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#small language model #software testing Open access

Let’s read the log: root cause analysis of railway test execution logs with large language models

Rahmanu Hermawan Alessio Bucaioni Eduard Paul Enoiu Wasif Afzal Mehrdad Saadatmand Nedim Zaimovic Md Saleh Ibtasham
Aug 2026 · Innovations in Systems and Software Engineering · Vol 22 · 0 citations · 46 references

TL;DR

Results showed that long-context LLMs tended to achieve higher accuracy than smaller models, suggesting that LLMs are currently better suited to support human-in-the-loop root cause analysis than to fully automate it, and motivating further work to improve prediction accuracy for log-based RCA.

Abstract

Software quality assurance is pivotal in safety-critical domains such as railway systems, where failures could have catastrophic consequences. In this context, the train control and management system, which enables communication and control across multiple subsystems (such as doors and information panels) within a modern train, and its software must undergo rigorous validation. Alstom Rail Sweden AB employs a digital twin infrastructure to simulate and validate train control and management system software. While this setup improves system-level testing, root-cause analysis of test failures remains a manual, time-consuming bottleneck. In this study, we explore the potential of large language models to automate root cause analysis by interpreting test execution logs generated during digital twin-based testing. We benchmark nine state-of-the-art large language models: Aion−1.0, DeepSeek R1, DeepSeek V3 0324, Mistral Small 3.1 24B, GPT o3-mini, Gemini 2.5 Pro Experimental, QwQ 32B, Gemini 2.0 Flash Experimental, and Amazon Nova 2 Lite using zero-shot chain-of-thought prompting to assess their ability to reason about fault patterns in real-world industrial test execution logs. The logs were sourced from Alstom’s digital twin-based testing environment and captured complex operational behaviour typical of embedded, safety-critical systems. Our results showed that long-context LLMs tended to achieve higher accuracy than smaller models. Gemini 2.5 Pro Experimental achieved the best performance with 66.7% accuracy and produced coherent step-by-step explanations. However, coherent reasoning did not consistently translate into correct root-cause identification, suggesting that LLMs are currently better suited to support human-in-the-loop root cause analysis than to fully automate it, and motivating further work to improve prediction accuracy for log-based RCA.

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