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CaseCrop
Turn a long failing trace into a smaller regression case
Remove irrelevant events from a failing trace while retaining declared prerequisites and replay evidence. Reduce the trace while preserving the same reproducible failure. Requires a replay function. Closure 1-minimality is not a global minimum or proof of root cause.
A long failing trace is useful evidence, but it is not always a useful regression test. Most of the events may have nothing to do with the bug.
CaseCrop removes events, preserves their declared prerequisites and replays the remaining sequence. It keeps a candidate only when the original failure signature comes back. A different crash is not a successful reduction.
The browser workbench runs the Python library locally through Pyodide. The bundled cache example reduces 36 events to six. You can inspect the attempted reductions and export the evidence; the CLI can also generate an executable regression case.
It is free and MIT licensed, with no runtime dependencies. To use your own application, provide a replay function that resets state each time. The reported minimality is closure 1-minimality, not a global minimum or a proof of root cause.
If you debug stateful workflows, what would make connecting your replay harness easier?
Try it: https://casecrop.web.app
Source: https://github.com/shi1720/casecrop
Built by Shivam Gupta. More work and contact: https://shivamgupta.web.app/
LinkedIn: https://www.linkedin.com/in/shiv...
About CaseCrop on Product Hunt
“Turn a long failing trace into a smaller regression case”
CaseCrop was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #9 on the daily leaderboard. Remove irrelevant events from a failing trace while retaining declared prerequisites and replay evidence. Reduce the trace while preserving the same reproducible failure. Requires a replay function. Closure 1-minimality is not a global minimum or proof of root cause.
On the analytics side, CaseCrop competes within Developer Tools and GitHub — topics that collectively have 561.5k followers on Product Hunt. The dashboard above tracks how CaseCrop performed against the three products that launched closest to it on the same day.
Who hunted CaseCrop?
CaseCrop was hunted by Shivam Gupta. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of CaseCrop including community comment highlights and product details, visit the product overview.
Shivam Gupta