I Opened 1,000 Issues on Open Source Projects — Here’s Why 90% Got Ignored (And How to Write One That Actually Gets Fixed)

It stung the first time.

I’d spent 45 minutes debugging a race condition in an async Redis client. Traced it down to a specific version. Wrote a detailed issue with logs, stack traces, even a failing test. And then… silence. The issue sat untouched for six months.

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Then I realized: I wasn’t the problem. My issue was.

That was three years ago. Since then, I’ve opened over 1,000 issues across major open source projects — Python frameworks, database drivers, build tools, you name it. And I’ve been collecting data. The results are ugly.

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The Numbers Don’t Lie

I pulled stats from my personal GitHub profile and a few hundred repos I contribute to. Here’s what I found:

Honestly, the gulf between “vague complaint” and “reproducible bug report” is ridiculous. One gets closed as “can’t reproduce” in 30 seconds. The other gets a maintainer to push a fix that same weekend.

Why Most Issues Get Ignored

It’s not laziness. Maintainers are drowning. A typical 10K-star repo gets 5-10 new issues *per day*. They triage by scanning titles and first paragraphs. If anything looks like noise, it’s closed or ignored.

Here are the three deadliest patterns I’ve seen (and committed):

1. The Life Story – “So I was working on this feature, and I updated pip, and then my environment broke, and I think maybe it’s related to Python 3.11 but I’m not sure…” Cut it. Get to the point in the first sentence.

1. The Drive-By – No logs. No version. No steps. Just “X doesn’t work”. That’s not an issue — it’s a tweet.

1. The Kitchen Sink – 10 different symptoms, 20 lines of error output, zero focus. Maintainers don’t want to triage your debugging process. They need *one* specific, reproducible scenario.

The Exact Issue Template I Use (And Recommend)

I’ve refined this over hundreds of issues. Copy-paste it into a markdown file and use it every time.

markdown
## Summary
[One sentence: "When I call `client.get()` with a corrupted key, it raises a `TypeError` instead of returning `None`."]

## Steps to Reproduce (Minimal)

1. Install `asyncio-redis==2.1.4` and Python 3.12.2
2. Run this script:

import asyncio_redis

import asyncio

async def main():

client = await asyncio_redis.Connection.create(host=’localhost’, port=6379)

Simulate a corrupted key (binary junk)

result = await client.get(b’\xff\xfe\x00′)

print(result)

asyncio.run(main())

3. Observe the `TypeError: expected string or bytes-like object`

## Expected Behavior
`client.get()` should handle corrupt keys gracefully and return `None`.

## Actual Behavior
Raises `TypeError` with `get expected string or bytes-like object`. Full traceback: [pastebin link]

## Environment
- Python: 3.12.2 (from `pyenv`)
- OS: macOS 14.3 (M3 Mac)
- Redis server: 7.2.4 (Docker image `redis:7-alpine`)
- Package version: `asyncio-redis==2.1.4`

## Additional Context
This blocks our production CSV ingestion pipeline. Happy to provide a Docker Compose file if needed.

Short. Precise. Tested. That issue gets triaged within an hour, guaranteed.

The Missing Piece: A Reproducible Script

Words are fine. Code is better. A self-contained Python script (or shell command) that I can run locally and watch fail is golden.

I once filed a bug in `httpx` where I included a 12-line script and a `docker-compose.yml` for a simple HTTP server. The maintainer replied in 20 minutes: “Confirmed. Fix is in #2452.” That fix shipped in the next release.

Writing a reproducible test takes more time upfront. But it saves *hours* of back-and-forth. And it earns you respect in the community. Maintainers start recognizing your name.

How AI-Assisted Teams at ECOAAI Write Bulletproof Issues

We apply the same discipline internally. When our Vietnamese engineering teams (based in Ho Chi Minh City and Can Tho) open issues for upstream projects, they follow this exact template. They even use the ECOA AI Platform ACP to auto-generate environment summaries and run reproducible scripts in isolated containers before filing.

The result? Our issues have a 73% resolution rate within two weeks. That’s not luck — it’s a systematic process.

If you’re hiring remote developers, this is the kind of rigor you want. Vague bug reports kill velocity. Precise ones save everyone’s weekend.

The One Thing You Should Never Do

Don’t ask “Is anyone working on this?” or “Will this be fixed soon?” in the issue body. That implies the maintainer owes you labor. Instead, offer help: “I can submit a PR if you point me in the right direction.” That changes everything.

Frequently Asked Questions

How long should an issue description be?

Aim for 5-10 lines of real content, plus code. If you need more, put it in a gist or pastebin. Maintainers skim — make every word count.

Should I include logs even if they’re long?

Yes, but link to a gist or use a collapsible `