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Technical debt (TD) refers to delayed tasks and immature artifacts that may bring short-term benefits but incur extra costs of change during maintenance and evolution in the long term. TD has been extensively studied in the past decade, and…

The technical debt (TD) metaphor is widely used to encapsulate numerous software quality problems. She describes the trade-off between the short term benefit of taking a shortcut during the design or implementation phase of a software…

When developers use different keywords such as TODO and FIXME in source code comments to describe self-admitted technical debt (SATD), we refer it as Keyword-Labeled SATD (KL-SATD). We study KL-SATD from 33 software repositories with 13,588…

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,…

Context: Technical lag accumulates when software systems fail to keep pace with technological advancements, leading to a deterioration in software quality. Objective: This paper aims to consolidate existing research on technical lag,…

Fixing bugs is an important phase in software development and maintenance. In practice, the process of bug fixing may conflict with the release schedule. Such confliction leads to a trade-off between software quality and release schedule,…

Research software (also called scientific software) is essential for advancing scientific endeavours. Research software encapsulates complex algorithms and domain-specific knowledge and is a fundamental component of all science. A pervasive…

Background: Technical Debt (TD) describes suboptimal software development practices with long-term consequences, such as defects and vulnerabilities. Deadlines are a leading cause of the emergence of TD in software systems. While multiple…

Technical debt refers to the trade-offs between code quality and faster delivery, impacting future development with increased complexity, bugs, and costs. This study empirically analyzes the additional work effort caused by technical debt…

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…

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: Technical debt (TD) is a widely studied metaphor that helps to explain how sub-optimal decisions that can harm software maintainability over time. Although incurring TD is not intrinsically bad, tracking and managing TD are crucial…

Context: Technical Debt (TD) can be paid back either by those that incurred it or by others. We call the former self-fixed TD, and it can be particularly effective, as developers are experts in their own code and are well-suited to fix the…

Existing software tools enable characterizing and measuring the amount of technical debt at selective granularity levels. In this paper we aim to study the evolution and characteristics of technical debt in open-source software. We carry…

Technical Debt is a term begat by Ward Cunningham to signify the measure of adjust required to put a software into that state which it ought to have had from the earliest starting point. Often organizations need to support continuous and…

NonTechnical Debt (NTD) is a common challenge in agile software development, manifesting in four critical forms, Process Debt, Social Debt, People Debt, Organizational debt. NODLA project is a collaboration between Karlstad University and…

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…

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…

Dockerfiles enable the creation of portable container-based execution environments for the application code, and have become an important part of the modern software development process. As Dockerfiles are a form of Infrastructure-as-Code…

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,…