Self-Admitted Technical Debt Detection Approaches: A Decade Systematic Review
📰 ArXiv cs.AI
Learn how to detect self-admitted technical debt in software development using approaches from a decade of research, and improve code quality by identifying suboptimal design or code decisions
Action Steps
- Conduct a systematic review of SATD detection approaches
- Apply machine learning-based methods to identify SATD in codebases
- Analyze comments and annotations to detect explicit acknowledgments of technical debt
- Use natural language processing techniques to improve SATD detection accuracy
- Integrate SATD detection into existing software development workflows
Who Needs to Know This
Software engineers, developers, and technical debt managers can benefit from understanding SATD detection approaches to improve code maintainability and reduce long-term costs
Key Insight
💡 Explicit acknowledgments of technical debt in code comments and annotations can be used to detect self-admitted technical debt
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🚨 Detect self-admitted technical debt in your codebase with machine learning and NLP! 🚨
Key Takeaways
Learn how to detect self-admitted technical debt in software development using approaches from a decade of research, and improve code quality by identifying suboptimal design or code decisions
Full Article
Title: Self-Admitted Technical Debt Detection Approaches: A Decade Systematic Review
Abstract:
arXiv:2312.15020v4 Announce Type: replace-cross Abstract: Technical debt (TD) refers to the long-term costs associated with suboptimal design or code decisions in software development, often made to meet short-term delivery goals. Self-Admitted Technical Debt (SATD) occurs when developers explicitly acknowledge these trade-offs in the codebase, typically through comments or annotations. SATD detection has become an increasingly important research area, particularly with the rise of learning-base
Abstract:
arXiv:2312.15020v4 Announce Type: replace-cross Abstract: Technical debt (TD) refers to the long-term costs associated with suboptimal design or code decisions in software development, often made to meet short-term delivery goals. Self-Admitted Technical Debt (SATD) occurs when developers explicitly acknowledge these trade-offs in the codebase, typically through comments or annotations. SATD detection has become an increasingly important research area, particularly with the rise of learning-base
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