Benchmarking AI Models in Software Engineering: A Review, Search Tool,
  and Enhancement Protocol
Authors: name: Roham Koohestani, name: Philippe de Bekker, name: Maliheh Izadi

Benchmarks are essential for consistent evaluation and reproducibility. The
integration of Artificial Intelligence into Software Engineering (AI4SE) has
given rise to numerous benchmarks for tasks such as code generation and bug
fixing. However, this surge presents challenges: (1) scattered benchmark
knowledge across tasks, (2) difficulty in selecting relevant benchmarks, (3)
the absence of a uniform standard for benchmark development, and (4)
limitations of existing benchmarks. In this paper, we review 173 studies and
identify 204 AI4SE benchmarks. We classify these benchmarks, analyze their
limitations, and expose gaps in practices. Based on our review, we created
BenchScout, a semantic search tool to find relevant benchmarks, using automated
clustering of the contexts from associated studies. We conducted a user study
with 22 participants to evaluate BenchScout's usability, effectiveness, and
intuitiveness which resulted in average scores of 4.5, 4.0, and 4.1 out of 5.
To advance benchmarking standards, we propose BenchFrame, a unified method to
enhance benchmark quality. As a case study, we applied BenchFrame to the
HumanEval benchmark and addressed its main limitations. This led to
HumanEvalNext, featuring (1) corrected errors, (2) improved language
conversion, (3) expanded test coverage, and (4) increased difficulty. We then
evaluated ten state-of-the-art code language models on HumanEval,
HumanEvalPlus, and HumanEvalNext. On HumanEvalNext, models showed a pass@1
score reduction of 31.22% and 19.94% compared to HumanEval and HumanEvalPlus,
respectively.