The Effect of Code Smells on the Relationship Between Design Patterns and Defects

Alkhaeir, Tarek; Walter, Bartosz

doi:10.1109/access.2020.3047870

Cited by 6 publications

(3 citation statements)

References 51 publications

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“…Refactoring DBS challenges: the occurrence of DBS (code smells and grime) has provided harmful impacts on both design pattern and software quality. They increase source code complexity, resulting in the decline of software understandability, correctness, and the increase in the fault and defect-proneness of software, proving that their exhiliration degrades the patterns' integrity, reusability, and adaptability [24,26,28]. However, an under-explored challenge is refactoring the design pattern's code fragments, which is affected by the occurrence of DBS.…”

Section: Dbs Detection and The Utilized Datasetsmentioning

confidence: 99%

“…Consequently, bad smells in design patterns are found to threaten software quality and sustainability by impacting software modularity, readability, reusability, correctness, and testability [1,22]. This, in turn, introduces a dramatic increase in code complexity, defect and change proneness, which impacts the stability, increases the energy consumption of software, and magnifies future maintenance costs (e.g., [17,[23][24][25][26][27][28][29]).…”

Section: Introductionmentioning

confidence: 99%

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Bad Smells of Gang of Four Design Patterns: A Decade Systematic Literature Review

2021

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Gang of Four (GoF) design patterns are widely approved solutions for recurring software design problems, and their benefits to software quality are extensively studied. However, the occurrence of bad smells in design patterns increases the crisis of degenerating design patterns’ structure and behavior. Their occurrences are detrimental to the benefits of design patterns and they influence software sustainability by increasing maintenance costs and energy consumption. Despite the destructive roles of bad smells in such designs, there are an absence of studies systematically reviewing bad smells of GoF design patterns. This study systematically reviews a 10-year state of the art sample, identifying 16 studies investigating this phenomenon. Following a thorough evaluation of the full contents, we observed that the occurrence of bad smells have been investigated in proportion to four granularity levels of analysis: Design level, category level, pattern level, and role level. We identified 28 bad smells, categorized under code smells and grime symptoms, and emphasized their relationship with GoF pattern types and categories. The utilization of design pattern bad smell detection approaches and datasets were also discussed. Consequently, we observed that the research phenomenon is growing intensively, with a prominent focus of studies analyzing code smell occurrences rather than grime occurrences, at various granularity levels. Finally, we uncovered research gaps and areas with significant potentials for future research.

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Section: Dbs Detection and The Utilized Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bad Smells of Gang of Four Design Patterns: A Decade Systematic Literature Review

2021

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“…'Bad smell' code reflects flaws in code structure and violations of design principles, significantly impacting software quality [4]. Previous studies have investigated the effects of smells on software, revealing that they not only reduce code quality, performance, and comprehensibility, but also lead to increased complexity in software maintenance and a higher risk of bugs [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Emerging Trends in Code Quality: Introducing Kotlin-Specific Bad Smell Detection Tool for Android Apps

Novendra,

Danar Sunindyo

2024

IEEE Access

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The increasing demand for Android applications in line with technological evolution and the development of new features often leads to frequent updates and releases of applications. However, in this update cycle, developers sometimes make hasty changes that result in poor design choices, leading to the emergence of bad smell code. The impact of smells involves a decrease in quality, performance, and understanding, and impedes the software maintenance process. Some previously developed smell detection tools rely on JavaParser to parse source code into an Abstract Syntax Tree representation, meaning the extracted information is limited to Java programming language source code. Meanwhile, Google recommends Kotlin as the language for developing Android applications, and over 60% of professional Android developers use Kotlin. This research is using Program Structure Interface representation and applies a software metric-based approach as a method of smell detection and implements it into a Kotlin Android bad smell code detection tool. This approach was chosen because the characteristics of each smell can be represented with software metrics. The tool's evaluation results on five Kotlin Android projects showed an F-measure of 100% for Brain Class detection, 93.77% for God Class, and 85.71% for Brain Method. Comparing the tool's detection results with the iPlasma tool on the Quran project showed that the developed tool detected more smell occurrences. Usability testing results indicated that seven participants strongly agreed with the easy-to-understand detection results, easy-to-find presented functionality, clear presented information, and the tool helped to perform metric calculation and smell detection more quickly and efficiently. The average rating given for each question was between 4.29 -4.71.

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An empirical investigation of the relationship between pattern grime and code smells

Alharbi,

Alshayeb

2024

J Software Evolu Process

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Developers are encouraged to adopt good design practices to maintain good software quality during the system's evolution. However, some modifications and changes to the system could cause code smells and pattern grime, which might incur more maintenance effort. As the presence of both code smells and pattern grime is considered a bad sign and raises a flag at code segments that need more careful examination, a potential connection between them may exist. Therefore, the main objective of this paper is to (1) empirically investigate the potential relationship between the accumulation of pattern grime and the presence of code smells and (2) evaluate the significance of individual code smells when they appear in a specific pattern grime category. To achieve this goal, we performed an empirical study using six‐grime metrics and 10 code smells on five Java open‐source projects ranging from 217 to 563 classes. Our statistical results indicate that, in general, the growth of grime is more likely to co‐occur with code smells using Spearman's correlation and Odd Ratio test. Specifically, there is a strong positive association between the growth of pattern grime at the class level and the presence of Shotgun Surgery smell according to the result of applying the Apriori algorithm, which gives conviction values equal to 1.66. The findings in this paper are helpful for developers and researchers as the presence of pattern grime could be considered a factor in improving the performance of existing smell detection methods. Furthermore, the link between grime and smells can be exploited as a hint for smell distribution in the system.

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The Effect of Code Smells on the Relationship Between Design Patterns and Defects

Cited by 6 publications

References 51 publications

Bad Smells of Gang of Four Design Patterns: A Decade Systematic Literature Review

Bad Smells of Gang of Four Design Patterns: A Decade Systematic Literature Review

Emerging Trends in Code Quality: Introducing Kotlin-Specific Bad Smell Detection Tool for Android Apps

An empirical investigation of the relationship between pattern grime and code smells

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