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
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.