Related papers: The Prevalence of Code Smells in Machine Learning …

Nowadays, modern applications are developed using components written in different programming languages. These systems introduce several advantages. However, as the number of languages increases, so does the challenges related to the…

Large Language Models (LLMs) have shown significant potential in automating software engineering tasks, particularly in code generation. However, current evaluation benchmarks, which primarily focus on accuracy, fall short in assessing the…

Architectural code smells erode software maintainability and are costly to repair manually, yet unlike localized bugs, they require cross-module reasoning about design intent that challenges both developers and automated tools. While large…

Code review that detects and locates defects and other quality issues plays an important role in software quality control. One type of issue that may impact the quality of software is code smells. Yet, little is known about the extent to…

Code smells as symptoms of poor design and implementation choices. Many times they are the result of so called technical debt. Our study showed that the interest in code smells research is increasing. However, most of the publications are…

A code smell is a surface indicator of an inherent problem in the system, most often due to deviation from standard coding practices on the developers part during the development phase. Studies observe that code smells made the code more…

High data quality is fundamental for today's AI-based systems. However, although data quality has been an object of research for decades, there is a clear lack of research on potential data quality issues (e.g., ambiguous, extraneous…

Code smells indicate software design problems that harm software quality. Data-intensive systems that frequently access databases often suffer from SQL code smells besides the traditional smells. While there have been extensive studies on…

Code smells indicate the potential problems of software quality so that developers can identify refactoring opportunities by detecting code smells. State-of-the-art approaches leverage heuristics, machine learning, and deep learning to…

Bad smells have been defined to describe potential problems in code, possibly pointing out refactoring opportunities. Several empirical studies have highlighted that smells have a negative impact on comprehension and maintainability.…

Nowadays, we are witnessing an increasing adoption of Deep Learning (DL) based software systems in many industries. Designing a DL program requires constructing a deep neural network (DNN) and then training it on a dataset. This process…

Code review plays an important role in software quality control. A typical review process would involve a careful check of a piece of code in an attempt to find defects and other quality issues/violations. One type of issues that may impact…

Effective software development relies on managing both collaboration and technology, but sociotechnical challenges can harm team dynamics and increase technical debt. Although teams working on ML enabled systems are interdisciplinary,…

Fault-proneness is an indication of programming errors that decreases software quality and maintainability. On the contrary, code smell is a symptom of potential design problems which has impact on fault-proneness. In the literature,…

Code smells are indicators of potential design flaws in source code and do not appear alone but in combination with other smells, creating complex interactions. While existing literature classifies these smell interactions into collocated,…

This study addresses the challenge of detecting code smells in large-scale software systems using machine learning (ML). Traditional detection methods often suffer from low accuracy and poor generalization across different datasets. To…

The adoption of Artificial Intelligence (AI) in high-stakes domains such as healthcare, wildlife preservation, autonomous driving and criminal justice system calls for a data-centric approach to AI. Data scientists spend the majority of…

Code smells are symptoms of poor design and implementation choices, which might hinder comprehension, increase code complexity and fault-proneness and decrease maintainability of software systems. The aim of our study was to perform a…

Dependency management in modern software development poses many challenges for developers who wish to stay up to date with the latest features and fixes whilst ensuring backwards compatibility. Project maintainers have opted for varied, and…

Much of the existing ML research focuses on model performance metrics, leaving limited attention to the long-term sustainability and resource efficiency of ML applications. While high performance is essential, ensuring efficient resource…