# A Defect is Being Born: How Close Are We? A Time Sensitive Forecasting Approach

arXiv: https://arxiv.org/abs/2601.01921 · HTML: https://mlsanity.com/paper/2601.01921/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 67 references that are in the map.
- First posted on arXiv 5 Jan 2026.

## Builds on (most cited first, 12 of 12)

- [Chronos: Learning the Language of Time Series](https://mlsanity.com/paper/2403.07815/) (2024)
- [A decoder-only foundation model for time-series forecasting](https://mlsanity.com/paper/2310.10688/) (2023)
- [Unified Training of Universal Time Series Forecasting Transformers](https://mlsanity.com/paper/2402.02592/) (2024)
- [Timer: Generative Pre-trained Transformers Are Large Time Series Models](https://mlsanity.com/paper/2402.02368/) (2024)
- [TimeGPT-1](https://mlsanity.com/paper/2310.03589/) (2023)
- [Leveraging the Defects Life Cycle to Label Affected Versions and Defective Classes](https://mlsanity.com/paper/2011.05531/) (2020)
- [A Systematic Review of Unsupervised Learning Techniques for Software Defect Prediction](https://mlsanity.com/paper/1907.12027/) (2019)
- [What Were You Thinking? An LLM-Driven Large-Scale Study of Refactoring Motivations in Open-Source Projects](https://mlsanity.com/paper/2509.07763/) (2025)
- [Evaluating Time-Dependent Methods and Seasonal Effects in Code Technical Debt Prediction](https://mlsanity.com/paper/2408.08095/) (2024)
- [Predicting the Number of Reported Bugs in a Software Repository](https://mlsanity.com/paper/2104.12001/) (2021)
- [Network Centrality as a New Perspective on Microservice Architecture](https://mlsanity.com/paper/2501.13520/) (2025)
- [Continuous Defect Prediction: The Idea and a Related Dataset](https://mlsanity.com/paper/1703.04142/) (2017)

## Abstract (arXiv)

Background. Defect prediction has been a highly active topic among researchers in the Empirical Software Engineering field. Previous literature has successfully achieved the most accurate prediction of an incoming fault and identified the features and anomalies that precede it through just-in-time prediction. As software systems evolve continuously, there is a growing need for time-sensitive methods capable of forecasting defects before they manifest. Aim. Our study seeks to explore the effectiveness of time-sensitive techniques for defect forecasting. Moreover, we aim to investigate the early indicators that precede the occurrence of a defect. Method. We will train multiple time-sensitive forecasting techniques to forecast the future bug density of a software project, as well as identify the early symptoms preceding the occurrence of a defect. Expected results. Our expected results are translated into empirical evidence on the effectiveness of our approach for early estimation of bug proneness.