Related papers: Crowdsmelling: The use of collective knowledge in …

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: Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous studies have proved their harmfulness for test code maintainability and effectiveness. Therefore, researchers have been proposing…

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…

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…

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…

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…

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.…

Code smell is a great challenge in software refactoring, which indicates latent design or implementation flaws that may degrade the software maintainability and evolution. Over the past of decades, the research on code smell has received…

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…

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…

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…

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,…

The code smell is a sign of design and development flaws in a software system that reduces the reusability and maintainability of the system. Refactoring is done as an ongoing practice to remove the code smell from the program code. Among…

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,…

The low cost and rapid provisioning capabilities have made open-source cloud a desirable platform to launch industrial applications. However, as open-source cloud moves towards maturity, it still suffers from quality issues like code…

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,…

\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…

To reduce technical debt and make code more maintainable, it is important to be able to warn programmers about code smells. State-of-the-art code small detectors use deep learners, without much exploration of alternatives within that…

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…

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…