An Investigative Study on How Developers Filter and Prioritize Code Smells

Sae-Lim, Natthawute; Hayashi, Shinji; Saeki, Motoshi

doi:10.1587/transinf.2017kbp0006

Cited by 17 publications

(4 citation statements)

References 31 publications

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“…The main reason is that although solving code smells that are irrelevant to developers' context (e.g., modules developers plan to modify) may improve the overall quality of the system, it does not support the developers' current activities. This statement is also supported by our previous study on professional developers showing that developers tend to refactor the code smells related to their context [29]. Therefore, context-irrelevant modules can be postponed until they become relevant to developers' context.…”

Section: Discussionsupporting

confidence: 69%

“…This phenomenon may be comparable to the fact that static analysis tools are not used well by developers owing to a large number of detected warnings [28]. In other words, the number of code smells is too high for developers to consider refactoring, thereby resulting in several studies focusing on filtering and prioritizing code smells [29]- [31]. Here the following simple question arises: Why should developers handle non-smelly modules when they already have more than enough smelly modules to handle.…”

Section: Discussionmentioning

confidence: 99%

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Supporting Proactive Refactoring: An Exploratory Study on Decaying Modules and Their Prediction

Sae-Lim

Hayashi

Saeki

2021

IEICE Trans. Inf. & Syst.

Self Cite

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Code smells can be detected using tools such as a static analyzer that detects code smells based on source code metrics. Developers perform refactoring activities based on the result of such detection tools to improve source code quality. However, such an approach can be considered as reactive refactoring, i.e., developers react to code smells after they occur. This means that developers first suffer the effects of low-quality source code before they start solving code smells. In this study, we focus on proactive refactoring, i.e., refactoring source code before it becomes smelly. This approach would allow developers to maintain source code quality without having to suffer the impact of code smells. To support the proactive refactoring process, we propose a technique to detect decaying modules, which are non-smelly modules that are about to become smelly. We present empirical studies on open source projects with the aim of studying the characteristics of decaying modules. Additionally, to facilitate developers in the refactoring planning process, we perform a study on using a machine learning technique to predict decaying modules and report a factor that contributes most to the performance of the model under consideration.

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Section: Discussionsupporting

confidence: 69%

Section: Discussionmentioning

confidence: 99%

Supporting Proactive Refactoring: An Exploratory Study on Decaying Modules and Their Prediction

Sae-Lim

Hayashi

Saeki

2021

IEICE Trans. Inf. & Syst.

Self Cite

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“…Many studies determine smell severity automatically by measuring the chosen code metrics or using other heuristics such as historical information of component modification. However, the large variety of such approaches suggests that researchers disagree on the factors that should be used to filter and prioritize code smells (Sae-Lim et al, 2018).…”

Section: Define Severity Scalementioning

confidence: 99%

Towards a systematic approach to manual annotation of code smells

Luburić¹,

Prokić²,

Grujić³

et al. 2022

Preprint

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<div>Code smells are structures in code that indicate the presence of maintainability issues. A significant problem with code smells is their ambiguity. They are challenging to define, and software engineers have a different understanding of what a code smell is and which code suffers from code smells.</div><div>A solution to this problem could be an AI digital assistant that understands code smells and can detect (and perhaps resolve) them. However, it is challenging to develop such an assistant as there are few usable datasets of code smells on which to train and evaluate it. Furthermore, the existing datasets suffer from issues that mostly arise from an unsystematic approach used for their construction.</div><div>Through this work, we address this issue by developing a procedure for the systematic manual annotation of code smells. We use this procedure to build a dataset of code smells. During this process, we refine the procedure and identify recommendations and pitfalls for its use. The primary contribution is the proposed annotation model and procedure and the annotators’ experience report. The dataset and supporting tool are secondary contributions of our study. Notably, our dataset includes open-source projects written in the C# programming language, while almost all manually annotated datasets contain projects written in Java.</div>

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“…In construction of this mapping, several sources in the literature have been used. Sae-Lim et al conducted an investigation study to find out how developers filter and prioritize code smells [79]. They found that the most important factors for filtering are 1) task relevance, 2) smell severity, and 3) task implementation cost.…”

Section: ) Code Heterogenitymentioning

confidence: 99%

Selected Code-Quality Characteristics and Metrics for Internet of Things Systems

et al. 2022

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Software code is present on multiple levels within current Internet of Things (IoT) systems.The quality of this code impacts system reliability, safety, maintainability, and other quality aspects. In this paper, we provide a comprehensive overview of code quality-related metrics, specifically revised for the context of IoT systems. These metrics are divided into main code quality categories: Size, redundancy, complexity, coupling, unit test coverage and effectiveness, cohesion, code readability, security, and code heterogeneity. The metrics are then linked to selected general quality characteristics from the ISO/IEC 25010:2011 standard by their possible impact on the quality and reliability of an IoT system, the principal layer of the system, the code levels and the main phases of the projectto which they are relevant. This analysis is followed by a discussion of code smells and their relation to the presented metrics. The overview presented in the paper is the result of a thorough analysis and discussion of the author's team with the involvement of external subject-matter experts in which a defined decision algorithm was followed. The primary result of the paper is an overview of the metrics accompanied by applicability notes related to the quality characteristics, the system layer, the level of the code, and the phase of the IoT project.

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An Investigative Study on How Developers Filter and Prioritize Code Smells

Cited by 17 publications

References 31 publications

Supporting Proactive Refactoring: An Exploratory Study on Decaying Modules and Their Prediction

Supporting Proactive Refactoring: An Exploratory Study on Decaying Modules and Their Prediction

Towards a systematic approach to manual annotation of code smells

Selected Code-Quality Characteristics and Metrics for Internet of Things Systems

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