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A phrase is never enough to describe what the task at hand is. It may work for simpler use cases like the results on stack overflow. Otherwise I do not see it d
by mkagenius 7y ago
A phrase is never enough to describe what the task at hand is. It may work for simpler use cases like the results on stack overflow. Otherwise I do not see it doing better than a google search which leads me to stack overflow.
- mloncode 7y agoIt might be useful for scoped search and code navigation when you don't have a general-purpose question that is amenable to Stack Overflow. Let's say you are trying to find code in a repository that carries out a task but your keyword search turns up empty -- semantic search might be able to help you in that kind of situation.
- nbardy 7y agoYou're right. We'll need bigger datasets and more complex models to achieve more useful results, but machine learning models can give predictions in real time so you could have this running as you type in your IDE, instead of visiting a website, also with a model fine tuned to your code base. "simpler use cases like the results on stack overflow" could explain at least 10% of the work I do, so even just that tool would be very useful for mediocre programmers like myself. Behind all the hype, predictive text is something machine learning models are beginning to do very well. G-mail has rolled out a lot of similar features from advancements in deep learning models.
- mallamanis 7y ago[I'm one of the Microsoft Research people who worked on this] That's certainly true for simple use cases. Our goal here is to eventually also capture the long-tail of queries about a codebase. Often, within the domain of a project there is a set of natural language terms/jargon that describe complex tasks specific to the domain. Imagine for example a developer joining a mid-sized project and trying to find how to achieve some simple but project/domain-specific task.