I built an ML tool to catch technical debt. It immediately learned the wrong lesson.
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I built TechDebt ML , an open-source Random Forest model that predicts which files in a Python repo are likely to be carrying technical debt. Along the way it hit 100% accuracy (a red flag, not a win), quietly turned into a "how much git activity does this file have" detector, and only got genuinely useful once I stopped trusting my own test set. This is the story of those three mistakes, with the numbers. Why build this I'm a systems engineering student, and I wanted a project that let me…
1Key Takeaways
- I built TechDebt ML , an open-source Random Forest model that predicts which files in a Python repo are likely to be carrying technical debt.
- Along the way it hit 100% accuracy (a red flag, not a win), quietly turned into a "how much git activity does this file have" detector, and only got genuinely useful once I stopped trusting my own test set.
- This is the story of those three mistakes, with the numbers.
- Why build this I'm a systems engineering student, and I wanted a project that let me….
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that i built TechDebt ML , an open-source Random Forest model that predicts which files in a Python repo are likely to be carrying technical debt.
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