Related papers: Detecting Bad Smells in Use Case Descriptions

Reinforcement Learning (RL) is being increasingly used to learn and adapt application behavior in many domains, including large-scale and safety critical systems, as for example, autonomous driving. With the advent of plug-n-play RL…

Issue Tracking Systems (ITSs) enable software developers and managers to collect and resolve issues collaboratively. While researchers have extensively analysed ITS data to automate or assist specific activities such as issue assignments,…

Urban air pollution has been linked to various human health considerations, including cardiopulmonary diseases. Communities who suffer from poor air quality often rely on experts to identify pollution sources due to the lack of accessible…

Code Smell Detection (CSD) plays a crucial role in improving software quality and maintainability. And Deep Learning (DL) techniques have emerged as a promising approach for CSD due to their superior performance. However, the effectiveness…

We explore a suite of generative modelling techniques to efficiently navigate and explore the complex landscapes of odor and the broader chemical space. Unlike traditional approaches, we not only generate molecules but also predict the odor…

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…

Explainability, i.e. the ability of a system to explain its behavior to users, has become an important quality of software-intensive systems. Recent work has focused on methods for generating explanations for various algorithmic paradigms…

It is known that user-centered approaches to requirements engineering in general lead to a better suited product for the end-users. LLM4RE provides promising approaches to support the requirements elicitation process (e.g. classification of…

Educating students about software testing practices is integral to the curricula of many computer science-related courses and typically involves students writing unit tests. Similar to production/source code, students might inadvertently…

Automated code smell detection faces persistent challenges due to the subjectivity of heuristic rules and the limited performance of traditional ML/DL models. While Large Language Models (LLMs) offer a promising alternative, their adoption…

The growth of Python adoption across diverse domains has led to increasingly complex codebases, presenting challenges in maintaining code quality. While numerous tools attempt to address these challenges, they often fall short in providing…

As software proliferates across domains, its aggregate energy footprint has become a major concern. To reduce software's growing environmental footprint, developers need to identify and refactor energy smells: source code implementations,…

Architecture erosion has a detrimental effect on maintenance and evolution, as the implementation deviates from the intended architecture. Detecting symptoms of erosion, particularly architectural violations, at an early stage is crucial.…

Molecular odor prediction has great potential across diverse fields such as chemistry, pharmaceuticals, and environmental science, enabling the rapid design of new materials and enhancing environmental monitoring. However, current methods…

Large Language Models (LLMs) changed the way we design and interact with software systems. Their ability to process and extract information from text has drastically improved productivity in a number of routine tasks. Developers that want…

Context: The evidence for the prevalence of test smells at the unit testing level has relied on the accuracy of detection tools, which have seen intense research in the last two decades. The Eager Test smell, one of the most prevalent, is…

Urban air pollution has been linked to various human health concerns, including cardiopulmonary diseases. Communities who suffer from poor air quality often rely on experts to identify pollution sources due to the lack of accessible tools.…

Requirements Engineering (RE) is the initial step towards building a software system. The success or failure of a software project is firmly tied to this phase, based on communication among stakeholders using natural language. The problem…

Determining the most effective Large Language Model for code smell detection presents a complex challenge. This study introduces a structured methodology and evaluation matrix to tackle this issue, leveraging a curated dataset of code…

While a substantial body of prior research has investigated the form and nature of production code, comparatively little attention has examined characteristics of test code, and, in particular, test smells in that code. In this paper, we…