Better Together? An Evaluation of AI-Supported Code Translation
This addresses the problem of improving code translation efficiency and accuracy for software engineers, though it is incremental in exploring user interaction with existing models.
The study evaluated AI-supported Java-to-Python code translation with 32 software engineers, finding that participants produced code with fewer errors when aided by a model's outputs, and that providing multiple translations had a larger impact than varying translation quality.
Generative machine learning models have recently been applied to source code, for use cases including translating code between programming languages, creating documentation from code, and auto-completing methods. Yet, state-of-the-art models often produce code that is erroneous or incomplete. In a controlled study with 32 software engineers, we examined whether such imperfect outputs are helpful in the context of Java-to-Python code translation. When aided by the outputs of a code translation model, participants produced code with fewer errors than when working alone. We also examined how the quality and quantity of AI translations affected the work process and quality of outcomes, and observed that providing multiple translations had a larger impact on the translation process than varying the quality of provided translations. Our results tell a complex, nuanced story about the benefits of generative code models and the challenges software engineers face when working with their outputs. Our work motivates the need for intelligent user interfaces that help software engineers effectively work with generative code models in order to understand and evaluate their outputs and achieve superior outcomes to working alone.