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Salesforce CodeT5 vs Github Copilot: A comparative guide to auto-code generators
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SEP 29, 2021
Creating large codes for software programs can sometimes be a time consuming and tedious task. Developers today are looking for methods and tools that can aid coding and improve turnaround times and accuracy for software development productivity. Therefore, automatic code generation capabilities are being discovered that can evolve within programming languages and IDEs that work at compile time. Automatic code generation can act as an amazing tool with potential use cases for enterprise settings. This article will discuss two of the most recently developed tools for automatic code generation, the Salesforce CodeT5 and Github Copilot.
The CodeT5 by Salesforce is an open-source machine learning tool that can understand and readily generate code in real-time. It is an identifier-aware unified pre-trained coder-encoder tool that enables a wide range of code intelligence applications. The tool aims to reduce time spent writing software as well as reduction of computational and operational costs. It consists of software code pre-training methods that boost a range of downstream applications in the software development lifecycle. CodeT5 possesses an uninformed model for natural language processing tasks, which reframes text-to-text with input, and output data always being strings of texts.
The existing code pre-training methods had two major limitations that CodeT5 addressed. First, they often rely on either an encoder-only model similar to BERT or a decoder-only model like GPT, which is suboptimal for generation and understanding tasks. Second, current methods can only adopt the conventional NLP pre-training techniques on source code by regarding it as a sequence of tokens like natural language, which largely ignores the rich structural information present in the programming language, information which is vital to fully comprehend code semantics.
Salesforce’s CodeT5 is built on a similar architectural scheme of Google’s T5 framework, but it incorporates better code specific knowledge, which endows the model with a better code understanding. It takes the code to be worked upon and its accompanying comments as a sequence to build and generate upon.
Some of the pre-training tasks of CodeT5 include:
Image Source: Salesforce Code T5
Some features of CodeT5 include:
Although CodeT5 can be a potential tool for auto code generation, there are still some ethical risks involved that one should consider beforehand. CodeT5 team says that they are still working on improving the following risks:
Github Copilot is a service tool created by GitHub and OpenAI and is described as an AI pair programmer. It is a plugin to Visual Studio Code and auto-generates code based on the current file’s contents and the current cursor location. Copilot can generate entire multiline functions and can even create documentation and tests based on the context of a file of code.
It is powered by a deep neural network language model called Codex, trained on several public code repositories on Github. It can help fine-tune and get state-of-the-art results on a wide range of NLP problems.
The Visual Studio Code sends comments and code typed by the developer to the Copilot service, which synthesises and suggests the implementation. Github states that the Copilot tool acts as a pen for generating code. The former claims that the Copilot understands more context than most of the currently available code assistants. It uses the provided context and synthesises a code to match. Copilot can work with a wide range of frameworks and languages such as Python, Javascript, TypeScript, Ruby, and Go. Alternative suggestions can be cycled through, and suggestions can be either accepted or rejected with an option to also manually edit the suggested code.
Image Source: Github Copilot
Some features of Github Copilot include:
Github Copilot might come along with unknown issues at implementation, which can be a potential risk factor, some of which include:
Although auto code generators are tools that aim to automate tedious and time-consuming coding work for developers, they come with their own set of limitations and risk factors. These issues seem to be still at work and require strong attention. In the coming future, this technology will enable existing engineers to be more productive, reducing manual tasks and helping them focus on other interesting aspects of work.
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