{"id":118495,"date":"2019-08-06T11:07:28","date_gmt":"2019-08-06T11:07:28","guid":{"rendered":"https:\/\/livablesoftware.com\/?p=118495"},"modified":"2019-08-09T09:42:39","modified_gmt":"2019-08-09T09:42:39","slug":"smart-intelligent-ide-programming","status":"publish","type":"post","link":"https:\/\/livablesoftware.com\/smart-intelligent-ide-programming\/","title":{"rendered":"I tested all intelligent IDEs (2019 edition)"},"content":{"rendered":"

Artificial intelligence is going to change the way software is developed. On the research side, we’ve seen many initiatives attempting to bring some of the benefits of AI and Machine Learning in particular to software development. Many of these papers are part of the curated repository “Awesome Machine Learning on Source Code”<\/a>. ML can help in all phases of software development<\/a>, not just at the code level. However, the combination of Machine Learning and Programming is where most of the works in the area are focusing right now.<\/p>\n

While I fully agree that AI is going to massively impact how we develop software, I was especially interested in checking out whether, today, a regular programmer has already available some useful intelligent tools that use AI techniques to enhance his productivity.<\/p>\n

So I searched for all so-called intelligent\/smart “copilots” for developers and tried to accomplish a “simple” programming task with their help: opening and reading a local text file. This is something simple enough for them to be able to help and that, at the same time, a task complex enough (for me) to make me look it up online every time I have to do it (I never manage t remember the names of the classes to be combined for this).<\/p>\n

Reading the description of some of the smart IDEs I found (“understands the world’s code and provides\u00a0you with the right suggestion at the right time” ,\u00a0“analyzes all the code on the web and gives you fast, smart completions ordered by popularity”<\/em>), it seemed that my little experiment would be a great success. But it wasn’t. In part because as I mention at the end, the quality of the training data plays a major role in the quality of the prediction and while many of the tools claim to be able to learn from Stack Overflow and other general public resources, it’s only when you train the tool with your own company data that results start to make sense.<\/p>\n

Let’s dive in the tools I tried (in no particular order). Note that since the first version of this post (2017) I’ve added new tools to the list and moved some others to the list of dead tools.<\/p>\n\n

Visual Studio IntelliCode<\/h2>\n

IntelliCode<\/a> is a very recent addition to Visual Studio. Created by Microsoft itself, IntelliCode saves you time by putting what you\u2019re most likely to use at the top of your completion list. IntelliCode recommendations are based on thousands of open source projects on GitHub each with over 100 stars. More specifically, Microsoft trains a base model on public code repositories and (optionally) a custom model based on the developer\u2019s own code repositories to find patterns in API usage. \u201cAs of May 2019, IntelliCode uses over 14,000 total repositories”<\/a>.<\/p>\n

As you can see in this screenshot, IntelliCode was able to properly recommend the method I should use in the loop for reading the fie. Note how, among all the potential methods to be used at this point of the code, IntelliCode stars and puts as a first recommendation the one I need (the readLine<\/em>).<\/p>\n

\"\"<\/a><\/p>\n

According to Microsoft, IntelliCode is also able to merge the recommendations with the context of your code in order to tailor the completion list to promote common practices. IntelliCode isn\u2019t limited to statement completion. Signature help also recommends the most likely overload for your context. And even more exciting, this post announces a new feature coming up soon: repeated edits<\/a>. IntelliCode will be able to automatically detect when you’re repeating the same modifications on your code over and over again (kind of a manual refactoring) and suggest you an automatic application of those changes in all places of the code where a similar modification is required.<\/p>\n

IntelliCode works with C#, Java, C++, Python, TypeScript\/JavaScript and XAML.<\/p>\n

Kite<\/h2>\n

Kite <\/a>defines itself as the plugin for your IDE that uses machine learning to give you useful code completions for Python.\u00a0<\/em><\/p>\n

In terms of features, it’s quite similar to some of the above tools. It is useful to give you inline documentation plus examples from its broad collection of samples that could<\/em> be useful to you. This is what I got for my file reading example.<\/p>\n

\"Kite<\/a><\/p>\n

Until now, suggestions were based on the classes\/methods referenced in your code but without taking into account the actual usage of those in the code. Latest versions improve this behavior by providing a new type Line-of-Code completions that incorporate context from code you’ve previously written to power smart completions up to a full line of code. Kite claims this improvement to reduce around 30-40% of the keystrokes needed to complete the code.<\/p>\n

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The following video is also a good explanation of how Kite works.<\/p>\n