Most code benchmarks focus on short, single-function problems. Why that matters: agentic coding models must plan, chain steps, and interact with complex systems — not just return a one-off snippet. This dataset collects model-generated prompts and unaltered responses designed to probe those agentic behaviors across real engineering domains, so you can both stress-test reasoning and study failure modes.
What Sets It Apart
- Focus on multi-step, agentic developer tasks (architecture, debugging, tooling, deployment), so evaluations reflect workflow-level competence rather than isolated unit problems.
- Broad language and domain coverage (Python, C/C++, Rust, TypeScript, SQL, mobile, systems, game dev), enabling cross-language analysis and transfer studies.
- Raw model outputs preserved "as-is" — useful for auditing hallucinations, infinite-thought loops, and instruction-following failure modes during dataset curation.
- Includes metadata (DOI: 10.57967/hf/8696; size category 10K–100K) and an Apache-2.0 license, making reuse straightforward after appropriate filtering.
Who it's for and tradeoffs
Great fit if you need a stress-test or fine-tuning corpus for coding agents that must plan and act across multiple files, languages, or system boundaries. It’s valuable for researchers studying agentic failure modes, prompt robustness, or cross-language coding transfer.
Look elsewhere if you require human-verified ground-truth solutions (this dataset contains model outputs without manual correction), or if your benchmark needs unit-level held-out tests like HumanEval/MBPP with human-written reference implementations.
Where it fits
Positioned between unit-code benchmarks (HumanEval/MBPP) and large-scale web-scraped code corpora: it targets higher-level developer tasks and agentic behaviors rather than concise, single-function correctness. Use it to complement standard code benchmarks when evaluating planning, tool use, or multi-step debugging.
Practical notes on use
Treat the dataset as synthetic model-output data: run automated and manual filtering for correctness, safety, and license compliance before training. Because responses are unaltered, expect noise (inaccurate answers, loops). Suggested workflows: (1) use it for zero-shot/chain-of-thought robustness tests; (2) curate subsets for supervised fine-tuning; (3) analyze failure patterns to guide prompt or system design.
Overall, this is a purpose-built resource to surface agentic coding behaviors and failure modes — high value for evaluation and analysis, but not a substitute for vetted, human-curated reference code.
