Related papers: Test Smell Detection Tools: A Systematic Mapping S…

As Deep learning (DL) systems continuously evolve and grow, assuring their quality becomes an important yet challenging task. Compared to non-DL systems, DL systems have more complex team compositions and heavier data dependency. These…

The popularity of machine learning has wildly expanded in recent years. Machine learning techniques have been heatedly studied in academia and applied in the industry to create business value. However, there is a lack of guidelines for code…

The context of this work is specification, detection and ultimately removal of detectable harmful patterns in source code that are associated with defects in design and implementation of software. In particular, we investigate five 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…

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…

During software development, poor design and implementation choices can detrimentally impact software maintainability. Design smells, recurring patterns of poorly designed fragments, signify these issues. Role-stereotypes denote the generic…

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…

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…

Simulation modelling systems are routinely used to test or understand real-world scenarios in a controlled setting. They have found numerous applications in scientific research, engineering, and industrial operations. Due to their complex…

Context. The adoption of Machine Learning (ML)--enabled systems is steadily increasing. Nevertheless, there is a shortage of ML-specific quality assurance approaches, possibly because of the limited knowledge of how quality-related concerns…

Machine Learning (ML) projects incur novel challenges in their development and productionisation over traditional software applications, though established principles and best practices in ensuring the project's software quality still…

Unlike most other software quality attributes, testability cannot be evaluated solely based on the characteristics of the source code. The effectiveness of the test suite and the budget assigned to the test highly impact the testability of…

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,…

Test smells indicate poor development practices in test code, reducing maintainability and reliability. While developers often struggle to prevent or refactor these issues, existing tools focus primarily on detection rather than automated…

The Large Language Models (LLMs) have demonstrated great potential in code-related tasks. However, most research focuses on improving the output quality of LLMs (e.g., correctness), and less attention has been paid to the LLM input (e.g.,…

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,…

LLMs promise to transform unit test generation from a manual burden into an automated solution. Yet, beyond metrics such as compilability or coverage, little is known about the quality of LLM-generated tests, particularly their…

Java remains one of the most popular programming languages in education. Although Java programming education is well supported by study materials, learners also need more immediate support on the problems they face in their own code. When…

Code smells are seen as major source of technical debt and, as such, should be detected and removed. However, researchers argue that the subjectiveness of the code smells detection process is a major hindrance to mitigate the problem of…

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…