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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,…

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…

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.…

\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…

A smell in software source code denotes an indication of suboptimal design and implementation decisions, potentially hindering the code understanding and, in turn, raising the likelihood of being prone to changes and faults. Identifying…

Code smells indicate software design problems that harm software quality. Data-intensive systems that frequently access databases often suffer from SQL code smells besides the traditional smells. While there have been extensive studies on…

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 identification of code smells is largely recognized as a subjective task. Consequently, the automated detection tools available are insufficient to deal with the whole subjectivity involved in the task, requiring human validation.…

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,…

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…

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…

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…

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…

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…

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…

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…

Code review plays an important role in software quality control. A typical review process would involve a careful check of a piece of code in an attempt to find defects and other quality issues/violations. One type of issues that may impact…

Developing test code may be a time-consuming task that usually requires much effort and cost, especially when it is done manually. Besides, during this process, developers and testers are likely to adopt bad design choices, which may lead…

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…

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…