Add 50% floor results for current Perch question pack across three benchmarks
#20
by jfbrown95 - opened
- README.md +69 -64
- benchmark.py +8 -4
- predictions/current/bug/clm.jsonl +0 -0
- predictions/current/bug/diffusiongemma.jsonl +0 -0
- predictions/current/bug/jev.jsonl +0 -0
- predictions/current/bug/semif.jsonl +0 -0
- predictions/current/lint/clm.jsonl +428 -0
- predictions/current/lint/diffusiongemma.jsonl +0 -0
- predictions/current/lint/jev.jsonl +428 -0
- predictions/current/lint/semif.jsonl +0 -0
- predictions/current/security/clm.jsonl +0 -0
- predictions/current/security/diffusiongemma.jsonl +0 -0
- predictions/current/security/jev.jsonl +0 -0
- predictions/current/security/semif.jsonl +0 -0
- results-current.jsonl +24 -0
- results.jsonl +24 -0
README.md
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# Perch benchmark results
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This benchmark compares [TypeSafe's Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev), a [System One classifier](https://typesafe.ai/), with open
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## Reading the results
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- **AP
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- **Precision
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- **TP, FP, and FN
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- **Pair order
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## Bugs
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| Model | AP | Precision
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_Sorted by F1
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### Private — 498 pairs
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| Model | AP | Precision
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_Sorted by F1
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## Security
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| Model | AP | Precision
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| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | **
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_Sorted by F1
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### Private — 439 pairs
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| Model | AP | Precision
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| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) |
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_Sorted by F1
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##
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| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | Public | 214 | 95.0% | 94.0% | 73.8% | 82.7% | 158 | 10 | 56 | 96.3% |
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| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | Private | 71 | 94.3% | 90.0% | 76.1% | 82.4% | 54 | 6 | 17 | 94.4% |
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| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | Combined | 285 | 94.8% | 93.0% | 74.4% | 82.7% | 212 | 16 | 73 | 95.8% |
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- [`question-pack.json`](https://huggingface.co/datasets/perchscan/benchmark-results/blob/main/question-pack.json): the current bug and security questions exported from Perch's public `scan.yaml`, including language selectors and alert floors.
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- [`adapters/`](https://huggingface.co/datasets/perchscan/benchmark-results/tree/main/adapters): example runners for the unmodified open-source models in the tables.
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- `results.jsonl`: one aggregate report per domain, track, model, and protocol.
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- [`perch-lint-results.jsonl`](https://huggingface.co/datasets/perchscan/benchmark-results/blob/main/perch-lint-results.jsonl): combined semantic-lint metrics for public, private, and total.
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- [`predictions/perch-lint/jev-1.13.0.jsonl`](https://huggingface.co/datasets/perchscan/benchmark-results/blob/main/predictions/perch-lint/jev-1.13.0.jsonl): public Perch Lint Bench method probabilities.
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- `benchmark-costs.json`: token usage, GPU timing, rates, and per-track cost calculations.
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- `split-summary.json`: split sizes and whole-file SHA-256 values. It contains no private rows or repository list.
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- `predictions/`: public-pair probabilities for auditing the public scores. No private predictions are included.
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## Run the public benchmark
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```
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```sh
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python3 benchmark.py
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```
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[Perch website](https://perchscan.com)
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# Perch benchmark results
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This benchmark compares [TypeSafe's Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev), a [System One classifier](https://typesafe.ai/), with open models on finding bugs and security issues in individual code methods. Each record contains a method before and after a transaction change, plus input adapted from a [Perch scan](https://github.com/lakeday-org/perch): method source, imports, callers, callees, and static call-graph edges where available. Models score each version independently, without seeing the repair. Benign reference methods are included, so a `before` method is not automatically positive.
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Pairs come from open-source projects in Rust, JavaScript, TypeScript, Python, Go, Java, C, C++, C#, PHP, and other languages. Bug, security, and user-written semantic lint rules are separate tracks. Public and private splits are disjoint by source repository.
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## Reading the results
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- **AP** measures how well scores rank actual issues across cutoffs.
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- **50% floor:** A score **strictly above 0.5** counts as an alert for every model and question. The production scan pack's per-question floors are ignored here.
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- **Precision** is the share of alerts that are correct; **recall** is the share of actual issues found; **F1** combines both.
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- **TP**, **FP**, and **FN** are correct alerts, false alerts, and missed issues.
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- **Pair order** is the share of eligible repairs where the issue-bearing before method scores above the fixed after method. Ties do not count.
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## Bugs
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[Public pairs](https://huggingface.co/datasets/perchscan/bug-pairs-public)
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### Public — 2,220 pairs
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| Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
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| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
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| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 42.1% | 42.2% | **93.7%** | **58.2%** | **1,744** | 2,387 | **117** | 45.3% | ~$0.210 |
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| [SemiF](https://github.com/TheoLeeCJ/SemIf) | 44.6% | 43.3% | 56.0% | 48.8% | 1,042 | 1,365 | 819 | 44.9% | $0.239 |
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| [DiffusionGemma Jev](https://github.com/razorback16/openjev) | **46.8%** | 46.3% | 39.9% | 42.9% | 743 | 863 | 1,118 | **52.4%** | $0.239 |
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| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 46.2% | **48.5%** | 17.4% | 25.6% | 324 | **344** | 1,537 | 42.1% | $0.620 |
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_Sorted by F1. Bold marks the best value in each quality column; lower FP and FN are better._
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### Private — 498 pairs
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| Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
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| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 42.7% | 42.9% | **94.8%** | **59.0%** | **402** | 536 | **22** | 47.9% | ~$0.047 |
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| [SemiF](https://github.com/TheoLeeCJ/SemIf) | 44.5% | 44.4% | 58.3% | 50.4% | 247 | 309 | 177 | 42.4% | $0.054 |
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| [DiffusionGemma Jev](https://github.com/razorback16/openjev) | **47.9%** | 49.5% | 43.4% | 46.2% | 184 | 188 | 240 | **53.1%** | $0.054 |
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| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 47.0% | **49.7%** | 19.1% | 27.6% | 81 | **82** | 343 | 48.1% | $0.138 |
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_Sorted by F1. Bold marks the best value in each quality column; lower FP and FN are better._
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## Security
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[Public pairs](https://huggingface.co/datasets/perchscan/security-pairs-public)
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### Public — 1,311 pairs
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| Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
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| [SemiF](https://github.com/TheoLeeCJ/SemIf) | 43.9% | 43.6% | **100.0%** | **60.7%** | **1,142** | 1,480 | **0** | 46.2% | $0.208 |
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| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 42.9% | 43.6% | **100.0%** | **60.7%** | **1,142** | 1,480 | **0** | 45.9% | ~$1.432 |
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| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | **51.5%** | **48.1%** | 56.7% | 52.1% | 648 | 699 | 494 | 52.6% | $0.507 |
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| [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 48.2% | 47.6% | 45.7% | 46.6% | 522 | **574** | 620 | **53.7%** | $0.208 |
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_Sorted by F1. Bold marks the best value in each quality column; lower FP and FN are better._
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### Private — 439 pairs
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| Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
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| [SemiF](https://github.com/TheoLeeCJ/SemIf) | 46.8% | 45.3% | **100.0%** | **62.4%** | **398** | 480 | **0** | 50.0% | $0.074 |
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| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 46.9% | 45.3% | **100.0%** | **62.4%** | **398** | 480 | **0** | 51.3% | ~$0.528 |
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| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 48.9% | **51.9%** | 44.5% | 47.9% | 177 | 164 | 221 | 40.2% | $0.182 |
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| [DiffusionGemma Jev](https://github.com/razorback16/openjev) | **49.2%** | 49.5% | 34.4% | 40.6% | 137 | **140** | 261 | **51.5%** | $0.074 |
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_Sorted by F1. Bold marks the best value in each quality column; lower FP and FN are better._
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## Perch Lint Bench
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[Public pairs](https://huggingface.co/datasets/perchscan/perch-lint-bench-public)
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### Public — 214 pairs
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| Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
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| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | **95.0%** | **94.0%** | 73.8% | **82.7%** | 158 | **10** | 56 | **96.3%** | — |
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| [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 87.0% | 89.1% | 65.0% | 75.1% | 139 | 17 | 75 | 86.4% | $0.018 |
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| [SemiF](https://github.com/TheoLeeCJ/SemIf) | 50.0% | 51.7% | **76.6%** | 61.8% | **164** | 153 | **50** | 56.5% | $0.018 |
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| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 50.5% | 49.0% | 68.2% | 57.0% | 146 | 152 | 68 | 51.4% | ~$0.010 |
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_Sorted by F1. Bold marks the best value in each quality column; lower FP and FN are better._
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### Private — 71 pairs
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| Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
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| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | **94.3%** | **90.0%** | 76.1% | **82.4%** | 54 | **6** | 17 | **94.4%** | — |
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| [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 83.0% | 86.3% | 62.0% | 72.1% | 44 | 7 | 27 | 85.9% | $0.006 |
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| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 51.4% | 50.5% | **78.9%** | 61.5% | **56** | 55 | **15** | 56.3% | ~$0.003 |
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| [SemiF](https://github.com/TheoLeeCJ/SemIf) | 48.9% | 51.0% | 74.6% | 60.6% | 53 | 51 | 18 | 50.7% | $0.006 |
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_Sorted by F1. Bold marks the best value in each quality column; lower FP and FN are better._
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SemiF and CLM-v0.1-8B alerted on **every security method** at the 50% floor. Their security F1 equals the always-alert reference; the FP columns show the resulting false alerts.
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Inference costs cover scoring the method sides in each table. Jev costs are estimated from API-reported input tokens at [TypeSafe's published rate](https://typesafe.ai/blog/introducing-system-one-models-and-jev). Beam costs are API-reported. CLM cost is an estimated share of a 38.4-minute H100 run costing $2.23 total, allocated by embedding count. Jev's semantic-lint token cost was not recorded. Costs are not ranked.
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JevLike (192-byte input), Laya (512-token input), and Kev (packed-branch limit) could not score every full-context method. NanoJev was unavailable on Beam. They are excluded rather than ranked on partial results.
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## Files and reproduction
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- [`results-current.jsonl`](https://huggingface.co/datasets/perchscan/benchmark-results/blob/main/results-current.jsonl): current-pack aggregate results for public and private splits; no private pair data.
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- [`results.jsonl`](https://huggingface.co/datasets/perchscan/benchmark-results/blob/main/results.jsonl): current results plus previous broad-question protocol results, identified by protocol ID.
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- [`predictions/current/`](https://huggingface.co/datasets/perchscan/benchmark-results/tree/main/predictions/current): public method probabilities from this run. Private predictions are not published.
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- [`benchmark.py`](https://huggingface.co/datasets/perchscan/benchmark-results/blob/main/benchmark.py) and [`question-pack.json`](https://huggingface.co/datasets/perchscan/benchmark-results/blob/main/question-pack.json): reproducible current-pack scorer and frozen questions.
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- [`perch-lint-bench-public`](https://huggingface.co/datasets/perchscan/perch-lint-bench-public): paired rules and its runnable scorer.
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To run Jev with the current pack, set `TYPESAFE_API_KEY` or `PERCH_API_KEY` in your environment:
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```sh
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python3 benchmark.py run --domain bug --scorer jev --predictions bug-jev.jsonl --report bug-jev.json
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python3 benchmark.py run --domain security --scorer jev --predictions security-jev.jsonl --report security-jev.json
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```
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For another model, use `--scorer command --command 'python3 your_adapter.py' --model-revision YOUR_MODEL`. The adapter receives the method state and each applicable detection question and returns its true-answer probability. The runner saves predictions for resumption and reports scores only when every method side is complete.
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[Perch website](https://perchscan.com)
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benchmark.py
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if set(question_scores) != expected:
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raise ValueError(f"Question scores differ from selected scan pack: {expected ^ set(question_scores)}")
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score = max(question_scores.values(), default=0.0)
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return score, alert
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"model_revision": model_revision,
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"protocol_id": protocol_id,
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"threshold": threshold,
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"alert_rule": "
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"question_pack_sha256": pack["pack_sha256"] if pack and protocol_id in (pack["protocol_id"], pack["binary_protocol_id"]) else None,
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"benchmark_sha256": digest,
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"benchmark_pairs": len(pairs),
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result = {"domain": args.domain, "pair_id": pair["pair_id"], "role": role,
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"score": score, "model_revision": revision, "protocol_id": protocol}
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if mode == "scan":
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result.update(question_scores=question_scores, alert=alert,
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output.write(json.dumps(result) + "\n")
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output.flush()
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if index % 100 == 0 or index == len(pending):
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if set(question_scores) != expected:
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raise ValueError(f"Question scores differ from selected scan pack: {expected ^ set(question_scores)}")
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score = max(question_scores.values(), default=0.0)
|
| 90 |
+
# Product question `min` values are not benchmark operating points. Every
|
| 91 |
+
# model and question uses the same strict threshold for fair comparison.
|
| 92 |
+
alert = any(question_scores[q["name"]] > threshold for q in relevant)
|
| 93 |
return score, alert
|
| 94 |
|
| 95 |
|
|
|
|
| 201 |
"model_revision": model_revision,
|
| 202 |
"protocol_id": protocol_id,
|
| 203 |
"threshold": threshold,
|
| 204 |
+
"alert_rule": "uniform score > threshold; question min ignored" if pack and protocol_id in (pack["protocol_id"], pack["binary_protocol_id"]) else "score >= threshold",
|
| 205 |
"question_pack_sha256": pack["pack_sha256"] if pack and protocol_id in (pack["protocol_id"], pack["binary_protocol_id"]) else None,
|
| 206 |
"benchmark_sha256": digest,
|
| 207 |
"benchmark_pairs": len(pairs),
|
|
|
|
| 324 |
result = {"domain": args.domain, "pair_id": pair["pair_id"], "role": role,
|
| 325 |
"score": score, "model_revision": revision, "protocol_id": protocol}
|
| 326 |
if mode == "scan":
|
| 327 |
+
result.update(question_scores=question_scores, alert=alert,
|
| 328 |
+
threshold=args.threshold,
|
| 329 |
+
alert_rule="uniform score > threshold; question min ignored",
|
| 330 |
+
pack_sha256=pack["pack_sha256"])
|
| 331 |
output.write(json.dumps(result) + "\n")
|
| 332 |
output.flush()
|
| 333 |
if index % 100 == 0 or index == len(pending):
|
predictions/current/bug/clm.jsonl
ADDED
|
The diff for this file is too large to render.
See raw diff
|
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predictions/current/bug/diffusiongemma.jsonl
ADDED
|
The diff for this file is too large to render.
See raw diff
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predictions/current/bug/jev.jsonl
ADDED
|
The diff for this file is too large to render.
See raw diff
|
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predictions/current/bug/semif.jsonl
ADDED
|
The diff for this file is too large to render.
See raw diff
|
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|
predictions/current/lint/clm.jsonl
ADDED
|
@@ -0,0 +1,428 @@
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|
| 1 |
+
{"alert": false, "domain": "lint", "model_id": "clm", "pair_id": "009ffed856a8788e2837bdaa", "question_scores": {"semantic": 0.3319106101989746}, "role": "after", "score": 0.3319106101989746}
|
| 2 |
+
{"alert": false, "domain": "lint", "model_id": "clm", "pair_id": "009ffed856a8788e2837bdaa", "question_scores": {"semantic": 0.3315621614456177}, "role": "before", "score": 0.3315621614456177}
|
| 3 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "015d5e44896fe6018e25e0ea", "question_scores": {"semantic": 0.5526745617389679}, "role": "after", "score": 0.5526745617389679}
|
| 4 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "015d5e44896fe6018e25e0ea", "question_scores": {"semantic": 0.5972234606742859}, "role": "before", "score": 0.5972234606742859}
|
| 5 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "0fdf03c6c5e3da1ab02991a9", "question_scores": {"semantic": 0.8306773006916046}, "role": "after", "score": 0.8306773006916046}
|
| 6 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "0fdf03c6c5e3da1ab02991a9", "question_scores": {"semantic": 0.8509531021118164}, "role": "before", "score": 0.8509531021118164}
|
| 7 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "105fe03be99c099cbfa5eed5", "question_scores": {"semantic": 0.8081485480070114}, "role": "after", "score": 0.8081485480070114}
|
| 8 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "105fe03be99c099cbfa5eed5", "question_scores": {"semantic": 0.8340117335319519}, "role": "before", "score": 0.8340117335319519}
|
| 9 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "1af8d68290cf1ea185eb1c99", "question_scores": {"semantic": 0.6305902600288391}, "role": "after", "score": 0.6305902600288391}
|
| 10 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "1af8d68290cf1ea185eb1c99", "question_scores": {"semantic": 0.6391336023807526}, "role": "before", "score": 0.6391336023807526}
|
| 11 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "bugsinpy:matplotlib_7:LightSource_shade_rgb", "question_scores": {"semantic": 0.9692585114389658}, "role": "after", "score": 0.9692585114389658}
|
| 12 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "bugsinpy:matplotlib_7:LightSource_shade_rgb", "question_scores": {"semantic": 0.9689994994550943}, "role": "before", "score": 0.9689994994550943}
|
| 13 |
+
{"alert": false, "domain": "lint", "model_id": "clm", "pair_id": "c3530210bb0961ed1d266c50", "question_scores": {"semantic": 0.21599280834197998}, "role": "after", "score": 0.21599280834197998}
|
| 14 |
+
{"alert": false, "domain": "lint", "model_id": "clm", "pair_id": "c3530210bb0961ed1d266c50", "question_scores": {"semantic": 0.20913249254226685}, "role": "before", "score": 0.20913249254226685}
|
| 15 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "c6a9fa4c0365a32ad3e06371", "question_scores": {"semantic": 0.7311436831951141}, "role": "after", "score": 0.7311436831951141}
|
| 16 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "c6a9fa4c0365a32ad3e06371", "question_scores": {"semantic": 0.7340186238288879}, "role": "before", "score": 0.7340186238288879}
|
| 17 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "de6dbd793488a59c1a32387e", "question_scores": {"semantic": 0.8466800004243851}, "role": "after", "score": 0.8466800004243851}
|
| 18 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "de6dbd793488a59c1a32387e", "question_scores": {"semantic": 0.8165929764509201}, "role": "before", "score": 0.8165929764509201}
|
| 19 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "fd45265c79745554ce66fbf1", "question_scores": {"semantic": 0.8780228570103645}, "role": "after", "score": 0.8780228570103645}
|
| 20 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "fd45265c79745554ce66fbf1", "question_scores": {"semantic": 0.8765512183308601}, "role": "before", "score": 0.8765512183308601}
|
| 21 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "gapauth:1c37df75769d5e55b7c8d7d3", "question_scores": {"semantic": 0.5951221883296967}, "role": "after", "score": 0.5951221883296967}
|
| 22 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "gapauth:1c37df75769d5e55b7c8d7d3", "question_scores": {"semantic": 0.5726700127124786}, "role": "before", "score": 0.5726700127124786}
|
| 23 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "gapauth:b64272a984ba1553726075c0", "question_scores": {"semantic": 0.5578152239322662}, "role": "after", "score": 0.5578152239322662}
|
| 24 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "gapauth:b64272a984ba1553726075c0", "question_scores": {"semantic": 0.5266651809215546}, "role": "before", "score": 0.5266651809215546}
|
| 25 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "gapauth:d0fea8de8d2ee566df24f8db", "question_scores": {"semantic": 0.8318952322006226}, "role": "after", "score": 0.8318952322006226}
|
| 26 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "gapauth:d0fea8de8d2ee566df24f8db", "question_scores": {"semantic": 0.8233567029237747}, "role": "before", "score": 0.8233567029237747}
|
| 27 |
+
{"alert": false, "domain": "lint", "model_id": "clm", "pair_id": "gapauth:d732e61fc21036115b3665ca", "question_scores": {"semantic": 0.30040955543518066}, "role": "after", "score": 0.30040955543518066}
|
| 28 |
+
{"alert": false, "domain": "lint", "model_id": "clm", "pair_id": "gapauth:d732e61fc21036115b3665ca", "question_scores": {"semantic": 0.3468398451805115}, "role": "before", "score": 0.3468398451805115}
|
| 29 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "ghsa-source:025b0b29906e75b79b3a57d2", "question_scores": {"semantic": 0.6323599815368652}, "role": "after", "score": 0.6323599815368652}
|
| 30 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "ghsa-source:025b0b29906e75b79b3a57d2", "question_scores": {"semantic": 0.6429159939289093}, "role": "before", "score": 0.6429159939289093}
|
| 31 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "ghsa-source:02aca0684267aaa581ae0d5f", "question_scores": {"semantic": 0.6664467751979828}, "role": "after", "score": 0.6664467751979828}
|
| 32 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "ghsa-source:02aca0684267aaa581ae0d5f", "question_scores": {"semantic": 0.6000130474567413}, "role": "before", "score": 0.6000130474567413}
|
| 33 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "ghsa-source:0a9e7b338fb1f2618b4f879f", "question_scores": {"semantic": 0.6072210967540741}, "role": "after", "score": 0.6072210967540741}
|
| 34 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "ghsa-source:0a9e7b338fb1f2618b4f879f", "question_scores": {"semantic": 0.5562526881694794}, "role": "before", "score": 0.5562526881694794}
|
| 35 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "ghsa-source:149563bff3bfcce6172d951d", "question_scores": {"semantic": 0.527417778968811}, "role": "after", "score": 0.527417778968811}
|
| 36 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "ghsa-source:149563bff3bfcce6172d951d", "question_scores": {"semantic": 0.5288033485412598}, "role": "before", "score": 0.5288033485412598}
|
| 37 |
+
{"alert": true, "domain": "lint", "model_id": "clm", "pair_id": "ghsa-source:189c9212337aac1cf2b55bd5", "question_scores": {"semantic": 0.8308623284101486}, "role": "after", "score": 0.8308623284101486}
|
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predictions/current/lint/diffusiongemma.jsonl
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predictions/current/lint/jev.jsonl
ADDED
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|
| 1 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "bugsinpy:matplotlib_7:LightSource_shade_rgb", "role": "before", "rule_true_probability": 0.66, "score": 0.33999999999999997}
|
| 2 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "bugsinpy:matplotlib_7:LightSource_shade_rgb", "role": "after", "rule_true_probability": 0.77, "score": 0.22999999999999998}
|
| 3 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "github-pr:rack/rack#2270", "role": "before", "rule_true_probability": 0.56, "score": 0.43999999999999995}
|
| 4 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "github-pr:rack/rack#2270", "role": "after", "rule_true_probability": 0.76, "score": 0.24}
|
| 5 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "github-pr:rails/rails#58854", "role": "before", "rule_true_probability": 0.7, "score": 0.30000000000000004}
|
| 6 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "github-pr:rails/rails#58854", "role": "after", "rule_true_probability": 0.66, "score": 0.33999999999999997}
|
| 7 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "jsts-exp-003", "role": "before", "rule_true_probability": 0.35, "score": 0.65}
|
| 8 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "jsts-exp-003", "role": "after", "rule_true_probability": 0.89, "score": 0.10999999999999999}
|
| 9 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:analysis-dev__diktat-596:242162b945c0", "role": "before", "rule_true_probability": 0.46, "score": 0.54}
|
| 10 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:analysis-dev__diktat-596:242162b945c0", "role": "after", "rule_true_probability": 0.8, "score": 0.19999999999999996}
|
| 11 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:analysis-dev__diktat-596:50bf988c970f", "role": "before", "rule_true_probability": 0.43, "score": 0.5700000000000001}
|
| 12 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:analysis-dev__diktat-596:50bf988c970f", "role": "after", "rule_true_probability": 0.72, "score": 0.28}
|
| 13 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:ant-design-blazor__ant-design-blazor-4147:71d86eb8c6c7", "role": "before", "rule_true_probability": 0.23, "score": 0.77}
|
| 14 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:ant-design-blazor__ant-design-blazor-4147:71d86eb8c6c7", "role": "after", "rule_true_probability": 0.85, "score": 0.15000000000000002}
|
| 15 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:anz-bank__sysl-1020:6644005fa407", "role": "before", "rule_true_probability": 0.29, "score": 0.71}
|
| 16 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:anz-bank__sysl-1020:6644005fa407", "role": "after", "rule_true_probability": 0.72, "score": 0.28}
|
| 17 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:apache__jena-2623:7b4f03929161", "role": "before", "rule_true_probability": 0.43, "score": 0.5700000000000001}
|
| 18 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:apache__jena-2623:7b4f03929161", "role": "after", "rule_true_probability": 0.84, "score": 0.16000000000000003}
|
| 19 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:apollographql__apollo-codegen-312:f87be2100129", "role": "before", "rule_true_probability": 0.58, "score": 0.42000000000000004}
|
| 20 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:apollographql__apollo-codegen-312:f87be2100129", "role": "after", "rule_true_probability": 0.78, "score": 0.21999999999999997}
|
| 21 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:apple__swift-nio-extras-100:a85c466e7ca4", "role": "before", "rule_true_probability": 0.18, "score": 0.8200000000000001}
|
| 22 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:apple__swift-nio-extras-100:a85c466e7ca4", "role": "after", "rule_true_probability": 0.67, "score": 0.32999999999999996}
|
| 23 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:behat__gherkin-343:af0ee1072e21", "role": "before", "rule_true_probability": 0.82, "score": 0.18000000000000005}
|
| 24 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:behat__gherkin-343:af0ee1072e21", "role": "after", "rule_true_probability": 0.89, "score": 0.10999999999999999}
|
| 25 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:bluenviron__gortsplib-446:0ef11be4bfc9", "role": "before", "rule_true_probability": 0.12, "score": 0.88}
|
| 26 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:bluenviron__gortsplib-446:0ef11be4bfc9", "role": "after", "rule_true_probability": 0.88, "score": 0.12}
|
| 27 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:btcpayserver__btcpayserver-6228:d9580301bdc1", "role": "before", "rule_true_probability": 0.1, "score": 0.9}
|
| 28 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:btcpayserver__btcpayserver-6228:d9580301bdc1", "role": "after", "rule_true_probability": 0.44, "score": 0.56}
|
| 29 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:cloudspannerecosystem__harbourbridge-42:68765060916d", "role": "before", "rule_true_probability": 0.37, "score": 0.63}
|
| 30 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:cloudspannerecosystem__harbourbridge-42:68765060916d", "role": "after", "rule_true_probability": 0.66, "score": 0.33999999999999997}
|
| 31 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:cobalt-org__liquid-rust-404:b1a06d50da36", "role": "before", "rule_true_probability": 0.17, "score": 0.83}
|
| 32 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:cobalt-org__liquid-rust-404:b1a06d50da36", "role": "after", "rule_true_probability": 0.88, "score": 0.12}
|
| 33 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:cqfn__diktat-1098:d9cd81aae67b", "role": "before", "rule_true_probability": 0.32, "score": 0.6799999999999999}
|
| 34 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:cqfn__diktat-1098:d9cd81aae67b", "role": "after", "rule_true_probability": 0.88, "score": 0.12}
|
| 35 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:dedoc__scramble-362:6b687a3dff51", "role": "before", "rule_true_probability": 0.08, "score": 0.92}
|
| 36 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:dedoc__scramble-362:6b687a3dff51", "role": "after", "rule_true_probability": 0.85, "score": 0.15000000000000002}
|
| 37 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:delgan__loguru-960:9985727b72ce", "role": "before", "rule_true_probability": 0.57, "score": 0.43000000000000005}
|
| 38 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:delgan__loguru-960:9985727b72ce", "role": "after", "rule_true_probability": 0.71, "score": 0.29000000000000004}
|
| 39 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:dotnet-script__dotnet-script-762:2906c196f88d", "role": "before", "rule_true_probability": 0.45, "score": 0.55}
|
| 40 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:dotnet-script__dotnet-script-762:2906c196f88d", "role": "after", "rule_true_probability": 0.83, "score": 0.17000000000000004}
|
| 41 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:dvanoni__notero-608:9b62d028fce0", "role": "before", "rule_true_probability": 0.79, "score": 0.20999999999999996}
|
| 42 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:dvanoni__notero-608:9b62d028fce0", "role": "after", "rule_true_probability": 0.9, "score": 0.09999999999999998}
|
| 43 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:e2niee__pandapower-2017:41265c598a15", "role": "before", "rule_true_probability": 0.23, "score": 0.77}
|
| 44 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:e2niee__pandapower-2017:41265c598a15", "role": "after", "rule_true_probability": 0.9, "score": 0.09999999999999998}
|
| 45 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:eclipse-ee4j__yasson-510:53d7bca0dda5", "role": "before", "rule_true_probability": 0.72, "score": 0.28}
|
| 46 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:eclipse-ee4j__yasson-510:53d7bca0dda5", "role": "after", "rule_true_probability": 0.81, "score": 0.18999999999999995}
|
| 47 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:effekt-lang__effekt-1043:03bdd684f0ef", "role": "before", "rule_true_probability": 0.21, "score": 0.79}
|
| 48 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:effekt-lang__effekt-1043:03bdd684f0ef", "role": "after", "rule_true_probability": 0.77, "score": 0.22999999999999998}
|
| 49 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:evant__kotlin-inject-427:15b8dc7a726a", "role": "before", "rule_true_probability": 0.69, "score": 0.31000000000000005}
|
| 50 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:evant__kotlin-inject-427:15b8dc7a726a", "role": "after", "rule_true_probability": 0.92, "score": 0.07999999999999996}
|
| 51 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:getsentry__sentry-rust-846:ca3774c995f9", "role": "before", "rule_true_probability": 0.49, "score": 0.51}
|
| 52 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:getsentry__sentry-rust-846:ca3774c995f9", "role": "after", "rule_true_probability": 0.82, "score": 0.18000000000000005}
|
| 53 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:getslash__slash-1035:8b461bfb87cf", "role": "before", "rule_true_probability": 0.25, "score": 0.75}
|
| 54 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:getslash__slash-1035:8b461bfb87cf", "role": "after", "rule_true_probability": 0.86, "score": 0.14}
|
| 55 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:go-co-op__gocron-809:fc41814bae65", "role": "before", "rule_true_probability": 0.24, "score": 0.76}
|
| 56 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:go-co-op__gocron-809:fc41814bae65", "role": "after", "rule_true_probability": 0.81, "score": 0.18999999999999995}
|
| 57 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:go-kit__kit-974:f1b8a4c5cb1b", "role": "before", "rule_true_probability": 0.43, "score": 0.5700000000000001}
|
| 58 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:go-kit__kit-974:f1b8a4c5cb1b", "role": "after", "rule_true_probability": 0.83, "score": 0.17000000000000004}
|
| 59 |
+
{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "swe-rebench:google__wire-220:5cae5f9c28ec", "role": "before", "rule_true_probability": 0.3, "score": 0.7}
|
| 60 |
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{"alert": true, "domain": "lint", "model_id": "jev", "pair_id": "ghsa-source:229bb05ca26d3d494b4f9f46", "role": "before", "rule_true_probability": 0.2, "score": 0.8}
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| 424 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "ghsa-source:229bb05ca26d3d494b4f9f46", "role": "after", "rule_true_probability": 0.93, "score": 0.06999999999999995}
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| 425 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "ghsa-source:a6f6c0d8824e16f7da058b9b", "role": "before", "rule_true_probability": 0.56, "score": 0.43999999999999995}
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| 426 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "ghsa-source:a6f6c0d8824e16f7da058b9b", "role": "after", "rule_true_probability": 0.87, "score": 0.13}
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| 427 |
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{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "native-cwe120-v2:case_db3d8def02599df0", "role": "before", "rule_true_probability": 0.59, "score": 0.41000000000000003}
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| 428 |
+
{"alert": false, "domain": "lint", "model_id": "jev", "pair_id": "native-cwe120-v2:case_db3d8def02599df0", "role": "after", "rule_true_probability": 0.89, "score": 0.10999999999999999}
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{"alert_rule": "score > 0.5", "average_precision": 0.4620038040696911, "benchmark_methods": 4440, "benchmark_pairs": 2220, "confusion": {"fn": 1537, "fp": 344, "tn": 2235, "tp": 324}, "domain": "bug", "f1": 0.25622775800711745, "inference_cost_basis": "API input tokens at published rate", "inference_cost_usd": 0.6199362540000001, "model_id": "jev", "model_revision": "jev-1.13.0", "pair_order": 0.4214365881032548, "pair_order_correct": 751, "pair_order_eligible": 1782, "precision": 0.48502994011976047, "protocol_id": "perch-scan-bb53c8a6ca30", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.1740999462654487, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "public"}
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| 2 |
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{"alert_rule": "score > 0.5", "average_precision": 0.44622354051764024, "benchmark_methods": 4440, "benchmark_pairs": 2220, "confusion": {"fn": 819, "fp": 1365, "tn": 1214, "tp": 1042}, "domain": "bug", "f1": 0.48828491096532334, "inference_cost_basis": "API reported", "inference_cost_usd": 0.239212, "model_id": "semif", "model_revision": "beam:jev/semif@851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a source:ca3ba65f142967030ecb453346e94d6f476a69df", "pair_order": 0.4489337822671156, "pair_order_correct": 800, "pair_order_eligible": 1782, "precision": 0.43290402991275445, "protocol_id": "beam-system-one-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.5599140247178936, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "public"}
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| 3 |
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{"alert_rule": "score > 0.5", "average_precision": 0.4681363756945909, "benchmark_methods": 4440, "benchmark_pairs": 2220, "confusion": {"fn": 1118, "fp": 863, "tn": 1716, "tp": 743}, "domain": "bug", "f1": 0.42861263340063455, "inference_cost_basis": "API reported", "inference_cost_usd": 0.239212, "model_id": "diffusiongemma", "model_revision": "beam:jev/diffusiongemma@ec4ff3df205028f4e81c954c2227f9312b3ec2ea source:3e296f085ce88e5da3850288646b3429eb476774", "pair_order": 0.5241301907968574, "pair_order_correct": 934, "pair_order_eligible": 1782, "precision": 0.462640099626401, "protocol_id": "beam-system-one-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.3992477162815691, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "public"}
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{"alert_rule": "score > 0.5", "average_precision": 0.4206166543192416, "benchmark_methods": 4440, "benchmark_pairs": 2220, "confusion": {"fn": 117, "fp": 2387, "tn": 192, "tp": 1744}, "domain": "bug", "f1": 0.582109479305741, "inference_cost_basis": "allocated GPU runtime by embedding rows", "inference_cost_usd": 0.21036392983052288, "model_id": "clm", "model_revision": "Contrastive-LM/CLM-v0.1-8B@head-b2b4a8c9c2d39263eff78a351eb909a342ce9b3bf21a3f07c1d1bf15f1c4eda5", "pair_order": 0.45286195286195285, "pair_order_correct": 807, "pair_order_eligible": 1782, "precision": 0.42217380779472286, "protocol_id": "clm-choice-head-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.9371305749596991, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "public"}
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| 5 |
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{"alert_rule": "score > 0.5", "average_precision": 0.46965237444275143, "benchmark_methods": 996, "benchmark_pairs": 498, "confusion": {"fn": 343, "fp": 82, "tn": 490, "tp": 81}, "domain": "bug", "f1": 0.27597955706984667, "inference_cost_basis": "API input tokens at published rate", "inference_cost_usd": 0.138020904, "model_id": "jev", "model_revision": "jev-1.13.0", "pair_order": 0.48129675810473815, "pair_order_correct": 193, "pair_order_eligible": 401, "precision": 0.49693251533742333, "protocol_id": "perch-scan-bb53c8a6ca30", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.19103773584905662, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "private"}
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| 6 |
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{"alert_rule": "score > 0.5", "average_precision": 0.44537360325188174, "benchmark_methods": 996, "benchmark_pairs": 498, "confusion": {"fn": 177, "fp": 309, "tn": 263, "tp": 247}, "domain": "bug", "f1": 0.5040816326530613, "inference_cost_basis": "API reported", "inference_cost_usd": 0.053726, "model_id": "semif", "model_revision": "beam:jev/semif@851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a source:ca3ba65f142967030ecb453346e94d6f476a69df", "pair_order": 0.4239401496259352, "pair_order_correct": 170, "pair_order_eligible": 401, "precision": 0.4442446043165468, "protocol_id": "beam-system-one-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.5825471698113207, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "private"}
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| 7 |
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{"alert_rule": "score > 0.5", "average_precision": 0.4793149154487596, "benchmark_methods": 996, "benchmark_pairs": 498, "confusion": {"fn": 240, "fp": 188, "tn": 384, "tp": 184}, "domain": "bug", "f1": 0.4623115577889447, "inference_cost_basis": "API reported", "inference_cost_usd": 0.053726, "model_id": "diffusiongemma", "model_revision": "beam:jev/diffusiongemma@ec4ff3df205028f4e81c954c2227f9312b3ec2ea source:3e296f085ce88e5da3850288646b3429eb476774", "pair_order": 0.5311720698254364, "pair_order_correct": 213, "pair_order_eligible": 401, "precision": 0.4946236559139785, "protocol_id": "beam-system-one-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.4339622641509434, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "private"}
|
| 8 |
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{"alert_rule": "score > 0.5", "average_precision": 0.42672500156530696, "benchmark_methods": 996, "benchmark_pairs": 498, "confusion": {"fn": 22, "fp": 536, "tn": 36, "tp": 402}, "domain": "bug", "f1": 0.5903083700440529, "inference_cost_basis": "allocated GPU runtime by embedding rows", "inference_cost_usd": 0.047189746421441615, "model_id": "clm", "model_revision": "Contrastive-LM/CLM-v0.1-8B@head-b2b4a8c9c2d39263eff78a351eb909a342ce9b3bf21a3f07c1d1bf15f1c4eda5", "pair_order": 0.47880299251870323, "pair_order_correct": 192, "pair_order_eligible": 401, "precision": 0.42857142857142855, "protocol_id": "clm-choice-head-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.9481132075471698, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "private"}
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| 9 |
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{"alert_rule": "score > 0.5", "average_precision": 0.5151534578389204, "benchmark_methods": 2622, "benchmark_pairs": 1311, "confusion": {"fn": 494, "fp": 699, "tn": 781, "tp": 648}, "domain": "security", "f1": 0.5206910405785456, "inference_cost_basis": "API input tokens at published rate", "inference_cost_usd": 0.506539362, "model_id": "jev", "model_revision": "jev-1.13.0", "pair_order": 0.5258007117437722, "pair_order_correct": 591, "pair_order_eligible": 1124, "precision": 0.48106904231625836, "protocol_id": "perch-scan-bb53c8a6ca30", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.5674255691768827, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "public"}
|
| 10 |
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{"alert_rule": "score > 0.5", "average_precision": 0.4393580721720199, "benchmark_methods": 2622, "benchmark_pairs": 1311, "confusion": {"fn": 0, "fp": 1480, "tn": 0, "tp": 1142}, "domain": "security", "f1": 0.6068012752391073, "inference_cost_basis": "API reported", "inference_cost_usd": 0.208224, "model_id": "semif", "model_revision": "beam:jev/semif@851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a source:ca3ba65f142967030ecb453346e94d6f476a69df", "pair_order": 0.46174377224199287, "pair_order_correct": 519, "pair_order_eligible": 1124, "precision": 0.43554538520213576, "protocol_id": "beam-system-one-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 1.0, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "public"}
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| 11 |
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{"alert_rule": "score > 0.5", "average_precision": 0.48223333383816414, "benchmark_methods": 2622, "benchmark_pairs": 1311, "confusion": {"fn": 620, "fp": 574, "tn": 906, "tp": 522}, "domain": "security", "f1": 0.46648793565683644, "inference_cost_basis": "API reported", "inference_cost_usd": 0.208224, "model_id": "diffusiongemma", "model_revision": "beam:jev/diffusiongemma@ec4ff3df205028f4e81c954c2227f9312b3ec2ea source:3e296f085ce88e5da3850288646b3429eb476774", "pair_order": 0.5373665480427047, "pair_order_correct": 604, "pair_order_eligible": 1124, "precision": 0.4762773722627737, "protocol_id": "beam-system-one-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.45709281961471104, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "public"}
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| 12 |
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{"alert_rule": "score > 0.5", "average_precision": 0.4286620202416757, "benchmark_methods": 2622, "benchmark_pairs": 1311, "confusion": {"fn": 0, "fp": 1480, "tn": 0, "tp": 1142}, "domain": "security", "f1": 0.6068012752391073, "inference_cost_basis": "allocated GPU runtime by embedding rows", "inference_cost_usd": 1.4318487214883406, "model_id": "clm", "model_revision": "Contrastive-LM/CLM-v0.1-8B@head-b2b4a8c9c2d39263eff78a351eb909a342ce9b3bf21a3f07c1d1bf15f1c4eda5", "pair_order": 0.45907473309608543, "pair_order_correct": 516, "pair_order_eligible": 1124, "precision": 0.43554538520213576, "protocol_id": "clm-choice-head-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 1.0, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "public"}
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| 13 |
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{"alert_rule": "score > 0.5", "average_precision": 0.48923834174114805, "benchmark_methods": 878, "benchmark_pairs": 439, "confusion": {"fn": 221, "fp": 164, "tn": 316, "tp": 177}, "domain": "security", "f1": 0.4790257104194858, "inference_cost_basis": "API input tokens at published rate", "inference_cost_usd": 0.18228470400000002, "model_id": "jev", "model_revision": "jev-1.13.0", "pair_order": 0.4020100502512563, "pair_order_correct": 160, "pair_order_eligible": 398, "precision": 0.5190615835777126, "protocol_id": "perch-scan-bb53c8a6ca30", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.44472361809045224, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "private"}
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| 14 |
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{"alert_rule": "score > 0.5", "average_precision": 0.46783576019545, "benchmark_methods": 878, "benchmark_pairs": 439, "confusion": {"fn": 0, "fp": 480, "tn": 0, "tp": 398}, "domain": "security", "f1": 0.6238244514106583, "inference_cost_basis": "API reported", "inference_cost_usd": 0.074232, "model_id": "semif", "model_revision": "beam:jev/semif@851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a source:ca3ba65f142967030ecb453346e94d6f476a69df", "pair_order": 0.5, "pair_order_correct": 199, "pair_order_eligible": 398, "precision": 0.4533029612756264, "protocol_id": "beam-system-one-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 1.0, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "private"}
|
| 15 |
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{"alert_rule": "score > 0.5", "average_precision": 0.49213375578819896, "benchmark_methods": 878, "benchmark_pairs": 439, "confusion": {"fn": 261, "fp": 140, "tn": 340, "tp": 137}, "domain": "security", "f1": 0.4059259259259259, "inference_cost_basis": "API reported", "inference_cost_usd": 0.074232, "model_id": "diffusiongemma", "model_revision": "beam:jev/diffusiongemma@ec4ff3df205028f4e81c954c2227f9312b3ec2ea source:3e296f085ce88e5da3850288646b3429eb476774", "pair_order": 0.5150753768844221, "pair_order_correct": 205, "pair_order_eligible": 398, "precision": 0.49458483754512633, "protocol_id": "beam-system-one-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.3442211055276382, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "private"}
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| 16 |
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| 17 |
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{"alert_rule": "score > 0.5", "average_precision": 0.9503065382620107, "benchmark_methods": 428, "benchmark_pairs": 214, "confusion": {"fn": 56, "fp": 10, "tn": 204, "tp": 158}, "domain": "lint", "f1": 0.8272251308900523, "inference_cost_basis": "not recorded", "inference_cost_usd": null, "model_id": "jev", "model_revision": "jev-1.13.0", "pair_order": 0.9626168224299065, "pair_order_correct": 206, "pair_order_eligible": 214, "precision": 0.9404761904761905, "protocol_id": "perch-semantic-ensure-v1", "question_pack_sha256": null, "recall": 0.7383177570093458, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "public"}
|
| 18 |
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{"alert_rule": "score > 0.5", "average_precision": 0.5001988302534378, "benchmark_methods": 428, "benchmark_pairs": 214, "confusion": {"fn": 50, "fp": 153, "tn": 61, "tp": 164}, "domain": "lint", "f1": 0.6177024482109228, "inference_cost_basis": "API reported", "inference_cost_usd": 0.018353, "model_id": "semif", "model_revision": "semif", "pair_order": 0.5654205607476636, "pair_order_correct": 121, "pair_order_eligible": 214, "precision": 0.5173501577287066, "protocol_id": "perch-semantic-ensure-v1", "question_pack_sha256": null, "recall": 0.7663551401869159, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "public"}
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| 19 |
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{"alert_rule": "score > 0.5", "average_precision": 0.869561328005063, "benchmark_methods": 428, "benchmark_pairs": 214, "confusion": {"fn": 75, "fp": 17, "tn": 197, "tp": 139}, "domain": "lint", "f1": 0.7513513513513513, "inference_cost_basis": "API reported", "inference_cost_usd": 0.018353, "model_id": "diffusiongemma", "model_revision": "diffusiongemma", "pair_order": 0.8644859813084113, "pair_order_correct": 185, "pair_order_eligible": 214, "precision": 0.8910256410256411, "protocol_id": "perch-semantic-ensure-v1", "question_pack_sha256": null, "recall": 0.6495327102803738, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "public"}
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| 20 |
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{"alert_rule": "score > 0.5", "average_precision": 0.5053795675276925, "benchmark_methods": 428, "benchmark_pairs": 214, "confusion": {"fn": 68, "fp": 152, "tn": 62, "tp": 146}, "domain": "lint", "f1": 0.5703125, "inference_cost_basis": "allocated GPU runtime by embedding rows", "inference_cost_usd": 0.0101391623837234, "model_id": "clm", "model_revision": "Contrastive-LM/CLM-v0.1-8B@head-b2b4a8c9c2d39263eff78a351eb909a342ce9b3bf21a3f07c1d1bf15f1c4eda5", "pair_order": 0.514018691588785, "pair_order_correct": 110, "pair_order_eligible": 214, "precision": 0.4899328859060403, "protocol_id": "clm-choice-head-v1", "question_pack_sha256": null, "recall": 0.6822429906542056, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "public"}
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| 21 |
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{"alert_rule": "score > 0.5", "average_precision": 0.9434782206832935, "benchmark_methods": 142, "benchmark_pairs": 71, "confusion": {"fn": 17, "fp": 6, "tn": 65, "tp": 54}, "domain": "lint", "f1": 0.8244274809160306, "inference_cost_basis": "not recorded", "inference_cost_usd": null, "model_id": "jev", "model_revision": "jev-1.13.0", "pair_order": 0.9436619718309859, "pair_order_correct": 67, "pair_order_eligible": 71, "precision": 0.9, "protocol_id": "perch-semantic-ensure-v1", "question_pack_sha256": null, "recall": 0.7605633802816901, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "private"}
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| 22 |
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| 23 |
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| 24 |
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{"alert_rule": "score > 0.5", "average_precision": 0.5141925432023778, "benchmark_methods": 142, "benchmark_pairs": 71, "confusion": {"fn": 15, "fp": 55, "tn": 16, "tp": 56}, "domain": "lint", "f1": 0.6153846153846154, "inference_cost_basis": "allocated GPU runtime by embedding rows", "inference_cost_usd": 0.0033639277067493522, "model_id": "clm", "model_revision": "Contrastive-LM/CLM-v0.1-8B@head-b2b4a8c9c2d39263eff78a351eb909a342ce9b3bf21a3f07c1d1bf15f1c4eda5", "pair_order": 0.5633802816901409, "pair_order_correct": 40, "pair_order_eligible": 71, "precision": 0.5045045045045045, "protocol_id": "clm-choice-head-v1", "question_pack_sha256": null, "recall": 0.7887323943661971, "run_id": "perch-scan-pack-20260928", "threshold": 0.5, "track": "private"}
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results.jsonl
CHANGED
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{"average_precision": 0.4728876281488367, "benchmark_methods": 878, "benchmark_pairs": 439, "benchmark_sha256": "a4b81728e9e520cf1c6f342974bec369d72f5b7d8aabc1c9cf2aceba966f8051", "confusion": {"fn": 391, "fp": 9, "tn": 471, "tp": 7}, "domain": "security", "f1": 0.033816425120772944, "false_positives_per_true_positive": 1.2857142857142858, "model_id": "vagmi/jev-lite", "model_revision": "gemma-4-E4B-it+jev-lite-released", "precision": 0.4375, "protocol_id": "perch-broad-v1", "recall": 0.017587939698492462, "retrospective_split": true, "run_id": "gemma-jev-lite-base", "threshold": 0.5, "track": "private", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 117, "eligible": 398, "ties": 132}}
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| 17 |
{"average_precision": 0.471468796194999, "benchmark_methods": 878, "benchmark_pairs": 439, "benchmark_sha256": "a4b81728e9e520cf1c6f342974bec369d72f5b7d8aabc1c9cf2aceba966f8051", "confusion": {"fn": 128, "fp": 306, "tn": 174, "tp": 270}, "domain": "security", "f1": 0.5544147843942505, "false_positives_per_true_positive": 1.1333333333333333, "model_id": "Contrastive-LM/CLM-v0.1-8B", "model_revision": "b2b4a8c9c2d39263eff78a351eb909a342ce9b3bf21a3f07c1d1bf15f1c4eda5", "precision": 0.46875, "protocol_id": "clm-native-choice-6144-v1", "recall": 0.678391959798995, "retrospective_split": true, "run_id": "clm-base", "threshold": 0.5, "track": "private", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 210, "eligible": 398, "ties": 0}}
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| 18 |
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{"average_precision": 0.4728876281488367, "benchmark_methods": 878, "benchmark_pairs": 439, "benchmark_sha256": "a4b81728e9e520cf1c6f342974bec369d72f5b7d8aabc1c9cf2aceba966f8051", "confusion": {"fn": 391, "fp": 9, "tn": 471, "tp": 7}, "domain": "security", "f1": 0.033816425120772944, "false_positives_per_true_positive": 1.2857142857142858, "model_id": "vagmi/jev-lite", "model_revision": "gemma-4-E4B-it+jev-lite-released", "precision": 0.4375, "protocol_id": "perch-broad-v1", "recall": 0.017587939698492462, "retrospective_split": true, "run_id": "gemma-jev-lite-base", "threshold": 0.5, "track": "private", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 117, "eligible": 398, "ties": 132}}
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| 17 |
{"average_precision": 0.471468796194999, "benchmark_methods": 878, "benchmark_pairs": 439, "benchmark_sha256": "a4b81728e9e520cf1c6f342974bec369d72f5b7d8aabc1c9cf2aceba966f8051", "confusion": {"fn": 128, "fp": 306, "tn": 174, "tp": 270}, "domain": "security", "f1": 0.5544147843942505, "false_positives_per_true_positive": 1.1333333333333333, "model_id": "Contrastive-LM/CLM-v0.1-8B", "model_revision": "b2b4a8c9c2d39263eff78a351eb909a342ce9b3bf21a3f07c1d1bf15f1c4eda5", "precision": 0.46875, "protocol_id": "clm-native-choice-6144-v1", "recall": 0.678391959798995, "retrospective_split": true, "run_id": "clm-base", "threshold": 0.5, "track": "private", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 210, "eligible": 398, "ties": 0}}
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| 18 |
{"average_precision": 0.430399069654715, "benchmark_methods": 878, "benchmark_pairs": 439, "benchmark_sha256": "a4b81728e9e520cf1c6f342974bec369d72f5b7d8aabc1c9cf2aceba966f8051", "confusion": {"fn": 1, "fp": 478, "tn": 2, "tp": 397}, "domain": "security", "f1": 0.6237234878240377, "false_positives_per_true_positive": 1.2040302267002518, "model_id": "cisco-securebert2.0-code-vuln", "model_revision": "cisco-ai/SecureBERT2.0-code-vuln-detection", "precision": 0.45371428571428574, "protocol_id": "perch-security-state-4096-v1", "recall": 0.9974874371859297, "retrospective_split": true, "run_id": "securebert-base-normalized", "threshold": 0.5, "track": "private", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 222, "eligible": 398, "ties": 1}}
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| 19 |
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| 21 |
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| 38 |
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