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DevOps-Eval is a comprehensive evaluation suite specifically designed for foundation models in the DevOps field. We hope DevOps-Eval could help developers, especially in the DevOps field, track the progress and analyze the important strengths/shortcomings of their models.

📚 This repo contains questions and exercises related to DevOps, including the AIOps, ToolLearning;

💥️ There are currently 7486 multiple-choice questions spanning 8 diverse general categories, as shown below.

🔥 There are a total of 2840 samples in the AIOps subcategory, covering scenarios such as log parsing, time series anomaly detection, time series classification, time series forecasting, and root cause analysis.

🔧 There are a total of 1509 samples in the ToolLearning subcategory, covering 239 tool scenes across 59 fields.

• [2023.12.27] Add 1509 ToolLearning samples, covering 239 tool categories across 59 fields; Release the associated evaluation leaderboard;
• [2023.11.27] Add 487 operation scene samples and 640 time series forecasting samples; Update the Leaderboard;
• [2023.10.30] Add the AIOps Leaderboard.
• [2023.10.25] Add the AIOps samples, including log parsing, time series anomaly detection, time series classification and root cause analysis.
• [2023.10.18] Update the initial Leaderboard...

• 🏆 Leaderboard
  • 👀 DevOps
  • 🔥 AIOps
  • 🔧 ToolLearning
• ⏬ Data
  • 👀 Notes
  • 🔥 AIOps Sample Example
  • 🔧 ToolLearning Sample Example
• 🚀 How to Evaluate
• 🧭 TODO
• 🏁 Licenses
• 😃 Citation
• 🗂 Miscellaneous
  • 📱 Contact Us
  • ✨ Star History
  • 🤝 Friendship Links

Below are zero-shot and five-shot accuracies from the models that we evaluate in the initial release. We note that five-shot performance is better than zero-shot for many instruction-tuned models.

• Method 1: Download the zip file (you can also simply open the following link with the browser):

  wget https://huggingface.co/datasets/codefuse-admin/devopseval-exam/resolve/main/devopseval-exam.zip
  

  then unzip it and you may load the data with pandas:

  import os
  import pandas as pd
  
  File_Dir="devopseval-exam"
  test_df=pd.read_csv(os.path.join(File_Dir,"test","UnitTesting.csv"))
  

• Method 2: Directly load the dataset using Hugging Face datasets:

  from datasets import load_dataset
  dataset=load_dataset(r"DevOps-Eval/devopseval-exam",name="UnitTesting")
  
  print(dataset['val'][0])
  # {"id": 1, "question": "单元测试应该覆盖以下哪些方面？", "A": "正常路径", "B": "异常路径", "C": "边界值条件"，"D": 所有以上，"answer": "D", "explanation": ""}  ```

• Method 3: Directly load the datase t using ModelScope datasets:

  from modelscope.msdatasets import MsDataset
  MsDataset.clone_meta(dataset_work_dir='./xxx', dataset_id='codefuse-ai/devopseval-exam')

To facilitate usage, we have organized the category name handlers and English/Chinese names corresponding to 55 subcategories. Please refer to category_mapping.json for details. The format is:

{
  "UnitTesting.csv": [
    "unit testing",
    "单元测试",
    {"dev": 5, "test": 32}
    "TEST"
  ],
  ...
  "file_name":[
  "English Name",
  "Chinese Name",
  "Sample Number",
  "Supercatagory Label(PLAN,CODE,BUILD,TEST,RELEASE,DEPOLY,OPERATE,MONITOR choose 1 out of 8)"
  ]
}

Each subcategory consists of two splits: dev and test. The dev set per subcategory consists of five exemplars with explanations for few-shot evaluation. And the test set is for model evaluation. Labels on the test split are also released.

Below is a dev example from 'version control':

id: 4
question: 如何找到Git特定提交中已更改的文件列表？
A: 使用命令 `git diff --name-only SHA`
B: 使用命令 `git log --name-only SHA`
C: 使用命令 `git commit --name-only SHA`
D: 使用命令 `git clone --name-only SHA`
answer: A
explanation: 
分析原因：
git diff --name-only SHA命令会显示与SHA参数对应的提交中已修改的文件列表。参数--name-only让命令只输出文件名，而忽略其他信息。其它选项中的命令并不能实现此功能。

👀 👀 Taking log parsing and time series anomaly detection as examples, here is a brief showcase of the AIOps samples:

LogParsing

id: 0
question:
Here are some running logs
 0 04:21:15,429 WARN Cannot open channel to 2 at election address /10.10.34.12:3888
 1 19:18:56,377 WARN ******* GOODBYE /10.10.34.11:52703 ********
 2 19:13:46,128 WARN ******* GOODBYE /10.10.34.11:52308 ********
 3 19:16:26,268 WARN ******* GOODBYE /10.10.34.11:52502 ********
 4 09:11:16,012 WARN Cannot open channel to 3 at election address /10.10.34.13:3888
 5 16:37:13,837 WARN Cannot open channel to 2 at election address /10.10.34.12:3888
 6 09:09:16,008 WARN Cannot open channel to 3 at election address /10.10.34.13:3888
 7 15:27:03,681 WARN Cannot open channel to 3 at election address /10.10.34.13:3888
The first three parts of the log are index, timestamp, and log level. Without considering these three parts, Here we assume that the variables in the logs are represented as '<*>', separated by spaces between tokens. What is the specific log template for the above logs? 
A: Notification time out: <*> 和 Connection broken for id <*>, my id = <*>, error =
B: Send worker leaving thread 和 Connection broken for id <*>, my id = <*>, error =
C: Received connection request /<*>:<*> 和 Interrupting SendWorker
D: Cannot open channel to <*> at election address /<*>:<*> 和 ******* GOODBYE /<*>:<*> ********
answer: D
explanation: The log includes the fixed template fragments "Cannot open channel to <> at election address /<>:<>" and "****** GOODBYE /<>:<> ********," both of which appear in option D. Meanwhile, the template fragments in the other options do not match the content in the log. Therefore, option D is the most consistent with the log template.

TimeSeriesAnomalyDetection

id: 0
question:
Analyze the following time series
[50,62,74,84,92,97,99,98,94,87,77,65,265,40,28,17,8,3,0,0,4,10,20,31,43,56,68,79,89,95,99,99,96,91,82,71,59,46,34,22,12,5,1,0,2,7,15,25,37,49]
Please identify the indices of obvious outlier points. Outlier points generally refer to points that significantly deviate from the overall trend of the data.
A: 46
B: 0
C: 37
D: 12
answer: D
explanation: According to the analysis, the value 265 in the given time series at 12 o'clock is significantly larger than the surrounding data, indicating a sudden increase phenomenon. Therefore, selecting option D is correct.

👀 👀The data format of ToolLearning samples is compatible with OpenAI's Function Calling.

Please refer to tool_learning_info.md for details.

If you need to test your own huggingface-formatted model, the overall steps are as follows:

1. Write the loader function for the model.
2. Write the context_builder function for the model.
3. Register the model in the configuration file.
4. Run the testing script. If the model does not require any special processing after loading, and the input does not need to be converted to a specific format (e.g. chatml format or other human-bot formats), you can directly proceed to step 4 to initiate the testing.

If the model requires additional processing after loading (e.g. adjusting the tokenizer), you need to inherit the ModelAndTokenizerLoader class in src.context_builder.context_builder_family.py and override the corresponding load_model and load_tokenizer functions. You can refer to the following example:

class QwenModelAndTokenizerLoader(ModelAndTokenizerLoader):
    def __init__(self):
        super().__init__()
        pass
    
    @override
    def load_model(self, model_path: str):
    # Implementation of the method
        pass
    
    @override
    def load_tokenizer(self, model_path: str):
    # Implementation of the method
        pass

If the input needs to be converted to a specific format (e.g. chatml format or other human-bot formats), you need to inherit the ContextBuilder class in src.context_builder.context_builder_family and override the make_context function. This function is used to convert the input to the corresponding required format. An example is shown below:

class QwenChatContextBuilder(ContextBuilder):
    def __init__(self):
        super().__init__()
        
    @override
    def make_context(self, model, tokenizer, query: str, system: str = "hello！"):
    # Implementation of the method
        pass

Go to the model_conf.json file in the conf directory and register the corresponding model name and the loader and context_builder that will be used for this model. Simply write the class names defined in the first and second steps for the loader and context_builder. Here is an example:

{
  "Qwen-Chat": {
  "loader": "QwenModelAndTokenizerLoader",
  "context_builder": "QwenChatContextBuilder"
  }
}

Run the following code to initiate the test:

python src/run_eval.py \
--model_path path_to_model \
--model_name model_name_in_conf \
--model_conf_path path_to_model_conf \
--eval_dataset_list all \
--eval_dataset_fp_conf_path path_to_dataset_conf \
--eval_dataset_type test \
--data_path path_to_downloaded_devops_eval_data \
--k_shot 0

👀 👀 The specific evaluation process is as follows 📖 Evaluate Tutorial

• add AIOps samples.
• add AIOps scenario time series forecasting.
• add ToolLearning samples.
• increase in sample size.
• add samples with the difficulty level set to hard.
• add the English version of the samples.

This project is licensed under the Apache License (Version 2.0).

Please cite our paper if you use our dataset.

Coming Soon...

• Codefuse-ChatBot
  • Codefuse-ChatBot is an open-source AI smart assistant designed to support the software development lifecycle with conversational access to tools, knowledge, and platform integration.
• Awesome AIGC Tutorials
  • Awesome AIGC Tutorials houses a curated collection of tutorials and resources spanning across Large Language Models, AI Painting, and related fields.