GitHub Copilot, a recent AI-based assistant to help developers write code faster and better in the market, kicked off discussions across developer and data science communities. Since its announcement, there is a constant debate on Copilot being a blessing or a curse.
In simple terms, GitHub’s Copilot is an AI pair programmer. It is an outcome of the collaboration of GitHub with OpenAI. It gives suggestions on the specific lines of code or the entire functions. Copilot can suggest alternative ways to resolve the issues, explore new APIs, and write tests. It eliminates the need to spend a considerable amount of time looking for these answers on the web. The AI-based application easily adapts to the code writing style of the user to introduce better accuracy and speed.
One of the interesting aspects of GitHub’s Copilot is its training on the source code as well as natural language. It gives the tool the power to interpret the written code along with the comments while providing suggestions.
GitHub Copilot and It’s Set of Features
GitHub Copilot comes with numerous features and capabilities. Some of the significant ones are:
- It can easily convert comments to code. It can identify and understand bad comments.
- Practical tests without any toil
- Autofill feature for the recurring lines of code
With such usable and interesting capabilities, there arises a question if GitHub Copilot can replace the data scientists. The answer is a big NO. It indeed comes with several features that may not require extensive human intervention. However, GitHub Copilot does not write perfect code or provide accurate code predictions every single time. The application is trained on billions of source code lines covering almost all the GitHub repositories. However, there can be scenarios that the application may not cover. The suggestions given may not work or make sense all the time. There is continuous improvement essential for GitHub Copilot to correctly function and meet all the users’ needs.
Why does GitHub Copilot Matter for Data Scientists?
For any data science project, data Scientists perform data clean-up and data manipulation tasks as a primary step to start the project. An AI-based innovative tool like GitHub’s Copilot can be helpful in these tasks so that they can focus more on the data analysis part.
Copilot can be good with the languages with high boilerplate. It can also work well with languages with limited meta-programming functionality. Go is one such language. Data Scientists and programmers with good experience can also take assistance from Copilot while working on an unfamiliar language. It can help in making the structure and syntax right. Error identification can become easier with the involvement of such an application.
Also, it can point to adequate library functions saving considerable time.
Current Problems and Issues
Code generation and suggestions by GitHub Copilot are not accurate all the time. This is because of the functioning of the language models. GitHub Copilot training data includes a large number of GitHub repositories. Most of these are very old and have average code quality. GitHub Copilot automatically extracts the features from these lines of code and presents the suggestions.
GitHub Copilot also does not try to carry out code compilation. Codex also misses out on the recent libraries and language features since it is trained on the code written a few years back.
Also, GitHub Copilot is very new in terms of its use and application. There is a massive scope of improvement in the functionalities and capabilities.
GitHub Copilot as a Data Scientist
GitHub Copilot cannot replace the data scientists or the programmers any time soon. It can be helpful in improving the productivity of these human resources. It can also have an essential role in bringing down the costs of code creation and improvement.
GitHub Copilot is not error-free and requires significant improvements in the tasks it performs. It will change the current workflows with the automation of code predictions for code development and enhancements. It can also introduce changes in the existing roles of data scientists, engineers, and programmers. The involvement of AI is increasing in all the business domains impacting the workflows and the job responsibilities of the resources. The same impact can occur with the increase in the use and application of GitHub Copilot.
In the practical applications, GitHub Copilot has brought up several incorrect statements. It still has a lot to learn. Newbie Data Scientists and Programmers should avoid using such AI tools as it can spoil their concepts.
The efficiency of GitHub Copilot with the more enormous datasets is still something that is not clear. With new programming paradigms emerging, Copilot can save a significant amount of time in googling and provide suggestions directly. It can also reduce the dependency on StackOverflow and similar platforms for programming and data flow-related queries. The elimination of the human resources in the entire process can cause adverse implications on the overall efficiency, productivity, and quality. For instance, Copilot requires Data Scientists and Programmers to guide the direction of the projects.
GitHub Copilot Not To Replace Data Scientists But to Increase Productivity
The primary point to consider is the newness of Copilot since it is in the extremely early phase. With the inclusion of other code repositories and with better training, Copilot and its performance will continue to improve. GitHub will also develop new releases and versions of the tool with better efficiency and productivity levels.
It is also essential to move beyond the language models to obtain holistic solutions and applications. Data Scientists have the knowledge and skills to incorporate best software engineering practices, data modeling, testing, and other disciplines. These capabilities currently are not present with GitHub Copilot.
At present, GitHub’s Copilot is more of an assistive tool for Data Scientists and Programmers.