Microsoft's Jigsaw tool leverages post‑processing and user feedback to improve large language model code synthesis, especially for Python Pandas, achieving up to 80% accuracy and aiming to automate code verification and debugging for developers.
This article outlines a three‑track roadmap for tackling software defects—starting with bug prediction using machine‑learning models, progressing through defect understanding and localization via static and dynamic analysis, and culminating in automated repair and program synthesis techniques, while highlighting key research directions and representative literature.
This article surveys the landscape of automatic program synthesis for logic code, covering visual programming, example‑driven generation, code‑completion models, intent inference, NL2SQL, NL2IFTTT, and advanced frameworks like TranX and Debuild, while highlighting current challenges and research directions.
