Repocod

Repocod Repocod (full) consists of 980 instances from 11 repositories across diverse domains, including data science, scientific computing, web, and software development. To address these challenges, we create repocod, a python code generation benchmark containing complex tasks with realistic dependencies in real world large projects and appropriate metrics for evaluating source code.

Repocod To address these challenges, we create repocod, a code generation benchmark with 980 problems collected from 11 popular real world projects, with more than 58% of them requiring file level or repository level context information. But can ai generated code be applied directly in real world projects? to address this curiosity, a new benchmark, repocod, has emerged. repocod moves away from artificial, simplified code generation tests, focusing instead on assessing ai performance within real project environments. We create repocod, a code generation benchmark with 980 problems collected from 11 popular real world projects, with more than 58% of them requiring file level or repository level context information. To address these challenges, we create repocod, a code generation benchmark with 980 problems collected from 11 popular real world projects, with more than 58% of them requiring file level or repository level context information.

Repocod We create repocod, a code generation benchmark with 980 problems collected from 11 popular real world projects, with more than 58% of them requiring file level or repository level context information. To address these challenges, we create repocod, a code generation benchmark with 980 problems collected from 11 popular real world projects, with more than 58% of them requiring file level or repository level context information. To address this curiosity, a new benchmark, repocod, has emerged. repocod moves away from artificial, simplified code generation tests, focusing instead on assessing ai performance within real project environments. Can language models replace programmers? repocod says 'not yet' large language models (llms) have achieved high accuracy, i.e., more than 90 pass@1, in solving python coding problems in humaneval and mbpp. thus, a natural question is, whether llms achieve comparable code completion performance compared to human developers? unfortunately, one cannot answer this question using existing manual. This paper designs and builds repocod, a benchmark for assessing llms' ability in real world coding tasks, i.e., generating complex func tions that require repository level dependencies in real world projects, using developer test cases as the validation method. Repocod contains 980 code generation tasks from 11 widely used repositories, covering a wide range of functionalities, including data science, scientific computing, web development, and software devel opment tools.

Repocod To address this curiosity, a new benchmark, repocod, has emerged. repocod moves away from artificial, simplified code generation tests, focusing instead on assessing ai performance within real project environments. Can language models replace programmers? repocod says 'not yet' large language models (llms) have achieved high accuracy, i.e., more than 90 pass@1, in solving python coding problems in humaneval and mbpp. thus, a natural question is, whether llms achieve comparable code completion performance compared to human developers? unfortunately, one cannot answer this question using existing manual. This paper designs and builds repocod, a benchmark for assessing llms' ability in real world coding tasks, i.e., generating complex func tions that require repository level dependencies in real world projects, using developer test cases as the validation method. Repocod contains 980 code generation tasks from 11 widely used repositories, covering a wide range of functionalities, including data science, scientific computing, web development, and software devel opment tools.