baidu/AnyQ
FAQ-based Question Answering System
- Repository: https://github.com/baidu/AnyQ
- Commit:
d94d450d2aaa5f7ed73424b10aa4539835b97527 - Generated: 2026-11-13T23:41:32+00:00
- Scorer 0.1.0, scoring prov-4+qual-2+sec-2, report schema v2
Scores
| Pillar | Score | Signal | Confidence | Coverage |
|---|---|---|---|---|
| AI provenance (estimated AI-generated share) | 0 | none-detected | 59% | 100% |
| Code quality | 40 | poor | 90% | 100% |
| Security | 72 | adequate | 85% | 100% |
AI provenance
No AI attribution or AI-consistent velocity signals detected.
- Explicitly attributed share (lower bound): 0.0%
- Unattributed share in AI-consistent velocity bursts: 0.0%
- Estimated AI-generated share: 0.0%
| Month | Lines added | Attributed to AI |
|---|---|---|
| 2018-07 | 14,454 | 0% |
| 2018-08 | 17 | 0% |
| 2018-09 | 204 | 0% |
| 2018-10 | 12 | 0% |
| 2018-11 | 66 | 0% |
| 2018-12 | 1 | 0% |
| 2019-01 | 1 | 0% |
| 2019-02 | 10 | 0% |
| 2019-04 | 3 | 0% |
| 2019-07 | 24 | 0% |
| 2019-10 | 2 | 0% |
AI provenance describes how code was produced; it is not a quality judgment. Unless attribution is explicit, these are signals consistent with AI generation, not proof. Squash/rebase workflows, imported code, and rewritten history reduce accuracy.
Code quality
Code quality is poor: test-to-source ratio 0.00, no CI test run detected; 0% of function code in complex or long functions; 23% duplicated lines. Weakest area: testing.
| Area | Score |
|---|---|
| Complexity (lizard) | 100 |
| Duplication | 25 |
| Code smells (Opengrep) | 46 |
| Tests and CI | 0 |
| Documentation | 65 |
| Type safety | 40 |
| Process (Scorecard review/maintenance) | 5 |
- Functions: 642; mean complexity 2.6, 90th percentile 6; 0 complex and 0 long functions
- Duplicated lines: 23.1% of 10,358 compared
- Tests: 0 test lines for 14,679 source lines (ratio 0.00); CI test commands: none detected; 0% of recent source commits also change tests
- Docs missing or thin: contributing, changelog
Security
Security posture is adequate: no high-impact findings. Weakest area: posture.
| Area | Score |
|---|---|
| Secrets (gitleaks, tree + history) | 100 |
| Static analysis (Opengrep) | 99 |
| Vulnerable dependencies (OSV) | n/a |
| CI workflows (zizmor) | n/a |
| Posture (policy, updates, signing, lockfiles) | 0 |
| OpenSSF Scorecard | 22 |
- Posture checks passed: lockfiles; missing: security policy, dependency updates, signed commits
- OpenSSF Scorecard aggregate: 2.0/10 (published 2026-11-09)
Top findings
| Severity | Finding | Location |
|---|---|---|
| medium | opengrep/c.lang.security.insecure-use-memset: When handling sensitive information in a buffer, it's important to ensure that the data is securely erased before the buffer is deleted or reused. While memset() is commonly used for this purpose, it can leave sensitive information behind due to compiler optimizations or other factors. To avoi [confidence low] |
include/retrieval/semantic/annoylib.h:690 |
Static signals only: the scorer never runs the project's code. Secret findings are pattern matches that were not verified, and secret values are never stored.
Repository profile
- Source lines: 14,679; test lines: 0
| Language | Lines |
|---|---|
| C++ | 5,589 |
| Python | 5,114 |
| C | 3,707 |
| Shell | 269 |