Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning

Abstract

Code summarization generates brief natural language description given a source code snippet, while code retrieval fetches relevant source code given a natural language query. Since both tasks aim to model the association between natural language and programming language, recent studies have combined these two tasks to improve their performance. However, researchers have yet been able to effectively leverage the intrinsic connection between the two tasks as they train these tasks in a separate or pipeline manner, which means their performance can not be well balanced. In this paper, we propose a novel end-to-end model for the two tasks by introducing an additional code generation task. More specifically, we explicitly exploit the probabilistic correlation between code summarization and code generation with dual learning, and utilize the two encoders for code summarization and code generation to train the code retrieval task via multi-task learning. We have carried out extensive experiments on an existing dataset of SQL and Python, and results show that our model can significantly improve the results of the code retrieval task over the-state-of-art models, as well as achieve competitive performance in terms of BLEU score for the code summarization task.

Code Summarization with Structure-induced Transformer

“…Hu et al (2018b) introduced API knowledge from related tasks while Cai et al (2020) introduced type information to assist training, which also gained promising results. Additionally, reinforce learning (Wan et al, 2018) and dual learning Ye et al, 2020) are also shown effective to boost model performance.…”

ComFormer: Code Comment Generation via Transformer and Fusion Method-based Hybrid Code Representation

“…Their proposed framework BVAE has two Variational AutoEncoders (VAEs): C-VAE for source code and L-VAE for natural language. Ye et al [44] exploited the probabilistic correlation between code comment generation task and code generation task via dual learning. Wei et al [22] also utilized the correlation between code comment generation task and code generation task and proposed a dual training framework.…”

Research and Application of Machine Learning in Automatic Program Generation

“…Ye W. et al [60] have presented an end-to-end model named CO3 for code retrieval and code summarization. CO3 leverages code generation to bridge programming language and natural language better via dual learning and multi-task learning.…”