Converting Epics/Stories into Pseudocode using Transformers
Gaurav Kolhatkar Akshit Madan Nidhi Kowtal Satyajit Roy Sheetal Sonawane
The conversion of user epics or stories into their appropriate representation
in pseudocode or code is a time-consuming task, which can take up a large
portion of the time in an industrial project. With this research paper, we aim
to present a methodology to generate pseudocode from a given agile user story
of small functionalities so as to reduce the overall time spent on the
industrial project. Pseudocode is a programming language agnostic
representation of the steps involved in a computer program, which can be easily
converted into any programming language. Leveraging the potential of Natural
Language Processing, we want to simplify the development process in
organizations that use the Agile Model of Software Development. We present a
methodology to convert a problem described in the English language into
pseudocode. This methodology divides the Text to Pseudocode conversion task
into two stages or subtasks, each of which is treated like an individual
machine translation task. Stage 1 is Text to Code Conversion and Stage 2 is
Code to Pseudocode Conversion. We find that the CodeT5 model gives the best
results in terms of BLEU score when trained separately on the two subtasks
mentioned above. BLEU score is a metric that is used to measure the similarity
between a machine-translated text and a set of reference translations.