English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Technical debt (TD) represents the long-term costs associated with suboptimaldesign or code decisions in software development, often made to meet short-termdelivery goals. Self-Admitted Technical Debt (SATD) occurs when developersexplicitly acknowledge these trade-offs in the codebase, typically throughcomments or annotations. Automated detection of SATD has become an increasinglyimportant research area, particularly with the rise of natural languageprocessing (NLP), machine learning (ML), and deep learning (DL) techniques thataim to streamline SATD detection.  This systematic literature review provides a comprehensive analysis of SATDdetection approaches published between 2014 and 2024, focusing on the evolutionof techniques from NLP-based models to more advanced ML, DL, andTransformers-based models such as BERT. The review identifies key trends inSATD detection methodologies and tools, evaluates the effectiveness ofdifferent approaches using metrics like precision, recall, and F1-score, andhighlights the primary challenges in this domain, including datasetheterogeneity, model generalizability, and the explainability of models.  The findings suggest that while early NLP methods laid the foundation forSATD detection, more recent advancements in DL and Transformers models havesignificantly improved detection accuracy. However, challenges remain inscaling these models for broader industrial use. This SLR offers insights intocurrent research gaps and provides directions for future work, aiming toimprove the robustness and practicality of SATD detection tools.

Self-Admitted Technical Debt Detection Approaches: A Decade Systematic Review