Related papers: A First Look at the Self-Admitted Technical Debt i…
Most Self-Admitted Technical Debt (SATD) research utilizes explicit SATD features such as 'TODO' and 'FIXME' for SATD detection. A closer look reveals several SATD research uses simple SATD ('Easy to Find') code comments without the…
Self-Admitted Technical Debt (SATD) refers to circumstances where developers use textual artifacts to explain why the existing implementation is not optimal. Past research in detecting SATD has focused on either identifying SATD…
Context: Self-admitted technical debt (SATD) occurs when developers acknowledge shortcuts in code. In scientific software (SSW), such debt poses unique risks to the validity and reproducibility of results. Objective: This study aims to…
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…
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,…
The rapid adoption of Deep Learning (DL)-enabled systems has revolutionized software development, driving innovation across various domains. However, these systems also introduce unique challenges, particularly in maintaining software…
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…
Large Language Models (LLMs) are increasingly embedded in software via APIs like OpenAI, offering powerful AI features without heavy infrastructure. Yet these integrations bring their own form of self-admitted technical debt (SATD). In this…
Technical debt, specifically Self-Admitted Technical Debt (SATD), remains a significant challenge for software developers and managers due to its potential to adversely affect long-term software maintainability. Although various approaches…
Self-admitted technical debt refers to situations where a software developer knows that their current implementation is not optimal and indicates this using a source code comment. In this work, we hypothesize that it is possible to develop…
Artificial Intelligence and Machine Learning have witnessed rapid, significant improvements in Natural Language Processing (NLP) tasks. Utilizing Deep Learning, researchers have taken advantage of repository comments in Software Engineering…
To meet project timelines or budget constraints, developers intentionally deviate from writing optimal code to feasible code in what is known as incurring Technical Debt (TD). Furthermore, as part of planning their correction, developers…
Context: Previous research on software aging is limited with focus on dynamic runtime indicators like memory and performance, often neglecting evolutionary indicators like source code comments and narrowly examining legacy issues within the…
Speeding up development may produce technical debt, i.e., not-quite-right code for which the effort to make it right increases with time as a sort of interest. Developers may be aware of the debt as they admit it in their code comments.…
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…
Keeping track of and managing Self-Admitted Technical Debts (SATDs) are important to maintaining a healthy software project. This requires much time and effort from human experts to identify the SATDs manually. The current automated…
Self-Admitted Technical Debt (SATD), a concept highlighting sub-optimal choices in software development documented in code comments or other project resources, poses challenges in the maintainability and evolution of software systems. Large…
Community smells reflect poor organizational practices that often lead to socio-technical issues and the accumulation of Self-Admitted Technical Debt (SATD). While prior studies have explored these problems in general software systems,…
Multi-task learning is a paradigm that leverages information from related tasks to improve the performance of machine learning. Self-Admitted Technical Debt (SATD) are comments in the code that indicate not-quite-right code introduced for…
Technical Debt (TD) refers to the situation where developers make trade-offs to achieve short-term goals at the expense of long-term code quality, which can have a negative impact on the quality of software systems. In the context of code…