Natural Language To Code Generation (NL2CG) represents a crucial intersection between human communication and programming, enabling the translation of human-readable instructions into executable code. This paper presents a systematic mapping study that explores the landscape of NL2CG techniques within a specific domain of application. The study investigates publications spanning the years 2013–2023, analyzing research trends, methodologies, and application domains. To keep the relevance of searched studies, my search is directed to studies published from 2013 to 2023. This systematic map contributes a comprehensive overview of the current state of NL2CG in a specific application context, shedding light on emerging trends, identifying gaps, and providing valuable insights for future research directions.