Thanks to smartphones and their downsized keyboards, autocomplete has become a nearly ubiquitous feature of how we write these days. To save us precious seconds composing and (at least in my fat-thumbed case) correcting words, our keyboards now prompt us with suggestions of what we're trying to write to get the job done a little bit more easily. But email and messaging composing isn't the only area where artificial intelligence and semantic analytics are being used in this way. Today, a startup that has built a platform that applies the concept to the world of coding is announcing a round of funding to expand its business. Codota, an Israeli startup that provides an AI tool to developers to let them autocomplete strings of code that they are writing -- intended both to speed up their work (it claims to "boost productivity by 25%") and to make sure that it's using the right syntax and'spelled' correctly -- has picked up $12 million, a Series A led by e.ventures, with participation also from previous backer Khosla Ventures, along with new investors TPY Capital and Hetz Ventures.
You might've seen people on the internet saying "it's like my autocomplete gets me." Indeed, Keyboard protection AI has come a long way so much so that it can almost complete your sentences. So, why shouldn't developers get the benefit of auto-complete too? For years, IDEs (Integrated Development Environment) have tried to make development quicker by predicting the next part of a developer's code. Now, startups like Codota are using AI to help developers with code completion on any code editor.
One obvious place to start with AI in app development is the integrated development environment (IDE). AI features are a logical extension of the code automation features IDEs have offered over the years. While these AI IDE initiatives have generally disappointed programmers thus far, there are some encouraging trends. Take Microsoft's Visual Studio IntelliCode for C, which exemplifies the possibilities of IDE integration with AI. IntelliCode can detect and learn code style formats with the aim to improve readability and maintainability.
Today, programmers interested in machine learning potential talk about building apps with artificial intelligence and the tools for AI-based software development. Good examples include solutions like PyTorch and TensorFlow, among others. However, machine learning technology is affecting the programming world in yet another interesting way. We are talking about recent software development solutions that employ machine learning algorithms to ease and streamline the work of developers. Three of them are already selling on the market, while the other two are still in the testing phase.