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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,…
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…
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…
Technical debt refers to suboptimal code that degrades software quality. When developers intentionally introduce such debt, it is called self-admitted technical debt (SATD). Since SATD hinders maintenance, identifying its categories is key…
Context: Previous studies demonstrate that Machine or Deep Learning (ML/DL) models can detect Technical Debt from source code comments called Self-Admitted Technical Debt (SATD). Despite the importance of ML/DL in software development,…
Self-admitted technical debt (SATD) impairs scientific software (SSW), yet its prioritization, sentiment, persistence, and propagation remains underexplored. Understanding how SSW developers express, and address SATD is crucial for…
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…
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…
In software engineering, technical debt, signifying the compromise between short-term expediency and long-term maintainability, is being addressed by researchers through various machine learning approaches. This study seeks to provide a…
Technical debt refers to taking shortcuts to achieve short-term goals while sacrificing the long-term maintainability and evolvability of software systems. A large part of technical debt is explicitly reported by the developers themselves;…
Software systems naturally evolve, and this evolution often brings design problems that cause system degradation. Architectural smells are typical symptoms of such problems, and several of these smells are related to undesired dependencies…
Self-Admitted Technical Debt, or SATD, is a self-admission of technical debt present in a software system. To effectively manage SATD, developers need to estimate its priority and assess the effort required to fix the described technical…
Self-Admitted Technical Debt (SATD) is a form of Technical Debt where developers document the debt using source code comments (SATD-C) or issues (SATD-I). However, it is still unclear the circumstances that drive developers to choose one or…
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…
Upon evolving their software, organizations and individual developers have to spend a substantial effort to pay back technical debt, i.e., the fact that software is released in a shape not as good as it should be, e.g., in terms of…
Technical debt describes situations where developers write less-than-optimal code to meet project milestones. However, this debt accumulation often results in future developer effort to live with or fix these quality issues. To better…
Technical Debt (TD) refers to non-optimal decisions made in software projects that may lead to short-term benefits, but potentially harm the system's maintenance in the long-term. Technical debt management (TDM) refers to a set of…
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…
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…
Context: Technical Debt (TD) discusses the negative impact of sub-optimal decisions to cope with the need-for-speed in software development. Code Technical Debt Items (TDI) are atomic elements of TD that can be observed in code artefacts.…