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Enables end-to-end evaluation of generated code by running test cases against candidate solutions and computing pass rates (percentage of test cases passed), rather than relying on single-example correctness or syntax validation.","intents":["measure functional correctness of generated code by executing against comprehensive test suites","identify partial solutions that pass some test cases but fail on edge cases or larger inputs","compute pass@k metrics (probability of passing all tests with k samples) to evaluate code generation quality","detect overfitting or memorization by comparing performance across different test cases from the same problem"],"best_for":["researchers computing pass@1, pass@10, pass@100 metrics for code generation models","teams building code generation systems that need automated correctness validation","organizations running large-scale evaluation pipelines with thousands of generated solutions"],"limitations":["test suites are fixed and finite; no dynamic test generation or property-based testing to discover edge cases","test case coverage varies by source platform — some problems may have insufficient edge case coverage","timeout and resource limits are not standardized across platforms; execution may fail due to TLE (time limit exceeded) rather than logical incorrectness","test suites do not validate code quality, readability, or efficiency — only functional correctness on given test cases","no mechanism to detect or penalize hardcoded solutions that pass tests but don't generalize"],"requires":["code execution environment with language-specific runtimes (Python, Java, C++, etc.)","sandboxing or containerization to safely execute untrusted generated code","timeout enforcement (typically 5-10 seconds per test case) to prevent infinite loops","memory limits to prevent resource exhaustion"],"input_types":["generated code as strings","test case inputs and expected outputs","problem specifications"],"output_types":["pass/fail status per test case","pass rate (0-100%)","execution traces and error messages","pass@k metrics"],"categories":["code-generation-editing","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"apps-automated-programming-progress-standard__cap_3","uri":"capability://code.generation.editing.natural.language.to.code.pipeline.evaluation","name":"natural language to code pipeline evaluation","description":"Structures problems as natural language descriptions paired with input/output specifications and test suites, enabling end-to-end evaluation of the full code generation pipeline from problem understanding through test validation. 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Problems span domains including dynamic programming, graph algorithms, number theory, and combinatorics, sourced from competitive programming platforms (AtCoder, Codeforces, Kattis) where algorithmic rigor is enforced by time and memory limits.","intents":["evaluate whether code generation models can perform genuine algorithmic reasoning and not just memorize patterns","measure model capability on problems requiring data structure selection (arrays, trees, graphs, heaps, etc.)","assess performance on problems with non-trivial time complexity requirements (e.g., O(n log n) vs O(n²))","identify algorithmic domains where models struggle (e.g., dynamic programming, graph traversal)"],"best_for":["researchers studying algorithmic reasoning capabilities of code generation models","teams building AI systems for competitive programming or technical interview preparation","organizations evaluating whether models have learned generalizable problem-solving strategies"],"limitations":["algorithmic complexity is implicit in test cases (via time/memory limits) rather than explicitly labeled","no structured taxonomy of algorithmic domains or techniques required per problem","problems may have multiple valid algorithmic approaches with different complexity profiles; no preference specified","time and memory limits vary by source platform; no standardized complexity thresholds","no explicit measurement of whether models understand complexity trade-offs or just find solutions that pass tests"],"requires":["code execution environment with enforced time and memory limits","understanding of algorithmic complexity and data structures","ability to analyze generated code for algorithmic approach (optional but useful)"],"input_types":["problem descriptions requiring algorithmic solutions","test cases with varying input sizes and constraints"],"output_types":["generated code","pass/fail on time-limited test execution","implicit complexity assessment via pass rates"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"apps-automated-programming-progress-standard__cap_5","uri":"capability://data.processing.analysis.cross.platform.problem.normalization.and.schema.unification","name":"cross-platform problem normalization and schema unification","description":"Normalizes problems from four heterogeneous online judge platforms (Codewars, AtCoder, Kattis, Codeforces) with different native formats, input/output conventions, and metadata structures into a unified dataset schema. 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