Generative AI in Software Development: A ChatGPT Code Companion Workflow Experiment

ChatGPT is an AI-powered assistant designed to enhance productivity across various tasks, acting as a supportive collaborator for the user. In the context of software development, ChatGPT, or more particularly the Code Companion feature, excels at generating code snippets, solving technical problems, and offering alternative solutions, making workflows more efficient and streamlined. For developers working on unfamiliar languages/ frameworks or tackling complex algorithms, ChatGPT can help reduce the mental load and the learning curve by providing clear, relevant insights in real time, minimizing the need for extensive searches through Google or Stack Overflow. It can assist in refactoring code, debugging, and testing, and it helps catch subtle errors that might otherwise be overlooked. ChatGPT also supports code annotation and documentation updates, making it an invaluable tool for improving code clarity, enhancing collaboration, and brainstorming solutions for complex development challenges.

So, is it Time to Start Using ChatGPT Code Companion for Development?

As a developer, staying up to date with the latest resources and platforms is essential for success in the field. Recently, I worked on a project that required building an XML file to convey financial information to various parties involved. The database read queries involved intricate joins and group-by operations to calculate sum totals, which then had to be written to the XML file in a specific format. I had to design a database schema capable of supporting these queries and map approximately 30-40 fields from the database onto the XML file, all under a tight deadline as the project was nearing completion. Additionally, I was working in a programming language I didn’t use regularly, as my previous project was focused mostly on DevOps operations and cloud formation scripts. To improve my odds of completing this work on time, I leveraged generative AI in the form of ChatGPT for assistance.

I found ChatGPT’s Code Companion to be incredibly useful, particularly in boosting coding speed, improving logic accuracy, and simplifying complex tasks—all of which greatly enhanced my productivity during the experiment. In the sections that follow, I’ll dive into several use cases where ChatGPT significantly impacted my workflow. I'll share my personal experiences with incorporating generative AI into the software development process.

The XML Structure That Had to be Generated

Code Generation & Refactoring 

One of the first tasks where ChatGPT provided significant value was code generation. It not only accelerated the process but also suggested cleaner, more efficient coding techniques that streamlined my workflow. Instead of having to turn to Google or Stack Overflow for examples, I found ChatGPT's suggestions to be more direct and insightful. It even provided helpful comments explaining the purpose of various code sections, which made understanding, refining, and refactoring the code much more straightforward.

Did I gain speed? Absolutely. For example, mapping the 30-40 XML fields and encapsulating them in an object, a task that would have taken hours, including time spent cross-referencing syntax and checking for errors, was reduced to mere minutes. Code Companion’s ability to generate and refactor my code while offering explanations made the process both faster and more insightful.

Pros: Faster code generation, clean and concise logic updates, reduction in the cognitive load.
Cons: Sometimes, the suggested code examples were not optimal for performance or were outright incorrect, requiring manual refinement.

Testing & Debugging

Testing and debugging can be some of the most time-consuming parts of the software development lifecycle, but ChatGPT made these tasks a lot more manageable. When it came to writing unit and functional tests, Code Companion gave me a great head start by suggesting relevant scenarios based on my code. They weren’t perfect, but they definitely saved me from starting at square one and provided me with the scaffolding needed to get the work going. The AI agent also often highlighted problem areas and offered some potential fixes to the logic errors. That said, I still had to step in and manually verify things, especially when it came to specific edge cases or project requirements that were more complex than the AI could handle.

Pros: ChatGPT gave me a solid foundation for testing and debugging, helping me quickly identify common errors. This saved me a significant amount of time upfront, streamlining my development process.

Cons: Manual intervention was still required at times, especially when the context was misunderstood. In those cases, I had to re-enter prompts to clarify the issue and guide ChatGPT towards the correct solution.

Database Mapping

When I was mapping the database fields to the XML file, Code Companion was incredibly helpful in crafting queries based solely on the table structure I provided. It guided me through writing efficient ORM queries, enabling me to quickly generate the necessary solutions to extract the correct values. One of the more challenging parts of the project involved handling intricate joins and group-by operations across multiple tables to determine total amounts based on a metered rate. I was able to determine these totals directly through the query without the need for additional code to handle the business logic. The AI’s ability to offer optimized solutions for these complex operations drastically reduced the time spent on debugging and query refinement.

Pros: It provided multiple solutions for the same query. This versatility allowed me to explore different approaches to solving the SQL joins and field mappings, making it easier to focus on the bigger picture and choose the most suitable method for my needs. 

Cons: There were instances where the queries generated by ChatGPT produced incorrect results or did not extract the intended data. To address this, I had to manually test the queries to verify their accuracy and ensure they were delivering the expected outcomes.

Documentation Updates

Documentation is often overlooked, especially when deadlines loom. However, ChatGPT has proven to be useful in generating draft documentation, which I could then modify to fit the project’s needs. It helped streamline the process by auto-generating overviews, code explanations, and even inline documentation. Although I still had to review and personalize the generated content, it saved time by creating the initial drafts of project documentation.

Pros: Speedy generation of high-level documentation, easy to adjust for specific requirements.

Cons: Auto-generated content often needs further customization and review.

Tips and Tricks

Here are a few strategies I found helpful while integrating ChatGPT into my workflow:

1. Begin by using ChatGPT for smaller tasks, like refactoring or generating test cases, before relying on it for larger, more critical development tasks.
2. Start manually writing code before accepting ChatGPT suggestions.
3. Don’t assume that ChatGPT’s first suggestion is perfect. Use it as a starting point and refine the results to suit your project.
4. When working on large projects, break down tasks and communicate clearly with the AI to maintain relevant context.
5. Always double-check the suggestions for accuracy, especially when working in languages or systems with strict performance requirements.
6. Consider manually writing an initial unit test before using ChatGPT for the other tests.
7. Use a consistent “Arrange / Act / Assert” structure for unit tests.
8. Write comments for single, short, & specific (3 S’s) tasks you want ChatGPT to perform.
9. Don't be afraid to iterate with ChatGPT’s suggestions by accepting a suggestion and modifying or deleting the irrelevant parts with the autocomplete functionality or providing follow-up clarifications through the chat functionality.

Conclusion: Is the use of Generative AI Worth it for Developers?

The answer is it depends. ChatGPT can be a valuable tool for increasing productivity, especially for code generation, refactoring, testing, debugging, and documentation. However, it’s important to approach its use thoughtfully. While it can save time and effort, you’ll still need to verify the accuracy of its output and refine its suggestions to fit your specific needs.

For developers looking to adopt AI into their workflow, the key is to start small and be agile. Experiment with how AI can enhance your productivity, but always proceed cautiously. AI is another tool in our ever-evolving developer toolkit, and learning to integrate it effectively will be an ongoing process.