I built Hotspot to solve a problem I constantly saw: showing real, measurable progress against tech debt and knowledge risk in large codebases. It's a CLI tool written in Go that analyzes Git history to rank files/folders by custom metrics (Hot, Risk, Stale, Complexity).
Since the early versions, the tool has matured significantly. The biggest win has been a major performance optimization: analysis is now dramatically faster on large repos because it uses pre-aggregated commit data, eliminating thousands of individual `git log` calls per file. Additionally, I further improved the timeseries analysis with more comprehensive logic for greater accuracy in risk evolution tracking.
Key features in v1.3.3 focus on giving engineers better data:
1. *Metrics Transparency & Export*: `hotspot metrics` prints the exact weighted formulas used for scoring and now exports to CSV/JSON. The weights are fully configurable via YAML, letting you define risk metrics tailored to your company's standards.
2. *Trend Analysis*: `hotspot timeseries` tracks a file's score over time, helping you pinpoint when a file became a burden. *This includes owner tracking to audit knowledge shifts.*
3. *Release Auditing (Bus Factor Check)*: `hotspot compare` measures the risk delta between two Git refs and *shows the top 2 owners contributing to that change*, letting you catch escalating complexity and Bus Factor liabilities before merging.
I'd genuinely appreciate feedback on the weighted scoring model and its performance on real-world repos. *If you run it against a large repository, what kind of organizational risk blind spots did it uncover?*
I built Hotspot to solve a problem I constantly saw: showing real, measurable progress against tech debt and knowledge risk in large codebases. It's a CLI tool written in Go that analyzes Git history to rank files/folders by custom metrics (Hot, Risk, Stale, Complexity).
Since the early versions, the tool has matured significantly. The biggest win has been a major performance optimization: analysis is now dramatically faster on large repos because it uses pre-aggregated commit data, eliminating thousands of individual `git log` calls per file. Additionally, I further improved the timeseries analysis with more comprehensive logic for greater accuracy in risk evolution tracking.
Key features in v1.3.3 focus on giving engineers better data: 1. *Metrics Transparency & Export*: `hotspot metrics` prints the exact weighted formulas used for scoring and now exports to CSV/JSON. The weights are fully configurable via YAML, letting you define risk metrics tailored to your company's standards. 2. *Trend Analysis*: `hotspot timeseries` tracks a file's score over time, helping you pinpoint when a file became a burden. *This includes owner tracking to audit knowledge shifts.* 3. *Release Auditing (Bus Factor Check)*: `hotspot compare` measures the risk delta between two Git refs and *shows the top 2 owners contributing to that change*, letting you catch escalating complexity and Bus Factor liabilities before merging.
I'd genuinely appreciate feedback on the weighted scoring model and its performance on real-world repos. *If you run it against a large repository, what kind of organizational risk blind spots did it uncover?*