Improving the detection of technical debt in Java source code with an
  enriched dataset
Technical debt (TD) is a term used to describe the additional work and costs
that emerge when developers have opted for a quick and easy solution to a
problem, rather than a more effective and well-designed, but time-consuming
approach. Self-Admitted Technical Debts (SATDs) are a specific type of
technical debts that developers intentionally document and acknowledge,
typically via textual comments. While these self-admitted comments are a useful
tool for identifying technical debts, most of the existing approaches focus on
capturing crucial tokens associated with various categories of TD, neglecting
the rich information embedded within the source code itself. Recent research
has focused on detecting SATDs by analyzing comments embedded in source code,
and there has been little work dealing with technical debts contained in the
source code. To fill such a gap, in this study, through the analysis of
comments and their associated source code from 974 Java projects hosted in the
Stack corpus, we curated the first ever dataset of TD identified by code
comments, coupled with its associated source code. Through an empirical
evaluation, we found out that the comments of the resulting dataset help
enhance the prediction performance of state-of-the-art SATD detection models.
More importantly, including the classified source code significantly improves
the accuracy in predicting various types of technical debt. In this respect,
our work is two-fold: (i) We believe that our dataset will catalyze future work
in the domain, inspiring various research issues related to the recognition of
technical debt; (ii) The proposed classifiers may serve as baselines for other
studies on the detection of TD by means of the curated dataset.
6731accb45874081fb4e8fdf
Nam Le Hai
claimed_verified
2024-11-15T09:28:12.255Z
63a27e21f30c4642278de3ea
https://cdn-avatars.huggingface.co/v1/production/uploads/63a27e21f30c4642278de3ea/zj0mGDv9DEK9JYHFjfaZq.jpeg
Le Hai Nam
user
NamCyan
6731accb45874081fb4e8fe0
Anh M. T. Bui
6731accb45874081fb4e8fe1
Phuong T. Nguyen
6731accb45874081fb4e8fe2
Davide Di Ruscio
6731accb45874081fb4e8fe3
Rick Kazman
Improving the detection of technical debt in Java source code with an
  enriched dataset
Technical debt (TD) is a term used to describe the additional work and costs
that emerge when developers have opted for a quick and easy solution to a
problem, rather than a more effective and well-designed, but time-consuming
approach. Self-Admitted Technical Debts (SATDs) are a specific type of
technical debts that developers intentionally document and acknowledge,
typically via textual comments. While these self-admitted comments are a useful
tool for identifying technical debts, most of the existing approaches focus on
capturing crucial tokens associated with various categories of TD, neglecting
the rich information embedded within the source code itself. Recent research
has focused on detecting SATDs by analyzing comments embedded in source code,
and there has been little work dealing with technical debts contained in the
source code. To fill such a gap, in this study, through the analysis of
comments and their associated source code from 974 Java projects hosted in the
Stack corpus, we curated the first ever dataset of TD identified by code
comments, coupled with its associated source code. Through an empirical
evaluation, we found out that the comments of the resulting dataset help
enhance the prediction performance of state-of-the-art SATD detection models.
More importantly, including the classified source code significantly improves
the accuracy in predicting various types of technical debt. In this respect,
our work is two-fold: (i) We believe that our dataset will catalyze future work
in the domain, inspiring various research issues related to the recognition of
technical debt; (ii) The proposed classifiers may serve as baselines for other
studies on the detection of TD by means of the curated dataset.
A dataset of self-admitted technical debt comments coupled with source code enhances prediction accuracy in detecting technical debt types.
technical debt self-admitted technical debts SATDs source code Stack corpus technical debt detection state-of-the-art technical debt types baselines