# CodeFlowLM: Incremental Just-In-Time Defect Prediction with Pretrained Language Models and Exploratory Insights into Defect Localization

arXiv: https://arxiv.org/abs/2512.00231 · HTML: https://mlsanity.com/paper/2512.00231/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 28 Nov 2025.

## Builds on (most cited first, 19 of 19)

- [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)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://mlsanity.com/paper/2203.03850/) (2022)
- [CC2Vec: Distributed Representations of Code Changes](https://mlsanity.com/paper/2003.05620/) (2020)
- [A Deep Dive into Large Language Models for Automated Bug Localization and Repair](https://mlsanity.com/paper/2404.11595/) (2024)
- [Large Language Models in Fault Localisation](https://mlsanity.com/paper/2308.15276/) (2023)
- [JITLine: A Simpler, Better, Faster, Finer-grained Just-In-Time Defect Prediction](https://mlsanity.com/paper/2103.07068/) (2021)
- [A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commits](https://mlsanity.com/paper/2011.06244/) (2020)
- [Delving into Parameter-Efficient Fine-Tuning in Code Change Learning: An Empirical Study](https://mlsanity.com/paper/2402.06247/) (2024)
- [A study on the impact of pre-trained model on Just-In-Time defect prediction](https://mlsanity.com/paper/2309.02317/) (2023)
- [Bridging Expert Knowledge with Deep Learning Techniques for Just-In-Time Defect Prediction](https://mlsanity.com/paper/2403.11079/) (2024)
- [An Empirical Study on JIT Defect Prediction Based on BERT-style Model](https://mlsanity.com/paper/2403.11158/) (2024)

## Abstract (arXiv)

This work introduces CodeFlowLM, an incremental learning framework for Just-In-Time Software Defect Prediction (JIT-SDP) that leverages pre-trained language models (PLMs). Unlike traditional online learners, CodeFlowLM employs continual fine-tuning to address concept drift, class imbalance, and verification latency without retraining from scratch. We evaluated encoder-only and encoder-decoder PLMs (notably CodeT5+ and UniXCoder) in JIT-SDP scenarios within and between projects, comparing them with the incremental baseline BORB. The results show that CodeFlowLM achieves up to 68% G-Mean gains, confirming its superior adaptability and robustness in evolving software environments. We further extend the analysis to Just-in-Time Defect Localization (JIT-DL), benchmarking Large Language Models (LLMs) such as GPT-5, Claude Sonnet 4.5, and Gemini 2.5 Pro against attention-based models. GPT-5 delivers comparable performance for Recall@20% and Effort@20% with higher stability, although attention-based methods retain an advantage in fine-grained ranking metrics (Top-k, IFA). A qualitative error analysis reveals that most false positives arise from (1) human-like conservative bias, (2) insufficient contextual information in diff-based prompts, and (3) potential dataset mislabeling in JIT-Defects4J. These findings highlight both the promise and the current limitations of LLM reasoning in defect localization. False negatives occur in smaller proportions. Overall, CodeFlowLM significantly advances the state of the art in incremental JIT-SDP, demonstrating superior adaptability and robustness in evolving software environments. Furthermore, our exploratory analysis of LLMs in JIT-DL not only benchmarks their performance against established attention-based models but also provides critical insights into the current limitations of prompt-based defect reasoning.