title: Improving the detection of technical debt in Java source code with an
    enriched dataset
summary: 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.
paper_authors._id: 6731accb45874081fb4e8fdf
paper_authors.name: Nam Le Hai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-15T09:28:12.255Z
paper_authors._id: 63a27e21f30c4642278de3ea
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63a27e21f30c4642278de3ea/zj0mGDv9DEK9JYHFjfaZq.jpeg
paper_authors.fullname: Le Hai Nam
paper_authors.type: user
paper_authors.user: NamCyan
paper_authors._id: 6731accb45874081fb4e8fe0
paper_authors.name: Anh M. T. Bui
paper_authors._id: 6731accb45874081fb4e8fe1
paper_authors.name: Phuong T. Nguyen
paper_authors._id: 6731accb45874081fb4e8fe2
paper_authors.name: Davide Di Ruscio
paper_authors._id: 6731accb45874081fb4e8fe3
paper_authors.name: Rick Kazman
paper_title: Improving the detection of technical debt in Java source code with an
    enriched dataset
paper_summary: 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.