AI models capable of generating code present significant challenges regarding reliability, safety, and security. The complexity of software artifacts means evaluating correctness requires more nuance than traditional classification tasks. Human evaluation of software quality is generally problematic, necessitating routines like code review and both static and dynamic analysis. Current evaluation methods for AI-generated code, evidenced by benchmarks such as HumanEval and MBPP, show limitations that complicate the assurance of quality akin to human-generated software. Issues also arise in the realms of code correctness, comprehension, and maintenance when integrating these models into programming workflows.
AI models that generate code present significant challenges to issues related to reliability, safety, and security. The output from these models needs nuanced evaluation beyond simple classification tasks.
Determining whether AI-generated code is 'correct' requires a complex understanding, as humans face challenges with software quality evaluation, necessitating practices like code review and analysis.
Existing methodologies for assessing AI-generated code quality, such as benchmarks like HumanEval and MBPP, reveal limitations in ensuring reliability and safety comparable to human-written code.
Code comprehension and maintenance pose significant issues, as programmers may struggle with the inherent complexities and uncertainties introduced by using AI-generated solutions.
#ai-code-generation #programming-tools #software-reliability #usability-studies #code-quality-assessment
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