# TD-Suite: All Batteries Included Framework for Technical Debt Classification

arXiv: https://arxiv.org/abs/2504.11085 · HTML: https://mlsanity.com/paper/2504.11085/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [The Technical Debt Dataset](https://mlsanity.com/paper/1908.00827/) (2019)
- [Automatic Detection and Analysis of Technical Debts in Peer-Review Documentation of R Packages](https://mlsanity.com/paper/2201.04241/) (2022)
- [Technical Debt in the Peer-Review Documentation of R Packages: a rOpenSci Case Study](https://mlsanity.com/paper/2103.09340/) (2021)
- [Self-Admitted Technical Debt Detection Approaches: A Decade Systematic Review](https://mlsanity.com/paper/2312.15020/) (2023)
- [Systematic literature review on forecasting and prediction of technical debt evolution](https://mlsanity.com/paper/2406.12026/) (2024)
- [Exploring the Advances in Using Machine Learning to Identify Technical Debt and Self-Admitted Technical Debt](https://mlsanity.com/paper/2409.04662/) (2024)

## Abstract (arXiv)

Recognizing that technical debt is a persistent and significant challenge requiring sophisticated management tools, TD-Suite offers a comprehensive software framework specifically engineered to automate the complex task of its classification within software projects. It leverages the advanced natural language understanding of state-of-the-art transformer models to analyze textual artifacts, such as developer discussions in issue reports, where subtle indicators of debt often lie hidden. TD-Suite provides a seamless end-to-end pipeline, managing everything from initial data ingestion and rigorous preprocessing to model training, thorough evaluation, and final inference. This allows it to support both straightforward binary classification (debt or no debt) and more valuable, identifying specific categories like code, design, or documentation debt, thus enabling more targeted management strategies. To ensure the generated models are robust and perform reliably on real-world, often imbalanced, datasets, TD-Suite incorporates critical training methodologies: k-fold cross-validation assesses generalization capability, early stopping mechanisms prevent overfitting to the training data, and class weighting strategies effectively address skewed data distributions. Beyond core functionality, and acknowledging the growing importance of sustainability, the framework integrates tracking and reporting of carbon emissions associated with the computationally intensive model training process. It also features a user-friendly Gradio web interface in a Docker container setup, simplifying model interaction, evaluation, and inference.