AI Coding Evaluation Dataset & Benchmark Task Development
Designed and developed structured evaluation tasks used to benchmark large language models (LLMs) on real-world software engineering problems. The work involved creating containerized coding environments, writing reproducible tasks, and implementing automated evaluation pipelines. Key responsibilities included: Designing complex software engineering tasks simulating real production issues. Building isolated Docker-based environments for task reproducibility. Writing unit and integration tests to validate model-generated code. Creating task specifications and evaluation scripts used to measure model performance. Structuring datasets similar to SWE-bench style benchmarks used for testing autonomous coding agents. Ensuring high-quality evaluation through deterministic tests, edge case coverage, and reproducible execution environments. Project scale included the creation of multiple independent tasks covering backend development, debugging scenarios, and system-level engineering challenges.