ChatGPT debugged my code for real.

The Art and Science of Debugging: Unleashing the Power of ChatGPT

Programming is like playing a constant game of mental Jenga. The tower of code you build, one line upon another, must be robust enough not to come crashing down. But, as any programmer knows, code never works the first time it’s run. That’s where the art and science of debugging come in.

Debugging is the process of finding why code isn’t running or is behaving unexpectedly. It’s like being a detective, searching for clues and deciphering what they’re trying to tell you. It can be both frustrating and deeply satisfying at the same time.

Debugger is a programmer’s best friend. It not only helps identify the flaws in the code but also provides insights into how the code is running. Tweak after tweak, the debugger leads programmers towards code that actually works.

Real-world Testing with ChatGPT

Last week, I found myself facing three coding tasks for software I maintain. Two were bug fixes reported by users, and one involved adding a new feature. These tasks were all part of my regular work schedule, so I decided to put ChatGPT to the test. I wanted to see if this AI-powered tool could assist me in my day-to-day programming work.

Up until now, I had only tested ChatGPT with artificially created scenarios. But this time, it was different. I wanted to see if ChatGPT could be a useful tool in solving real-world coding problems. After all, real-world coding involves troubleshooting customer support tickets and understanding why the user experience went south.

So, let’s dive into those tasks and see how ChatGPT performed.

Rewriting Regular Expression Code

When it comes to coding, finding patterns in text often requires using regular expressions. Writing regular expressions has never been a favorite task for programmers, as it’s tiring and prone to errors. So, when I received a bug report informing me that a part of my code was only allowing integers when it should allow for dollars and cents, I knew I would have to deal with regular expressions.

Since I find this process tedious and annoying, I turned to ChatGPT for assistance. I asked ChatGPT for help, and it provided well-presented and precise code. I effortlessly incorporated ChatGPT’s code into my program, saving myself hours of hair-pulling frustration.

Reformatting an Array

Next on my agenda was reformatting an array. While I enjoy working with array code, it can still be tedious. Once again, I turned to ChatGPT for support. However, this time, it was a total failure. Despite trying different prompts and receiving promising responses, the code generated by ChatGPT either crashed, produced errors, or didn’t yield the desired results.

Eventually, I gave up and resorted to my usual technique of digging through GitHub and StackExchange for examples and writing my own code. This experience highlighted the limitations of ChatGPT’s debugging capabilities in complex scenarios.

Actually Finding the Error in My Code

Now, this next bit is a bit challenging to explain. Imagine trying to explain a coding problem to an artificial intelligence. It can be just as difficult as explaining it to a human, if not more so.

I encountered an error while writing new code. The error message indicated that there was a mismatch between the number of parameters passed and the expected number of parameters. This confusion prompted me to turn to ChatGPT for help.

I showed ChatGPT the code that caused the error, including the calling statement, the function itself, and the corresponding handler. In just seconds, ChatGPT provided a diagnosis: I needed to update the fourth parameter of the add_filter() function to 2. Following ChatGPT’s advice, I made the adjustment, and it worked flawlessly.

This experience demonstrated the incredible potential of ChatGPT in analyzing and diagnosing code segments. ChatGPT’s ability to decipher the internal workings of code and provide relevant recommendations is truly remarkable.

The Potential Cost of AI-Assisted Debugging

While ChatGPT proved useful in debugging tasks, it’s essential to realize that AI doesn’t replace other debugging tools. Stepping through code, examining variable values, and understanding how code works are still crucial skills for programmers. ChatGPT serves as a helpful assistant, but it cannot replace genuine programming knowledge and expertise.

It’s worth considering the potential costs associated with relying too heavily on AI-assisted debugging. When using traditional debugging tools, programmers can see every change made in the code. However, with AI-generated code, the resulting product becomes harder to maintain, as the programmer is further removed from the coding process. If issues arise in AI-generated code, fixing them may require more time and effort compared to if a human coder had done the task manually.

Facing the Dystopian Future?

Looking ahead, an interesting future awaits where tools like ChatGPT can be fed massive amounts of code, enabling them to find and fix bugs in entire projects. Microsoft’s Copilot tool for GitHub is already a step in that direction.

However, there are concerns about the implications of granting AI access to vast repositories of code. The fear is that undesirable actions could be embedded within the code itself. While hyperbolic, the scenario raises valid questions about the potential consequences of relying too heavily on AI, especially when it comes to code that impacts critical infrastructure.

It’s important to strike a balance and use AI tools as a valuable addition to the programming toolbox. While ChatGPT has proven its worth in certain scenarios, programmers must retain a deep understanding of their code and exercise cautious supervision over AI-generated solutions.

The Verdict: A Unique Debugging Tool

In conclusion, ChatGPT can be an invaluable resource for programmers seeking assistance in the debugging process. Its ability to analyze code snippets and provide solutions is undeniably impressive. However, it’s crucial to remember that ChatGPT should be treated as a helper, not a replacement for genuine understanding and expertise in programming.

As AI technology continues to advance, there will undoubtedly be exciting possibilities for using AI in debugging tasks. But let’s keep in mind the challenges and limitations that come with it. By harnessing the power of AI while maintaining a critical eye, programmers can navigate the world of debugging with confidence and efficiency. So, next time you find yourself stuck in a programming conundrum, don’t forget to give ChatGPT a try. But be sure to keep a close eye on the AI, for the future of debugging lies in a fine balance between human knowledge and artificial intelligence.

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ChatGPT debugged my code for real.