section: Code Smell Detection and Prioritizationmentioning

confidence: 99%

Improving change prediction models with code smell-related information

et al. 2019

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Code smells represent sub-optimal implementation choices applied by developers when evolving software systems. The nagative impact of code smells has been widely investigated in the past: besides developers' productivity and ability to comprehend source code, researchers empirically showed that the presence of code smells heavily impacts the change-proneness of the affected classes. On the basis of these findings, in this paper we conjecture that code smell-related information can be effectively exploited to improve the performance of change prediction models, i.e., models having as goal that of indicating to developers which classes are more likely to change in the future, so that they may apply preventive maintenance actions. Specifically, we exploit the so-called intensity index -a previously defined metric that captures the severity of a code smell-and evaluate its contribution when added as additional feature in the context of three state of the art change prediction models based on product, process, and developer-based features. We also compare the performance achieved by the proposed model with the one of an alternative technique that considers the previously defined antipattern metrics, namely a set of indicators computed considering the history of code smells in files. Our results report that (i) the prediction performance of the intensity-including

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“…In this approach, code smells are detected as agglomerations, unlike our work, where we focus on strategies that identify code smells individually. Another study by Fontana et al, (2015) applied 16 different machine-learning algorithms in 74 software systems to detect four code smells in an attempt to avoid some common problems of code smell detectors.…”

Section: Related Workmentioning

confidence: 99%

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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Comparing and experimenting machine learning techniques for code smell detection

Cited by 302 publications

References 54 publications

Did JHotDraw respect the Law of Good Style? - An exploratory deep dive into the nature of false positives of bad code smells

Did JHotDraw respect the Law of Good Style? - An exploratory deep dive into the nature of false positives of bad code smells

Improving change prediction models with code smell-related information

On the evaluation of code smells and detection tools

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