Abstract:
Developers face many problems in their day to day life. These problems vary from
environment setup problems, development problems to deployment problems. They
use various resources in their process of finding solutions for these problems they face.
Q&A platforms play a vital role helping developers resolve their problems since these
platforms provide experience from industry experts. Out of these Q&A platforms,
StackOverflow can be mentioned as one the standout Q&A platforms developers turn
to when they need to resolve a problem. StackOverflow consists of code examples
which play an important role in making it famous among developers.
Even though these code examples are very helpful for developers, they have to spend a
considerable amount of time to find relevant examples browsing through multiple posts
due to the lexical gap between natural language and code. Currently developers use few
keywords in order to search which means the search is limited to those few keywords.
Providing a solution to find code examples fairly quickly would impact on the
productivity of the developers.
The main objective of this project is to research and develop a solution to directly search
for code examples using natural language from Q&A platforms like StackOverflow.
This project focuses on following a deep learning approach along with natural language
processing techniques to provide a solution to the above mentioned problem.
After analyzing existing systems from general code search, this project has
implemented a mechanism with optimized sequence to sequence models using custom
question and code embeddings to bridge the lexical gap between code and natural
language. At the initial stage the models are trained to recommend code examples from
the python programming language. The project is also tested using actual user queries
which were extracted. The project was evaluated by technical and domain experts and
they have given positive feedback and suggestions for future improvements
