Related papers: SACS: A Code Smell Dataset using Semi-automatic Ge…
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.…
Microservice architecture has become a dominant architectural style in the service-oriented software industry. Poor practices in the design and development of microservices are called microservice bad smells. In microservice bad smells…
Machine learning (ML) codebases face unprecedented challenges in maintaining code quality and sustainability as their complexity grows exponentially. While traditional code smell detection tools exist, they fail to address ML-specific…
Test smells, similar to code smells, can negatively impact both the test code and the production code being tested. Despite extensive research on test smells in languages like Java, Scala, and Python, automated tools for detecting test…
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…
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…
Large-scale data collection is essential for developing personalized training data, mitigating the shortage of training data, and fine-tuning specialized models. However, creating high-quality datasets quickly and accurately remains a…
Developers often refactor code to improve the maintainability and comprehension of the software. There are many studies on refactoring activities in traditional software systems. However, refactoring in data-intensive systems is not well…
Diverse studies have analyzed the quality of automatically generated test cases by using test smells as the main quality attribute. But recent work reported that generated tests may suffer a number of quality issues not necessarily…
Context: Large Language Models (LLMs) are increasingly being used to generate program code. Much research has been reported on the functional correctness of generated code, but there is far less on code quality. Objectives: In this study,…
Code smells represent sub-optimal implementation choices applied by developers when evolving software systems. The negative impact of code smells has been widely investigated in the past: besides developers' productivity and ability to…
Similarly to production code, code smells also occur in test code, where they are called test smells. Test smells have a detrimental effect not only on test code but also on the production code that is being tested. To date, the majority of…
Architectural smells (AS) are notorious for their long-term impact on the Maintainability and Evolvability of software systems. The majority of research work has investigated this topic by mining software repositories of open source Java…
Code smells are symptoms of potential code quality problems that may affect software maintainability, thus increasing development costs and impacting software reliability. Large language models (LLMs) have shown remarkable capabilities for…
Context: Logging is an important part of modern software projects; logs are used in several tasks such as debugging and testing. Due to the complex nature of logging, it remains a difficult task with several pitfalls that could have serious…
As an integral part of source code files, code comments help improve program readability and comprehension. However, developers sometimes do not comment on their program code adequately due to the incurred extra efforts, lack of relevant…
Automatic generation of software code from system design models remains an actively explored research area for the past several years. A number of tools are currently available to facilitate and automate the task of generating code from…
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…
Code samples play a pivotal role in open-source ecosystems (OSSECO), serving as lightweight artifacts that support knowledge transfer, onboarding, and framework adoption. Despite their instructional relevance, these samples are often…
Artificial Intelligence (AI) and Machine Learning (ML) are pervasive in the current computer science landscape. Yet, there still exists a lack of software engineering experience and best practices in this field. One such best practice,…