Related papers: Detecting Discussions of Technical Debt
Self-Admitted Technical Debt (SATD) is a metaphorical concept to describe the self-documented addition of technical debt to a software project in the form of source code comments. SATD can linger in projects and degrade source-code quality,…
The adoption of Machine and Deep Learning (ML/DL) technologies introduces maintenance challenges, leading to Technical Debt (TD). Algorithm Debt (AD) is a TD type that impacts the performance and scalability of ML/DL systems. A review of 42…
A vigorous and growing set of technical debt analysis tools have been developed in recent years -- both research tools and industrial products -- such as Structure 101, SonarQube, and DV8. Each of these tools identifies problematic files…
Blockchain is a disruptive technology intended at implementing secure decentralized distributed systems, in which transactional data can be shared, stored and verified by participants of a system using cryptographic and consensus…
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 special form of technical debt in which developers intentionally record their hacks in the code by adding comments for attention. Here, we focus on issue-related "On-hold SATD", where developers…
Software analytics can be improved by surveying; i.e. rechecking and (possibly) revising the labels offered by prior analysis. Surveying is a time-consuming task and effective surveyors must carefully manage their time. Specifically, they…
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…
Self-Admitted Technical Debt (SATD) refers to instances where developers knowingly introduce suboptimal solutions into code and document them, often through textual artifacts. This paper provides a comprehensive state-of-practice report on…
Context: Advances in technical debt research demonstrate the benefits of applying the financial debt metaphor to support decision-making in software development activities. Although decision-making during requirements engineering has…
A systems quality is a major concern for development teams when it evolve. Understanding the effects of a loss of quality in the codebase is crucial to avoid side effects like the appearance of technical debt. Although the identification of…
Technical Debt (TD) refers to the long-term costs incurred when developers prioritize short-term delivery over quality-improving work. Architectural Technical Debt (ATD) arises when architectural decisions (e.g., technology choices,…
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,…
Technical debt is a well-known challenge in software development, and its negative impact on software quality, maintainability, and performance is widely recognized. In recent years, artificial intelligence (AI) has proven to be a promising…
In the process of software evolution, developers often sacrifice the long-term code quality to satisfy the short-term goals due to specific reasons, which is called technical debt. In particular, self-admitted technical debt (SATD) refers…
Recognizing that technical debt is a persistent and significant challenge requiring sophisticated management tools, TD-Suite offers a comprehensive software framework specifically engineered to automate the complex task of its…
Self-admitted technical debt (SATD), referring to comments flagged by developers that explicitly acknowledge suboptimal code or incomplete functionality, has received extensive attention in machine learning (ML) and traditional (Non-ML)…
As large language models (LLMs) such as ChatGPT, Copilot, Claude, and Gemini become integrated into software development workflows, developers increasingly leave traces of AI involvement in their code comments. Among these, some comments…
Quantum computing is a rapidly growing field attracting the interest of both researchers and software developers. Supported by its numerous open-source tools, developers can now build, test, or run their quantum algorithms. Although the…
Self-Admitted Technical Debt (SATD) encompasses a wide array of sub-optimal design and implementation choices reported in software artefacts (e.g., code comments and commit messages) by developers themselves. Such reports have been central…