Generating Test Cases for Code Blocks using GPT-4: A Guide

 Introduction

        In recent years, the field of artificial intelligence has made remarkable strides, and GPT-4 is one such innovation. GPT-4, short for Generative Pre-trained Transformer 4, is a state-of-the-art language model developed by OpenAI. It has been widely adopted in various applications, including text generation, summarization, translation, and more. In this blog post, we will explore how GPT-4 can be utilized to generate test cases for code blocks, an essential aspect of software engineering, especially in a production machine learning system. We will provide examples, discuss the strengths and weaknesses of this approach, and demonstrate how GPT-4 can be a valuable tool in the movie streaming scenario.

        Utilizing ChatGPT as a test case generator addresses the challenge of efficiently and effectively testing machine learning pipelines. In the rapidly evolving world of AI, it is crucial to ensure that ML models and their associated pipelines are thoroughly tested to prevent errors and maintain robustness. However, the manual creation of test cases can be time-consuming, labor-intensive, and often prone to human oversight. By leveraging ChatGPT for test case generation, developers can alleviate these pain points and accelerate the testing process. ChatGPT can quickly generate a wide range of test cases, covering different scenarios and edge cases that may be otherwise missed by human developers. Additionally, its adaptability across various domains makes it a versatile tool for generating test cases in different use cases. By automating test case generation with ChatGPT, the overall efficiency and reliability of machine learning pipelines can be improved, leading to more accurate and dependable AI-enabled systems. 

        The tests in this blog were written using the MAR23 Chat GPT version, with the GPT-4 language model selected. This is currently (as of 03APR23) only available as a paid service via OpenAI’s ChatGPT Plus subscription. However, it is widely expected that this technology will soon be available more broadly, either from OpenAI or via a Microsoft product like GitHub CoPilot or Bing Search.

The Problem: Generating Test Cases

        Test case generation is a crucial part of the software development process, as it helps ensure that the code functions as expected and meets the requirements. Writing test cases can be time-consuming and error-prone, especially when dealing with complex code blocks or ever-evolving requirements. This is where GPT-4 can come into play, offering a powerful solution to generate test cases by understanding the context and functionality of the given code blocks.

        As a first test, I explored a common example in unit-testing scenarios: a “next date” function:

        I then asked "write a series of unit tests that ensures the above function works on corner cases" and received the following response: 

         The test cases cover a normal day, an end-of-month changeover, a leap day on a leap year, leap day on a non leap year, leap day to March 1st, and the new year rollover. While not fully comprehensive, this represents a good "proof of concept". 

Using GPT-4 in Test Case Generation: Movie Streaming Scenario

    Let's consider a movie streaming scenario where the recommendation system is responsible for suggesting movies to users based on their preferences. The recommendation algorithm takes user history, movie ratings, and other factors into account to generate a list of recommended movies.

    To generate test cases for this code block using GPT-4, we can provide it with a brief description of the recommendation system's functionality and some examples of expected inputs and outputs. Based on this information, GPT-4 can generate a set of test cases that cover various edge cases and scenarios to ensure the recommendation system functions as intended.

    Consider the below function from the data pipeline, where databases, saved as CSV files and read into Pandas Dataframes, are examined for duplicate primary key values and, if any are returned, have their values logged and duplicate rows removed.

When prompted to "write some test cases for the above function", the model produced the following 5 cases: 

1. Test case for an empty DataFrame:
   
   
   This test case did not work- throws a KeyError as the empty DataFrame does not have a column "A". 
   
   
2. Test case for a DataFrame with unique values:
   
   
   Works as expected.
   
   
3. Test case for a DataFrame with duplicate values:
   
   
   Function fails this test case because the index is not reset by the function so the DataFrames are different. Once I corrected this mistake, passed the test.
   
   
4. Test case for a DataFrame with all identical values:
   
   
   Passed once the correction for #3 above was made. 
   
   
5. Test case for a DataFrame with multiple columns and duplicate values in the specified column:
   
   

   Works as expected

Let's consider a more complicated case. I have a file consisting of raw JSON outputs retrieved from a movie information API. They look like the following: 

I have written a function to process these hastily-stored information into properly tabular data: 

                    

I prompted GPT-4 for test cases by pasting in the read_formatted_movies function, saying "write test cases for the above function. The input is a CSV with column name "JSONString" where each entry is similar to the below example:" and then including a paste of the JSON output above. 

This produced two tests: 

Interestingly, GPT-4 seems to have mixed up the test cases. It says that "test_invalid_json" will throw a JSONDecodeError, but in fact the test_valid_input thows that error. Still, with minor adjustments these are useful tests cases. I was particularly impressed that it recognized that the functions would need to write a test CSV to work appropriately, and that it was nice enough to remove them after completion. 

Strengths of GPT-4 for Test Case Generation

• Time-saving: GPT-4 can significantly reduce the time it takes to generate test cases, as it can quickly understand the context and requirements of the code block.
• Comprehensive coverage: GPT-4 can generate a wide range of test cases, covering edge cases and scenarios that might be overlooked by human developers.
• Continuous improvement: As GPT-4 is trained on an extensive corpus of text, it continuously improves its understanding and generation capabilities, providing better test cases over time.
• Adaptability: GPT-4 can be easily adapted to different domains, making it suitable for generating test cases in various use cases. 

Weaknesses of GPT-4 for Test Case Generation

• Incomplete test coverage: GPT-4 is not guaranteed to generate test cases that cover all possible edge cases, branches, or paths in the code. It might miss important scenarios that a human tester or specialized test generation tool would identify. 
• Uncertainty in output quality: GPT-4's generated test cases may sometimes be incorrect or not make sense in the context of the code under test. This could result in false positives or negatives during the testing process. 
• Bias in generated test data: GPT-4 may unintentionally introduce biases in the generated test cases, as it is trained on a vast dataset that contains biases present in human-generated text. This could lead to test cases that are biased or not representative of real-world scenarios. 
• Limited understanding of code semantics: GPT-4 is a language model and might not fully understand the semantics and intricacies of the code it is generating test cases for. This limitation can result in test cases that do not adequately test the code's functionality or miss crucial aspects of the code. One example of this is mathematical or arithmetic functions. GPT-4 might struggle to write appropriate test cases in the same way that it struggles with arthimatic generally. 
• Resource consumption: Generating test cases using GPT-4 can be computationally expensive, especially for large or complex codebases. It may not be suitable for situations where resource constraints are a concern, or rapid test case generation is required.

Despite these limitations, GPT-4 can still be a valuable tool for test case generation, particularly when used in combination with other testing techniques and methodologies. By carefully considering the weaknesses and strengths of GPT-4, developers can harness its capabilities to improve their testing process and create more robust software systems.