  Pre-trained code generation models (PCGMs) have been widely applied in neural
code generation which can generate executable code from functional descriptions
in natural languages, possibly together with signatures. Despite substantial
performance improvement of PCGMs, the role of method names in neural code
generation has not been thoroughly investigated. In this paper, we study and
demonstrate the potential of benefiting from method names to enhance the
performance of PCGMs, from a model robustness perspective. Specifically, we
propose a novel approach, named RADAR (neuRAl coDe generAtor Robustifier).
RADAR consists of two components: RADAR-Attack and RADAR-Defense. The former
attacks a PCGM by generating adversarial method names as part of the input,
which are semantic and visual similar to the original input, but may trick the
PCGM to generate completely unrelated code snippets. As a countermeasure to
such attacks, RADAR-Defense synthesizes a new method name from the functional
description and supplies it to the PCGM. Evaluation results show that
RADAR-Attack can reduce the CodeBLEU of generated code by 19.72% to 38.74% in
three state-of-the-art PCGMs (i.e., CodeGPT, PLBART, and CodeT5) in the
fine-tuning code generation task, and reduce the Pass@1 of generated code by
32.28% to 44.42% in three state-of-the-art PCGMs (i.e., Replit, CodeGen, and
CodeT5+) in the zero-shot code generation task. Moreover, RADAR-Defense is able
to reinstate the performance of PCGMs with synthesized method names. These
results highlight the importance of good method names in neural code generation
and implicate the benefits of studying model robustness in software
engineering.
