Why it’s Important
The rise in the number of AI-powered coding assistants such as GitHub Copilot, Tabnine, AlphaCode by DeepMind and Project CodeNet by IBM is a promising development for users who want to save time and raise their productivity by streamlining their work through ML-powered solutions.
However, these solutions can only work well if it is trained on billions of lines of code to generate near accurate predictive results. Amazon, for instance, claims that its solution is based on open source repositories, API documentation and public forums in particular, to generate code snippets for a specific task, including integrating from the Cloud or a particular library.
But while the growth of automated programming assistance tools can benefit developers who want to cut down on time-consuming tasks, enterprises must remain vigilant about the quality of codes being generated and the tool’s integration with various infrastructures. It is also widely known that inferior codes can be offered no matter how extensive the training sources may be.
- Identify the organisation’s existing workflow tools and low-code platforms.
- Ensure that the platforms being used by the organisation provide visibility and sufficient capabilities to take developers' efforts and expand upon them.