Saturday 31 May 2025
A new dataset has been created that aims to help software developers better understand and manage a common problem: technical debt. Technical debt refers to shortcuts or quick fixes made in code that can make it difficult to maintain or update later on. This debt can add up quickly, making it harder for developers to make changes or fix bugs.
The new dataset, called CppSATD, contains over 531,000 comments from five popular open-source software projects written in the C++ programming language. These comments were manually annotated by researchers to identify instances of technical debt and classify them into one of five categories: design, code, requirement, defect, or test.
The researchers used a combination of machine learning techniques and natural language processing to analyze the comments and identify patterns that indicate technical debt. They also developed a system to automatically extract relevant information from the comments, such as the type of debt and its severity.
One of the key challenges in managing technical debt is identifying it in the first place. Many developers may not even realize they have debt, or may not know how to prioritize fixing it. The CppSATD dataset aims to change that by providing a large-scale collection of annotated comments that can be used to train machine learning models.
The researchers believe that this dataset will be useful for developers and researchers alike. For developers, it could help them identify technical debt in their own code and make informed decisions about how to address it. For researchers, it provides a rich source of data that can be used to develop new techniques for detecting and managing technical debt.
The CppSATD dataset is the first of its kind, and it has the potential to revolutionize the way we approach software development. By providing a large-scale collection of annotated comments, it could help developers and researchers better understand and manage technical debt, leading to more reliable and maintainable code.
The dataset includes a variety of features that make it useful for research and development. For example, each comment is associated with its surrounding code context, which can provide valuable information about the type and severity of the technical debt. The dataset also includes information about the project itself, such as its size and complexity, which can help researchers understand how different factors affect the presence and impact of technical debt.
In addition to its practical applications, the CppSATD dataset has the potential to advance our understanding of software development and maintenance.
Cite this article: “Dataset for Identifying and Managing Technical Debt in Software Development”, The Science Archive, 2025.
Software, Technical Debt, C++, Programming Language, Open-Source Projects, Machine Learning, Natural Language Processing, Code Comments, Dataset, Software Development, Maintenance
