The detection of code smell on software development: a mapping study

Kumar

J Software Evolu Process

2023

Code smells indicate poor design and imperfection in coding style. They increase maintenance costs and degrade software quality. Detecting and mitigating these code smells reduce the effects of flaws present in the software design. However, to save time and effort, it is a good practice to prioritize critical code smells and resolve them first. There have been various empirical studies on code smell prioritization in the last decades. Although the research community has been continuously working on the prioritization of code smells, yet a systematic mapping of all these studies would provide a panorama of research work. With this motivation, we selected 39 primary studies published between May 2011 and May 2022 and classified them based on seven factors: (i) code smells, (ii) factors, (iii) evaluation techniques, (iv) tools, (v) ranking formulas, (vi) data sets used, and (vii) measurements used for validating the research work. This paper aims to outline and discuss the prioritization procedure for code smells in object‐oriented software systems. The issues and recommendations for every question act as input for the researchers and industrialists for further research. Some issues for further investigation are less coverage of architecture smells, limited ranking formulas, lack of automated evaluation techniques, and so forth.

Section: Alternative Classes With Different Interfacesmentioning

confidence: 99%

Code smell prioritization in object‐oriented software systems: A systematic literature review

Kumar

J Software Evolu Process

2023

“…They aimed to provide a concise summary of these tools, highlighting their main features and effectiveness in detecting bad smells. Liu and Zhang [18] conducted a mapping study to scrutinize the purpose of code smell research and the scope of detection tools. They delved into the empirical software engineering context to determine if the available detection tools could identify all types of code smells.…”

Section: Related Workmentioning

confidence: 99%

Research Trends, Detection Methods, Practices, and Challenges in Code Smell: SLR

Hilmi,

Puspaningrum,

Darsih

et al. 2023

IEEE Access

Context: A code smell indicates a flaw in the design, implementation, or maintenance process that could degrade the software's quality and potentially cause future disruptions. Since being introduced by Beck and Fowler, the term code smell has attracted several studies from researchers and practitioners. However, over time, studies are needed to discuss whether this issue is still interesting and relevant. Objective: Conduct a thorough systematic literature review to learn the most recent state of the art for studying code smells, including detection methods, practices, and challenges. Also, an overview of trends and future relevance of the topic of code smell, whether it is still developing, or if there has been a shift in the discussion. Method: The search methodology was employed to identify pertinent scholarly articles from reputable databases such as ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, ProQuest, and CiteSeerX. The application of inclusion and exclusion criteria serves to filter the search results. In addition, forward and backward snowballing techniques are employed to enhance the comprehensiveness of the results. Results:The inquiry yielded 354 scholarly articles published over the timeframe spanning from January 2013 to July 2022. After inclusion, exclusion, and snowballing techniques were applied, 69 main studies regarding code smells were identified. Many researchers focus on detecting code smells, primarily via machine learning techniques and, to a lesser extent, deep learning methods. Additional subjects encompass the ramifications of code smells; code smells within specific contexts, the correlation between code smells and software metrics, and facets about security, refactoring, and development habits. Contexts and types of code smells vary in the focus of the study. Some tools used are Jspirit, aDoctor, CAME, and SonarQube. The study also explores the concept of design smells and anti-pattern detection. While a singular dominating technique to code smell detection has yet to be thoroughly investigated, other aspects of code smell detection remain that still need to be examined. Conclusion: The findings underscore scholarly attention's evolution towards code smells over the years. This study identified significant journals and conferences and influential researchers in this field. The detection methods used include empirical, machine learning, and deep learning. However, challenges include subjective interpretation and limited contextual applicability.

“…Major instances of technical debt include code smells and ineffective design decisions made by developers, which can have a negative impact on the maintainability, scalability, and quality of a software system [4]. Over the past few decades, research has heavily focused on the following areas [5]:…”

Section: Introductionmentioning

confidence: 99%

“…In the software engineering industry, extensive research has been conducted to identify code smells within codebases during software development and investigate their impact and various dimensions. Rulebased or static code analysis tools are increasingly popular [5]. By using intelligent code smell analysis and detection systems, the system would be able to adapt and update rules and sequences based on new changes, enabling it to predict or identify code smells in advance [6].…”

mentioning

confidence: 99%

Improving Code Smell Detection Using Deep Stacked Autoencoder

Rehef,

Abbas

2024

Preprint

The term "code smell" refers to an indication of a problem with the quality of source code. Numerous studies have been conducted to identify problematic features in source code. Initially, the focus was on utilizing metric-based and heuristic-based approaches. In recent years, however, there has been a shift towards using machine learning and deep learning (DL) techniques for smell detection. Nevertheless, the current algorithms are still considered to be in the early stages of development. Recognizing the challenges associated with identifying smells using DL methods, both academics and software developers have made efforts to address these obstacles. This work involves constructing and evaluating new DL models for code smell detection. Two models are built upon stacked autoencoders, employing a hybrid architecture that combines bidirectional long short-term memory and convolutional neural network components.