HackerRank-ASTRA: Evaluating Correctness & Consistency of Large Language
  Models on cross-domain multi-file project problems
Authors: name: Jun Xing, name: Mayur Bhatia, name: Sahil Phulwani, name: Darshan Suresh, name: Rafik Matta

Evaluating the real-world applicability of large language models (LLMs)
provides valuable insights for their development and use in software
development tasks. Existing benchmarks often focus on standalone coding
problems or specific libraries, overlooking multi-file, project-based scenarios
and lacking a rigorous evaluation of consistency. The HackerRank-ASTRA
Benchmark introduces project-based coding problems that mirror real-world
scenarios. It evaluates model consistency through 32 runs (k = 32) and median
standard deviation while incorporating taxonomy-level analysis to assess
sub-skill capabilities. Initial evaluations on 65 problems show that the top
three models -- o1, o1-preview, and Claude-3.5-Sonnet-1022 -- achieved
comparable average scores of 75%, with no statistically significant differences
in performance. Notably, Claude-3.5-Sonnet-1022 demonstrated the highest
consistency across problems, with low variability (SD = 0.0497), which was
statistically significant compared to other models, highlighting its
reliability for real-world software development tasks.