Developers' job can be a tough one.
Not only that they should create something out of nothing, or customizing the existing to better fit their needs, they also need to maintain it in an ongoing efforts. These people should be most productive when software development is inclusive and accessible.
This is why GitHub, the platform popular for coders and alike, is conducting research in various fields of techs to make sure that developers in needs can perform at their best with next generations of tools and workflows.
This is where GitHub introduces 'Experiments', a research effort that offers a sneak peek into what's behind the scene at GitHub.
For developers, including those at GitHub, patience is certainly a virtue.
This is especially true when it comes to research. People can't always see the benefit of their products right away, and sometimes some products' developments come to a halt, or even ditched completely, before ever seeing the light of day.
From machine learning, design and infrastructure, the development is constantly happening, but it is usually behind closed doors.
GitHub Experiments here, is trying to show what GitHub is trying to offer, before it happens. GitHub Experiments open the closed lab doors a bit, to show insight into ongoing research, showcases publicly available demos, and opens up discussion to the community.
For Experiments' first demo, GitHub have chosen Semantic Code Search.
"We’ve used machine learning to build semantic representations of code that allow you to use natural language to search for code by intent, rather than just keyword matching," explained GitHub about the project.
What this means, the Semantic Code Search project should allow GitHub in delivering the best search results that don’t necessarily contain the words people are searching for.
While this concept is nothing new, Semantic Code Search here should help developers that tirelessly scout the web for information using different keywords and phrases, by eliminating all of that by using machine learning instead.
As an added bonus, GitHub has also shared an open source example of the code and data for developers to reproduce results with.