Test Execution Logs Data for LLM Benchmarking for Root Cause Analysis

The test execution logs are obfuscated to enhance privacy. To replicate our experiment:

Log in through the OpenRouter website, 

Navigate to the “Chat” tab,

Select and add desired language models. In this case: Aion-1.0, DeepSeek R1, DeepSeek V3 0324, Mistral Small 3.1 24B, GPT o3-mini, Gemini 2.5 Pro Experimental, and QwB 32B,

Input the following prompt and the corresponding log (one log at a time) into the chat interface:

You are a debugging assistant. Given the following information:    1. A test execution log2. A list of possible root causes    Your task is to:- Provide a step-by-step reasoning to analyze the failure- Select the most likely root cause (only one) from the list- Suggest a reasonable action or next step to resolve it- Output the result in JSON format as shown below    ---Log File:{"<log-file-name>"}    Possible Root Causes:{ "misconfiguration", "signal_timeout", "test_aborted", "expired_bugfix", "unknown" }    Respond strictly in the following JSON format:{{   "File name": "{file_name}",   "Step-by-steps reasoning": "1. ...\n2. ...\n3. ...",   "Root cause": "<select one from above>",   "Action suggestion": "..."}}

Lastly, read the response directly in the chat box, or export it to JSON for later analysis.

Note:

Sometimes, one of the language models fail to generate the response. In this case, a retry is needed until the language model successfully generate the response.