MMRUC3: A recommendation approach of<i>move method</i>refactoring using coupling, cohesion, and contextual similarity to enhance software design

Rahman, Md. Masudur; Riyadh, Rashed Rubby; Khaled, Shah Mostafa; Satter, Abdus

doi:10.1002/spe.2591

Cited by 7 publications

(4 citation statements)

References 34 publications

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“…Other efforts [64][65][66][67][68][69][70][71] have been done in context of recommendation projects for developers and vice-versa. For instance, Liu et al 72 recommend software features from the users' perspective for designers of the project.…”

Section: Related Workmentioning

confidence: 99%

Dual analysis for helping developers to find collaborators based on co‐changed files: An empirical study

2023

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Summary Software developers must collaborate at all stages of the software life‐cycle to create successful complex software systems. To enable this collaboration, social coding platforms, for example, GitHub, include an increasing number of tools to support collaboration. However, for large projects with hundreds of dynamic developers, such as several successful open–source projects, it can be complex to find developers with the same interest and familiarity and thus, gain suitable collaborations and new insights. In this context, resources and efforts may be wasted, discouraging many developers from contributing. Moreover, it can be costly to manage many contributions, which is another challenge for the maintainer who wants to take advantage of this small, timid, but valuable contribution made by a volunteer developer in a short time. In this context, this paper presents an empirical study aiming to evaluate two strategies to recommend collaborators based on co‐changed files. Inspired in the TF–IDF (Term Frequency–Inverse Document Frequency) weighting scheme established in the Information Retrieval field, these strategies first estimate the importance of relevant files modified by developers and use these estimates to represent each developer “profile”. As a second step, they estimate the similarity between developers using the Cosine metric, providing top‐ranked developers according to this measure as recommendations. We evaluated these strategies based on an extensive survey with 102 real–world developers. We observed that developers have interest and familiarity with the co‐changed files for all strategies evaluated. These considerations are of relevance because many opportunities for contributions to the project are linked to coding. Thus, theses results may indicate one less barrier for improving collaboration among developers. Overall, the strategies present an acceptance rate of up to 81%, contributing to the discovery of further collaborators.

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Section: Related Workmentioning

confidence: 99%

Dual analysis for helping developers to find collaborators based on co‐changed files: An empirical study

2023

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“…As the scale of software systems becomes increasingly large and complex, software quality attracts the attention of researchers in the software engineering domain Tong et al (2022); Sabir et al (2019). Code smells are code symptoms caused by design flaws or bad coding idioms, which might negatively impact software quality factors Fowler (2018); Rahman et al (2018). Identifying code smells is a vital task that helps software developers improve the design of their software Alkharabsheh et al (2022); Rahad et al (2021); Sousa et al (2019).…”

Section: Introductionmentioning

confidence: 99%

On the Relative Value of Imbalanced Learning for Code Smell Detection

Xiao

Zou

et al. 2023

Preprint

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Machine learning-based code smell detection has been demonstrated to be a valuable approach for improving software quality and enabling developers to identify problematic patterns in code. However, previous researches have shown that the code smell datasets commonly used to train these models are heavily imbalanced. While some recent studies have explored the use of imbalanced learning techniques for code smell detection, they have only evaluated a limited number of techniques and thus their conclusions about the most effective methods may be biased and inconclusive. To thoroughly evaluate the effect of imbalanced learning techniques on machine learning-based code smell detection, we examine 31 imbalanced learning techniques with seven classifiers to build code smell detection models on four code smell data sets. We employ four evaluation metrics to assess the detection performance with the Wilcoxon signed-rank test and Cliff’s δ. The results show that (1) Not all imbalanced learning techniques significantly improve detection performance, but deep forest significantly outperforms the other techniques on all code smell data sets. (2) SMOTE (Synthetic Minority Over-sampling TEchnique) is not the most effective technique for resampling code smell data sets. (3) The best-performing imbalanced learning techniques and the top-3 data resampling techniques have little time cost for code smell detection. Therefore, we provide some practical guidelines. First, researchers and practitioners should select the appropriate imbalanced learning techniques (e.g., deep forest) to ameliorate the class imbalance problem. In contrast, the blind application of imbalanced learning techniques could be harmful. Then, better data resampling techniques than SMOTE should be selected to preprocess the code smell data sets.

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“…[1][2][3][4][5][6][7][8] One important aspect of maintaining software quality is the detection of poor programming practices or design weaknesses, known as "Code Smells". 9,10 Detecting code smells is crucial in supporting software developers to improve software design and quality [11][12][13] and the existence of code smells will negatively impact key software quality attributes such as reliability and changeability. 14,15 With the growing complexity in software systems, there is a need for innovative approaches and techniques to accurately detect the smelly code blocks.…”

Section: Introductionmentioning

confidence: 99%

“…As the scale of software systems becomes increasingly large and complex, software quality attracts the attention of researchers in the software engineering domain 1‐8 . One important aspect of maintaining software quality is the detection of poor programming practices or design weaknesses, known as “Code Smells” 9,10 . Detecting code smells is crucial in supporting software developers to improve software design and quality 11‐13 and the existence of code smells will negatively impact key software quality attributes such as reliability and changeability 14,15 .…”

Section: Introductionmentioning

confidence: 99%

On the relative value of imbalanced learning for code smell detection

Zou

Keung

et al. 2023

Softw Pract Exp

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SummaryMachine learning‐based code smell detection (CSD) has been demonstrated to be a valuable approach for improving software quality and enabling developers to identify problematic patterns in code. However, previous researches have shown that the code smell datasets commonly used to train these models are heavily imbalanced. While some recent studies have explored the use of imbalanced learning techniques for CSD, they have only evaluated a limited number of techniques and thus their conclusions about the most effective methods may be biased and inconclusive. To thoroughly evaluate the effect of imbalanced learning techniques for machine learning‐based CSD, we examine 31 imbalanced learning techniques with seven classifiers to build CSD models on four code smell data sets. We employ four evaluation metrics to assess the detection performance with the Wilcoxon signed‐rank test and Cliff's . The results show that (1) Not all imbalanced learning techniques significantly improve detection performance, but deep forest significantly outperforms the other techniques on all code smell data sets. (2) SMOTE (Synthetic Minority Over‐sampling TEchnique) is not the most effective technique for resampling code smell data sets. (3) The best‐performing imbalanced learning techniques and the top‐3 data resampling techniques have little time cost for code smell detection. Therefore, we provide some practical guidelines. First, researchers and practitioners should select the appropriate imbalanced learning techniques (e.g., deep forest) to ameliorate the class imbalance problem. In contrast, the blind application of imbalanced learning techniques could be harmful. Then, better data resampling techniques than SMOTE should be selected to preprocess the code smell data sets.

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MMRUC3: A recommendation approach ofmove methodrefactoring using coupling, cohesion, and contextual similarity to enhance software design

Cited by 7 publications

References 34 publications

Dual analysis for helping developers to find collaborators based on co‐changed files: An empirical study

Dual analysis for helping developers to find collaborators based on co‐changed files: An empirical study

On the Relative Value of Imbalanced Learning for Code Smell Detection

On the relative value of imbalanced learning for code smell detection

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