# Enhancing Interpretability in Software Change Management with Chain-of-Thought Reasoning

arXiv: https://arxiv.org/abs/2507.09315 · HTML: https://mlsanity.com/paper/2507.09315/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 11 references that are in the map.
- First posted on arXiv 12 Jul 2025.

## Built on by (most cited first, 1 of 1)

- [R-Log: Incentivizing Log Analysis Capability in LLMs via Reasoning-based Reinforcement Learning](https://mlsanity.com/paper/2509.25987/) (2025)

## Abstract (arXiv)

In modern online services, frequent software changes introduce significant risks. To tackle this challenge, we propose SCELM (Software Change Evaluation and Lifecycle Management), an end-to-end automated framework for software change management. SCELM aims to manage software changes efficiently and precisely, significantly reducing service failures and economic losses.