Measuring Coding Challenge Competence With APPS

TL;DR

This paper introduces APPS, a comprehensive benchmark with 10,000 Python problems to evaluate and track the progress of machine learning models in code generation from natural language specifications.

Contribution

The paper presents APPS, a new benchmark for code generation that measures models' ability to generate correct Python code from natural language descriptions, including a large problem set and evaluation methodology.

Findings

Models like GPT-Neo pass about 20% of introductory test cases.

Syntax errors in generated code decrease exponentially with model improvements.

Machine learning models are beginning to learn how to code.

Abstract

While programming is one of the most broadly applicable skills in modern society, modern machine learning models still cannot code solutions to basic problems. Despite its importance, there has been surprisingly little work on evaluating code generation, and it can be difficult to accurately assess code generation performance rigorously. To meet this challenge, we introduce APPS, a benchmark for code generation. Unlike prior work in more restricted settings, our benchmark measures the ability of models to take an arbitrary natural language specification and generate satisfactory Python code. Similar to how companies assess candidate software developers, we then evaluate models by checking their generated code on test cases. Our benchmark includes 10,000 problems, which range from having simple one-line solutions to being substantial algorithmic challenges. We fine-tune large language models on both GitHub and our training set, and we find that the prevalence of syntax errors is decreasing exponentially as models improve. Recent models such as GPT-Neo can pass approximately 20% of the test cases of introductory problems, so we find that machine learning models are now beginning to learn how to code. As the social significance of automatic code generation increases over the coming years, our benchmark can provide an important measure for tracking advancements.