| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - code-generation |
| - algorithms |
| - data-structures |
| - dynamic-programming |
| - graph-algorithms |
| - benchmark |
| - execution-grounded |
| pretty_name: ExecuGraph Internal-30 DSA Benchmark |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: internal30.jsonl |
| --- |
| |
| # ExecuGraph Internal-30 Benchmark |
|
|
| > **Anonymized for double-blind review.** Author, affiliation, and citation |
| > metadata are withheld and will be released on acceptance. |
|
|
| A curated suite of **30 data-structures-and-algorithms (DSA) problems** used as |
| the headline benchmark for the ExecuGraph framework. Each problem ships with an |
| unambiguous natural-language specification, the target function signature (and |
| accepted aliases), a documented selection rationale, and a set of |
| **deterministic test cases** (boundary, canonical, and stress). |
|
|
| ## Composition |
|
|
| | Category | Problems | Notes | |
| |---|---|---| |
| | Dynamic Programming (`dp`) | 10 | Fibonacci, coin change, LCS, LIS, edit-distance-style recurrences | |
| | Graph Algorithms (`graph`) | 10 | BFS/DFS, topological sort, Dijkstra, Bellman–Ford, SCC, connectivity | |
| | Data Structures (`ds`) | 10 | LRU cache, min-stack, heap/priority queue, AVL, linked-list, traversal | |
|
|
| - 27 problems are originally designed; 3 are adapted from the |
| APPS-introductory partition (marked `source = "apps-…"`). |
| - 30 problems, **125 deterministic test cases** in total. |
|
|
| ## Fields |
|
|
| Each line in `internal30.jsonl` is one problem: |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `id` | string | Stable problem identifier (e.g. `topo_sort`) | |
| | `category` | string | `dp` \| `graph` \| `ds` | |
| | `source` | string | `internal` or `apps-<id>` | |
| | `difficulty` | string | `easy` \| `medium` \| `hard` | |
| | `statement` | string | Self-contained problem specification | |
| | `primary_function` | string | Expected function name | |
| | `signature_aliases` | list[string] | Accepted alternative function names | |
| | `selection_rationale` | string | Why the problem is included | |
| | `timeout_s` | float | Per-execution wall-clock budget | |
| | `tests` | list[object] | Test cases: `{args, expected, call, description, kind, has_custom_judge}` | |
|
|
| Within each test, **`args` and `expected` are JSON-encoded strings** (their |
| native types vary between int, list, bool, … across problems, so they are |
| serialized for a uniform, Arrow-loadable schema). Parse them back with |
| `json.loads`. A small number of graph problems whose correct output is not |
| uniquely ordered (e.g. topological sort) use a custom equality judge in the |
| reference harness; those tests are flagged with `has_custom_judge = true`. For |
| portable use, treat them as exact-match or supply an order-insensitive |
| comparison. |
|
|
| ## Usage |
|
|
| ```python |
| import json |
| from datasets import load_dataset |
| |
| ds = load_dataset("anonymousreview111/execugraph-internal30", split="train") |
| p = ds[0] |
| print(p["id"], p["category"], len(p["tests"])) |
| args = json.loads(p["tests"][0]["args"]) # -> e.g. [10] |
| expected = json.loads(p["tests"][0]["expected"]) # -> e.g. 55 |
| ``` |
|
|
| ## Reproducibility |
|
|
| This dataset is regenerated from the framework's single source of truth |
| (`execugraph/benchmarks/internal30.py`) via `scripts/export_dataset.py` in the |
| accompanying (anonymized) code repository. Every numeric claim in the paper's |
| internal-30 tables is reproducible from that code together with this dataset. |
|
|
| ## License |
|
|
| Released under the Apache-2.0 license. |
|
|
