A Machine Learning Approach to Convert Pseudo-Code to Domain-Specific Programming Language

The California Department of Energy oversees a set of standards and rules that new constructions (i.e., buildings) must adhere to within the State of California (the California Energy Code). Energy Code Ace (ECA) is a website that helps users plan their future construction in California and see if the plans for their structure comply with the California Energy Code. However, there are thousands of details and sections in the California Energy Code that need to be translated into questions and fields on the website for the user to fill out. To do this, the Department of Energy provides a web development team at Binary Evolution with XML schematics that contain details of each field and pseudo-code of the logic that fields should follow compared to the other fields on the ECA website. A very large portion of the developers’ job involves reading the pseudo-code and writing real code in a language developed by Binary Evolution. Reading and handwriting the code as described in the schematics can take up to hours of a developer’s time.This paper presents ECAi, a new approach for automatically generating executable code from pseudo-code schematics. ECAi uses a Decision Tree Classifier trained on 23,864 lines of pseudo-code and had to classify a line of pseudo-code into one of 13 categories achieving an 89.87% F1-score. Leveraging the classifier category, ECAi then calls a code-writing algorithm to parse the line of pseudo-code and writes the executable code that corresponds with the given pseudo-code line. The implementation of a prototype of the system shows a statistically significant (p=0.0116) decrease in the time it takes a developer to obtain an executable piece of code. ECAi has saved countless hours of development work during the maintenance workflow of ECA.