binary ninja: optimize feature extraction
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- Dominant language
- Python
- Stars
- 6.2k
- Forks
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- Avg merge
- 11d 11h
- Merged PRs (30d)
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Description
During some initial profiling, I'm finding that the Binary Ninja backend is substantially slower than vivisect or IDA. This thread will enumerate all the things we discover. It might include: bugs in Binary Ninja, things we're doing wrong, workarounds, etc.
Given how good Binary Ninja's code analysis is, we'd really like to be able to use it widely. So, let's prepare the code for this.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the profiling findings in this issue and comparing the Binary Ninja backend with the vivisect and IDA backends. Trace the feature-extraction path to identify whether the slowdown comes from bugs, incorrect usage, or missing workarounds; done requires a concrete, agreed scope for improving Binary Ninja performance.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- performance, reverse-engineering
- Issue type
- Refactor
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100