Boost AI Tool Testing: 5 New DeepEval Test Cases

by Dimemap Team 49 views

Hey everyone! 👋 We're diving deep into the world of AI tool testing and expanding our horizons with some new DeepEval test cases. Right now, we've got a solid foundation with nine existing tests, but we want to make sure we're covering a wider range of tools and scenarios. This article will walk you through the process of adding five new test cases to our suite, ensuring our evaluation process is robust and comprehensive. Let's get started!

The Current Landscape: A Quick Recap

First off, let's take a look at what we've already got. Our current nine test cases are designed to evaluate the performance of various AI tools across different tasks. Here's a quick rundown:

  • platform_news: Tests the obi-expert tool.
  • species_list: Tests the entitycore-species-getall tool.
  • cerebellum_morphologies: Tests the entitycore-brainregion-getall and entitycore-cellmorphology-getall tools.
  • morphology_studies: Tests the literature-search-tool tool.
  • matplotlib_plot: Tests the execute-python-code and plot-generator tools.
  • sin_plot: Tests the execute-python-code and plot-generator tools.
  • thalamus_id: Tests the entitycore-brainregion-getall tool.
  • neuroscientists_search: Tests the web-search-tool tool.
  • simulation_tutorial: Tests the obi-expert tool.

As you can see, we've got a good start, but we can definitely broaden our scope. Our goal is to create a more comprehensive test suite that covers more tools and use cases. The aim is to enhance the accuracy of the AI tools.

The Goal: Expanding Tool Coverage

The primary objective here is to increase the variety of tools tested. The provided graph of tool selections (the image in the prompt) gives us excellent insights into which tools are most frequently used. This helps us prioritize the addition of new tests to cover these popular tools, thereby improving the overall evaluation process. This strategy ensures the most relevant and commonly used tools are thoroughly vetted.

We aim to incorporate at least five new test cases that will specifically target tools not currently covered in our existing tests. This will help make our testing process even more reliable and informative. We're looking at things like web search, code execution, data analysis, and more. This expansion ensures our tools work seamlessly for various needs.

Proposed New Test Cases

Alright, let's get into the meat of it – the new test cases! Here are five new test cases designed to test different tools and functionalities. Each test case will have a specific objective and will target different aspects of the tools' capabilities. We'll outline each test case, the tools it should utilize, and the expected outcomes.

1. Weather Forecast Analysis

  • Objective: Test the ability to fetch and interpret real-time weather data and present it in a user-friendly format.
  • Tools: web-search-tool, data-analysis-tool
  • Description: This test case will query for the current weather conditions in a specific city. The web-search-tool will be used to gather the information, and the data-analysis-tool will process the raw data to provide a concise summary, including temperature, wind speed, and precipitation. It should be able to create an insightful display of the data.
  • Expected Outcome: A clear and accurate summary of the weather data for the specified city. This includes all the basic weather metrics in an easily readable format.

2. Scientific Literature Summarization

  • Objective: Test the ability to find and summarize scientific literature on a given topic.
  • Tools: literature-search-tool, text-summarization-tool
  • Description: The test case will involve searching for scientific articles related to a specific scientific concept. The literature-search-tool will identify relevant articles, and the text-summarization-tool will condense the main points of each article into a brief, informative summary. It must be able to comprehend, analyze, and communicate the findings of scientific publications.
  • Expected Outcome: A concise summary of several scientific articles on the given topic, highlighting the key findings and conclusions.

3. Code Debugging and Explanation

  • Objective: Test the ability to debug and explain the functionality of a given piece of code.
  • Tools: code-execution-tool, code-explanation-tool
  • Description: This test case will provide a snippet of code with a known issue. The code-execution-tool will run the code, identify errors, and the code-explanation-tool will analyze the code, pinpoint the issue, and suggest a solution. It should be able to provide the user with clear instructions, for easy fixing of the code.
  • Expected Outcome: A correct identification of the error, accompanied by a detailed explanation and a suggested fix.

4. Financial Data Analysis and Visualization

  • Objective: Test the ability to fetch, analyze, and visualize financial data.
  • Tools: web-search-tool, data-analysis-tool, plot-generator
  • Description: This test case will fetch financial data (e.g., stock prices) using the web-search-tool. The data-analysis-tool will analyze the data, and the plot-generator will create a visual representation of the data, such as a line chart. It should be able to show the financial data in a visual and understandable way.
  • Expected Outcome: A visual representation of the financial data, such as a chart, which clearly displays the key trends.

5. Multilingual Translation and Contextual Understanding

  • Objective: Evaluate the AI's ability to translate text from one language to another and understand the context.
  • Tools: translation-tool, context-analysis-tool
  • Description: The test will involve translating a text passage from English to another language using the translation-tool. The context-analysis-tool will then evaluate the quality of the translation by understanding the meaning in the original text. It needs to keep the original meaning.
  • Expected Outcome: A correct translation of the text passage into the target language, preserving the original meaning and context.

Implementation Steps

Here’s how we'll implement these test cases:

  1. Define Test Parameters: Clearly define the input, expected output, and evaluation metrics for each test case.
  2. Tool Integration: Ensure that our testing environment is ready to integrate and use the specified tools. This may involve setting up APIs, configurations, and any necessary dependencies.
  3. Create Test Scripts: Develop scripts that execute each test case, send the inputs to the AI tools, and check the output against the expected results.
  4. Implement Evaluation Metrics: Implement the necessary metrics to automatically evaluate the results, determining if the AI's output meets the expected criteria.
  5. Run and Analyze: Run all of the tests, and analyze the results. This will provide valuable feedback on the performance of our tools and where improvements are needed.

Conclusion: The Future of AI Tool Testing

Adding these new test cases is a vital step in improving the robustness and reliability of our AI tools. These updates will let us ensure that our AI tools are accurate, reliable, and able to do the right job in real-world scenarios. We're not just testing; we're building a foundation for innovation and ensuring that AI can reach its full potential. By regularly updating and improving our test suite, we can keep the development of AI tools moving forward, bringing it to a better future. Thanks for reading, and happy testing! 💪