# Text Classification for Task-based Source Code Related Questions

arXiv: https://arxiv.org/abs/2111.00580 · HTML: https://mlsanity.com/paper/2111.00580/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 31 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019)
- [Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2006.03511/) (2020)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow](https://mlsanity.com/paper/1805.08949/) (2018)
- [SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair](https://mlsanity.com/paper/1901.01808/) (2018)
- [Tree-to-tree Neural Networks for Program Translation](https://mlsanity.com/paper/1802.03691/) (2018)
- [TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation](https://mlsanity.com/paper/1810.02720/) (2018)
- [TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation](https://mlsanity.com/paper/1804.09769/) (2018)
- [StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow](https://mlsanity.com/paper/1803.09371/) (2018)
- [DockerizeMe: Automatic Inference of Environment Dependencies for Python Code Snippets](https://mlsanity.com/paper/1905.11127/) (2019)
- [Path-Based Function Embedding and its Application to Specification Mining](https://mlsanity.com/paper/1802.07779/) (2018)
- [Using Distributed Representation of Code for Bug Detection](https://mlsanity.com/paper/1911.12863/) (2019)

## Abstract (arXiv)

There is a key demand to automatically generate code for small tasks for developers. Websites such as StackOverflow provide a simplistic way by offering solutions in small snippets which provide a complete answer to whatever task question the developer wants to code. Natural Language Processing and particularly Question-Answering Systems are very helpful in resolving and working on these tasks. In this paper, we develop a two-fold deep learning model: Seq2Seq and a binary classifier that takes in the intent (which is in natural language) and code snippets in Python. We train both the intent and the code utterances in the Seq2Seq model, where we decided to compare the effect of the hidden layer embedding from the encoder for representing the intent and similarly, using the decoder's hidden layer embeddings for the code sequence. Then we combine both these embeddings and then train a simple binary neural network classifier model for predicting if the intent is correctly answered by the predicted code sequence from the seq2seq model. We find that the hidden state layer's embeddings perform slightly better than regular standard embeddings from a constructed vocabulary. We experimented with our tests on the CoNaLa dataset in addition to the StaQC database consisting of simple task-code snippet-based pairs. We empirically establish that using additional pre-trained embeddings for code snippets in Python is less context-based in comparison to using hidden state context vectors from seq2seq models.