Hi everyone. I’ve been using GitHub Copilot (the Individual plan) for a full month now across a mix of legacy maintenance and greenfield development projects. I kept a detailed log of my time spent, and the results surprised me, so I wanted to share and see if others have had similar experiences.
My primary finding is a stark contrast in time savings:
* **Bug fixes and refactoring:** I measured an average time saving of approximately 15%. Copilot excels at suggesting common patterns, boilerplate error handling, and alternative methods when I'm clearly working within an existing code structure. For example, when I needed to add comprehensive input validation to an old API module, it quickly suggested the logical `if` branches and relevant error messages, which I only had to tweak.
* **New feature development:** Here, my measured time savings was effectively 0%. When architecting something novel or integrating a new SDK without extensive, context-rich comments, Copilot’s suggestions often felt generic or even distracting. I found myself spending as much time evaluating and correcting its off-target suggestions as I would have spent writing the code from scratch.
I have a few hypotheses for this discrepancy, and I'm curious if they resonate:
* Copilot seems to thrive on established context. Bug fixes usually happen in a well-defined part of the codebase, giving the AI a lot to work with.
* For new features, my initial scaffolding (like function names and high-level comments) might be too vague. Does anyone have a strategy for "priming" Copilot more effectively at the start of a feature?
* Perhaps my expectation for new features is different—I'm not just looking for code completion, but for architectural suggestions, which it doesn't seem to provide.
I also noted a few specific pitfalls that ate into potential gains:
* **Over-reliance on suggestions:** I had to train myself to not just accept the first multi-line suggestion without scrutiny, especially for complex logic. A few times, it introduced subtle bugs I had to later hunt down.
* **Comment-driven development works, but feels slower:** I found that writing very explicit, almost pedantic comments *did* yield better code, but the process of writing those comments sometimes felt slower than just writing the code itself.
My setup was standard: VSCode with the official extension, working primarily in Python and JavaScript. I didn't use any special context-loading techniques beyond the open files.
Has anyone else conducted a similar time-tracking exercise? I'm particularly interested if those working on more innovative or less common tech stacks see even less benefit for new features. And for those who *have* found it useful for new development, what's your secret?
~Heidi