Natural Language (NL) programming, the concept of synthesizingcode from natural language inputs, has garnered growing interest among the software community in recent years. Unfortunately,current solutions in the space all suffer from the same problem,they require many labeled training examples due to their data-driven nature. To address this issue, this paper proposes an NLU-driven approach that forgoes the need for large numbers of labeled training examples. Inspired by how humans learn programming, this solution centers around Natural Language Understanding and draws on a novel graph-based mapping algorithm. The resulting NL programming framework, HISyn, uses no training examples, but gives synthesis accuracies comparable to data-driven methods trained on hundreds of samples. HISyn meanwhile demonstrates advantages in terms of interpretability, error diagnosis support,and cross-domain extensibility. To encourage adoption of HISyn among developers, the tool is made available as an extension for the Visual Studio Code IDE, thereby allowing users to easily submit inputs to HISyn and insert the generated code expressions into their active programs. A demo of the HISyn Extension can be found at https://youtu.be/KKOqJS24FNo.