Software Design Smell Detection: a systematic mapping study

Alkharabsheh, Khalid; Crespo, Yania; Manso, M. Esperanza; Taboada, José Antonio Franco

doi:10.1007/s11219-018-9424-8

Cited by 50 publications

(32 citation statements)

References 20 publications

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“…The smell metaphor has been adopted for several categories of software projects. Alkharabsheh et al [27] carried out a systematic analysis of the state-of-the-art about code smell detection spanning over the period 2000-2017 (the smell metaphor was introduced in 1999 by Kent Beck). Sharma and Spinellis [28] is another up-to-date survey on studies about smell detection methods published in the period 1999-2016.…”

Section: Code Metrics and Code Smellsmentioning

confidence: 99%

Empirical Assessment of the Quality of MVC Web Applications Returned by xGenerator

et al. 2021

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Many scholars have reported that the adoption of Model Driven Engineering (MDE) in the industry is still marginal. Real-life case studies, completed with convincing empirical data about the quality of the developed source code, is an effective way to persuade the industry that the adoption of MDE brings an actual added value. This paper reports about the assessment of the quality of the code outputted by xGenerator: a Java technology platform for the development of enterprise Web applications, which implements the MDE paradigm. Two recent papers from Aniche and his colleagues were selected to carry out the measurements. The former study is about metrics and thresholds for MVC Web applications, while the latter presents a catalog of six smells tailored to MVC Web applications. A big merit of both of these proposals is that they fix the metric thresholds by taking into account the MVC software architecture. The results of the empirical assessment, carried out on a real-life project, proved that the quality of the code is high.

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Section: Code Metrics and Code Smellsmentioning

confidence: 99%

Empirical Assessment of the Quality of MVC Web Applications Returned by xGenerator

et al. 2021

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“…There are several tools and IDE (integrated development environment) available for detecting of code smells [37]. Code smells are suggested as an attempt by programmers to reform their software.…”

Section: Detection Toolsmentioning

confidence: 99%

Design and Implementation of a Web-Based Application for Code Smells Repository

Bamizadeh

Kumar²,

Kumar³

et al. 2021

Teh. glas. (Online)

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Pitfalls in software development process can be prevented by learning from other people's mistakes. Software practitioners and researchers document lessons learned and the knowledge about best practices is spread over literature. Presence of code smells does not indicate that software won’t work, but it will reveal deeper problems and rising risk of failure in future. Software metrics are applied to detect code smells whereas refactoring can remove code smells, improve code quality and make it simpler and cleaner. Detection tools facilitate management of code smells. Knowledge about code smells and related concepts can assist the software maintenance process. Exploratory analysis of code smells carried out in this paper, covers collecting data about code smells, identifying related concepts, categorizing and organizing this knowledge into a code smell repository, which can be made available to software developers. A detailed literature survey is carried out to identify code smells and related concepts. An initial list of 22 code smells proposed in 1999 has grown over the years into 65 code smells. The relationship between code smells, software metrics, refactoring methods and detection tools available in literature is also documented. Templates are designed that capture knowledge about code smells and related concepts. A code smell repository is designed and implemented to maintain all the information gathered about code smells and related concepts and is made available to software practitioners. All the knowledge about code smells found in literature is collected, organized and made accessible.

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“…Sobrinho, De Lucia, and Maia reviewed [37] 351 research papers published between 1990 and 2017. Alkharabsheh, Crespo, Manso, and Taboada reviewed [2] 395 research papers published between 2000 an 2017. We relied on the Sharma and Spinellis survey as our key witness for the relevance of the question of high rates of false positives.…”

Section: Related Workmentioning

confidence: 99%

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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Software Design Smell Detection: a systematic mapping study

Cited by 50 publications

References 20 publications

Empirical Assessment of the Quality of MVC Web Applications Returned by xGenerator

Empirical Assessment of the Quality of MVC Web Applications Returned by xGenerator

Design and Implementation of a Web-Based Application for Code Smells Repository

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

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