Related papers: Test Smell Detection Tools: A Systematic Mapping S…
Large Language Models (LLMs) have gained massive popularity in recent years and are increasingly integrated into software systems for diverse purposes. However, poorly integrating them in source code may undermine software system quality.…
Code Smell, similar to a bad smell, is a surface indication of something tainted but in terms of software writing practices. This metric is an indication of a deeper problem lies within the code and is associated with an issue which is…
The common use case of code smells assumes causality: Identify a smell, remove it, and by doing so improve the code. We empirically investigate their fitness to this use. We present a list of properties that code smells should have if they…
CONTEXT: There has been a rapid growth in the use of data analytics to underpin evidence-based software engineering. However the combination of complex techniques, diverse reporting standards and poorly understood underlying phenomena are…
Test smells reduce test suite reliability and complicate maintenance. While many methods detect test smells, few support automated removal, and most rely on static analysis or machine learning. This study evaluates models with relatively…
Code smells are widely used indicators of poor code quality, revealing structural problems and areas where improvement can be made. Although extensively studied in object-oriented languages, functional programming languages remain…
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 signal violations of design principles that degrade the internal quality of evolving software systems. Although many tools detect such anomalies using static metrics, they often ignore the development context in which smells…
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…
The learning and usage of an API is supported by official documentation. Like source code, API documentation is itself a software product. Several research results show that bad design in API documentation can make the reuse of API features…
Large Language Models (LLMs) are increasingly integrated into software systems for diverse purposes, due to their versatility, flexibility, and ability to simulate human reasoning to some extent. However, poor integration of LLM inference…
Unit testing is an essential component of the software development life-cycle. A developer could easily and quickly catch and fix software faults introduced in the source code by creating and running unit tests. Despite their importance,…
The accuracy reported for code smell-detecting tools varies depending on the dataset used to evaluate the tools. Our survey of 45 existing datasets reveals that the adequacy of a dataset for detecting smells highly depends on relevant…
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,…
Logging plays a central role in ensuring reproducibility, observability, and reliability in machine learning (ML) systems. While logging is generally considered a good engineering practice, poorly designed logging can negatively affect…
Requirements form the basis for defining software systems' obligations and tasks. Testable requirements help prevent failures, reduce maintenance costs, and make it easier to perform acceptance tests. However, despite the importance of…
Machine learning (ML) has rapidly grown in popularity, becoming vital to many industries. Currently, the research on code smells in ML applications lacks tools and studies that address the identification and validity of ML-specific code…
Angular is one of the most widely adopted frameworks for developing large-scale, dynamic web applications. As projects increase in scope and complexity, developers face growing challenges in managing architecture and maintaining clean,…
Context: Code smells (CS) tend to compromise software quality and also demand more effort by developers to maintain and evolve the application throughout its life-cycle. They have long been catalogued with corresponding mitigating solutions…
\underline{Context:} Logging is a fundamental yet complex practice in software engineering, essential for monitoring, debugging, and auditing software systems. With the increasing integration of machine learning (ML) components into…