On the Reliability and Explainability of Automated Code Generation Approaches

Abstract

Automatic code generation, the task of generating new code snippets from existing code or comments, has long been of interest. Numerous code generation models have been proposed and proven on different benchmark datasets. However, little is known about whether this objective has been achieved and why code generation models effectively transform code sequences automatically. In other words, can we totally trust these automated code generation models? Consequently, there is a pressing need to understand the inner logic of code generation models and to investigate their replicability, reliability, and explainability. To bridge these research gaps, we conduct a thorough empirical study of five code generation models on four representative code generation datasets to assess the limits and capabilities of automatic code generation approaches. We further employ advanced explainable AI approaches to highlight the tokens that significantly contribute to the code generation. Experiments demonstrate that we successfully replicate state-of-the-art code generation approaches. We discover that state-of-the-art approaches suffer from severe data duplication and input insensitivity, which are subtle issues with significant implications. Our explainability analysis reveals that, in various experimental scenarios, code generation models can recognize code grammar and structural information, but can not capture key tokens that need to be updated. Our results draw several lessons and guidelines for future work in this area.CCS Concepts: • Software and its engineering → Software maintenance tools; • General and reference → Reliability; • Computing methodologies → Natural language processing.

An Approach for Rapid Source Code Development Based on ChatGPT and Prompt Engineering

“…The main challenge stems from the fact that the underlying basis for predicting new code sequences by code generation models remains largely unknown. In this context, Liu et al [40] have delved into the reliability and explainability of Automated Code Generation Approaches.…”