Aider Polyglot Benchmark
https://github.com/Aider-AI/polyglot-benchmark- Category
- Developer Tools
- Rank
- No. 1275Tools index
Previous survey · No. 1282 ·
- Listed in
- #10 Find AI benchmarks
- Pricing
- Open Source
- Type
- TOOL
- Builder
- aider-ai
- GitHub
- 224 stars
- Date
About
Coding problems across multiple languages used to benchmark Aider — reuse it to evaluate any AI coding agent.
What it does
It presents unfinished exercise implementations beside executable behavioral checks. The collection draws from six Exercism tracks, while a separately referenced harness handles benchmark execution.
Why it's ranked here
The collection offers credible evaluation material because its tests probe more than happy paths, including overflow, negative values, non-greedy optimization cases, and large output shapes. Its value depends on external orchestration, since the repository description points elsewhere for the benchmark harness.
What's good
The exercises carry clear source attribution and concrete expected behavior. Supplied C++ cases cover clock arithmetic across days, knapsack choices that defeat simple greedy strategies, complete allergy combinations, and diamonds from the smallest input through the alphabet.
Tradeoffs
This is task material, not a complete evaluation product. The documented harness lives elsewhere, and no standalone installation workflow appears in the repository description. Some C++ coverage requires an expanded-test compile flag, while the meetup exercise also requires Boost date-time support.
How to use it well
Use it when comparing coding agents against fixed, test-backed exercises across C++, Go, Java, JavaScript, Python, and Rust. Connect the corpus to the referenced benchmark harness or your own runner. It does not supply agent orchestration, result analysis, or reporting by itself.
Technical notes+
README.md identifies Exercism as the source and links to the separate Aider benchmark harness. cpp/exercises/practice/clock/clock.cpp, cpp/exercises/practice/meetup/meetup.cpp, cpp/exercises/practice/diamond/diamond.cpp, and cpp/exercises/practice/knapsack/knapsack.cpp contain namespace shells without implementations. cpp/exercises/practice/clock/clock_test.cpp uses Catch2 and gates add and equality suites behind EXERCISM_RUN_ALL_TESTS. cpp/exercises/practice/knapsack/knapsack_test.cpp follows the same pattern for most cases. cpp/exercises/practice/meetup/meetup_test.cpp depends on Boost Gregorian date types, while cpp/exercises/practice/diamond/diamond_test.cpp checks exact row vectors through input Z.
Observed
- Language coverage
- Exercises are sourced from C++, Go, Java, JavaScript, Python, and Rust tracks.
- Interface
- Repository corpus with benchmark execution delegated to a separately referenced harness.
- Packaging and install
- No package or installation command is documented; the benchmark harness is referenced separately.
- License
- Exercise content is copyrighted by Exercism and used under the source tracks' open source licenses.
- Test structure
- Supplied C++ exercises include incomplete implementation stubs paired with Catch2 behavioral tests.
Read from README.md, cpp/exercises/practice/clock/clock.cpp, cpp/exercises/practice/meetup/meetup.cpp, cpp/exercises/practice/diamond/diamond.cpp, cpp/exercises/practice/clock/clock_test.cpp, cpp/exercises/practice/knapsack/knapsack.cpp, cpp/exercises/practice/meetup/meetup_test.cpp, cpp/exercises/practice/allergies/allergies.cpp, cpp/exercises/practice/space-age/space_age.cpp, cpp/exercises/practice/diamond/diamond_test.cpp, cpp/exercises/practice/gigasecond/gigasecond.cpp, cpp/exercises/practice/robot-name/robot_name.cpp, cpp/exercises/practice/knapsack/knapsack_test.cpp, cpp/exercises/practice/linked-list/linked_list.cpp, cpp/exercises/practice/allergies/allergies_test.cpp.
What it can do
Benchmark AI coding agents
AI coding agent → Performance metrics and scores
Execute coding problems in multiple programming languages
Coding problem specifications → Test results and validation outcomes
Evaluate code generation accuracy
Generated code solutions → Accuracy assessment and correctness scores
Compare coding agent performance
Multiple AI coding agents → Comparative analysis and rankings
Run multi-language test suites
Code solutions across different programming languages → Cross-language performance reports
Generate standardized coding benchmarks
Benchmark configuration parameters → Standardized test cases and evaluation criteria
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