How AI Helped Me in Unit Testing with Test Data Generation

As developers, we often find ourselves in need of generating large sets of test data to ensure our implementations are robust and error-free. The task can be tedious and time-consuming, especially when the data needs to be random and unique. However, I recently discovered a surprisingly efficient solution to this problem through an unexpected source: ChatGPT.

The Challenge: Generating Massive Test Data

I had just finished working on an implementation that required extensive testing. To test the functionality effectively, I needed to create 200 records of test data, each with a unique and random friendly-id and id. The pattern I needed was specific: <p friendly-id="doc_test_0" id="xc_pt_qw"/>. Manually creating such a dataset would have been a daunting task, and I was looking for a way to save time and automate the process.

The Ask: A Simple Request to ChatGPT

With the hope of speeding up the process, I turned to ChatGPT. I asked it to generate 200 records of test data following the pattern I provided. I was expecting a straightforward response with the data I requested, but what I got was beyond my expectations.

The Solution: A Custom JavaScript Function

Instead of simply providing the data, ChatGPT supplied me with a JavaScript function capable of generating the exact output I needed. The function was designed to create random strings for each friendly-id and id, then construct the HTML tags accordingly. Here's a snippet of the function provided:

function generateRandomTags(count) {
  const tags = [];
  for (let i = 0; i < count; i++) {
    const friendlyId = `doc_test_${Math.floor(Math.random() * 10000)}`;
    const id = `${Math.random().toString(36).substring(2, 6)}_${Math.random().toString(36).substring(2, 6)}_${Math.random().toString(36).substring(2, 6)}`;
    tags.push(`<p friendly-id="${friendlyId}" id="${id}"></p>`);
  }
  return tags.join('\n');
}

With this function, I could generate any number of <p> tags with random and unique friendly-id and id attributes. The friendly-id would start with "doc_test_" followed by a random number, and the id would be a random alphanumeric string separated by underscores.

The Outcome: Instant Test Data Generation

I ran the function to generate 200 random <p> tags, and it worked like a charm. The output was exactly what I needed, and it was generated in an instant. The tags were unique, as required, and I could proceed with my testing without any further delay.

The Reflection: AI as a Developer's Assistant

This experience was a testament to the potential of AI as a tool for developers. ChatGPT didn't just provide me with the data; it provided me with a reusable solution that I could adapt for future needs. It acted like a human brain, understanding my request and providing a smart, efficient answer.

Conclusion

The use of AI in software development is not just about automating tasks; it's about enhancing our capabilities and providing us with tools that can adapt to our needs. ChatGPT's response was a perfect example of this, saving me time and effort by generating the test data I needed in a way that was both intelligent and practical. As AI continues to evolve, I look forward to discovering more ways it can assist us in our daily coding challenges.

If you're a developer looking for ways to streamline your testing process or just curious about the practical applications of AI, feel free to reach out or comment below. Let's explore the possibilities together!