Machine Learning-Based Code Auto-Completion Implementation for Firmware Developers

With the advent of artificial intelligence, the research paradigm in natural language processing has been transitioned from statistical methods to machine learning-based approaches. One application is to develop a deep learning-based language model that helps software engineers write code faster. Although there have already been many attempts to develop code auto-completion functionality from different research groups, a need to establish an in-house code has been identified for the following reasons: (1) a security-sensitive company (e.g., Samsung Electronics) may not want to utilize commercial tools given that there is a risk of leaked source codes and (2) commercial tools may not be applicable to the specific domain (e.g., SSD firmware development) especially if one needs to predict unique code patterns and style. This research proposes a hybrid approach that harnesses the synergy between machine learning techniques and advanced design methods aiming to develop a code auto-completion framework that helps firmware developers write code in a more efficient manner. The sensitivity analysis results show that the deterministic design results in reducing prediction accuracy as it generates output in some unexpected ways, while the probabilistic design provides a list of reasonable next code elements in which one could select it manually to increase prediction accuracy.