  Automatic code synthesis from natural language descriptions is a challenging
task. We witness massive progress in developing code generation systems for
domain-specific languages (DSLs) employing sequence-to-sequence deep learning
techniques in the recent past. In this paper, we specifically experiment with
\textsc{AlgoLisp} DSL-based generative models and showcase the existence of
significant dataset bias through different classes of adversarial examples. We
also experiment with two variants of Transformer-based models that outperform
all existing \textsc{AlgoLisp} DSL-based code generation baselines. Consistent
with the current state-of-the-art systems, our proposed models, too, achieve
poor performance under adversarial settings. Therefore, we propose several
dataset augmentation techniques to reduce bias and showcase their efficacy
using robust experimentation.
