Use AI and Go to program a command-line predictor

Programming Snapshot – Smart Predictions with Go

© Lead Image © alphaspirit, 123RF.com

© Lead Image © alphaspirit, 123RF.com

Article from Issue 263/2022
Author(s):

Because shell command sequences tend to reoccur, smart predictions can save you time typing. We first let the shell keep notes on what gets typed, before a Go program guesses the next command and runs it for you.

When I'm developing new Snapshot articles, I regularly catch myself typing the same commands in the terminal window time and time again. Text or code files modified by vi are sent to a staging area by git add foo.go, git commit feeds them to the local repository clone, and git push origin v1:v1 backs them up on the server. New builds of the Go source code in programming examples are triggered by the go build foo.go bar.go command, before tests are run by go test, and so on. Excessive typing like this needs to be automated. Because software development dinosaurs like myself keep fighting IDEs, I need a homegrown approach.

Although the shell history will find old commands, locating the command you need in this massive list, and running it again, requires some manual work. This is rarely worthwhile because retyping is often quicker than browsing 10 entries up the list or using a search string. The key is that you normally type shell commands in a defined order. For example, vi edits a Go file, then git saves the results, and go build compiles them. Learning this context, a smart tool would be quite capable of determining what comes next. Also the command sequences I use seem to depend on the directory in which I run them. In a Go project, I use the commands I listed earlier. For a text project, I would possibly use others, such as make publish to generate HTML or PDF files.

If a tool had access to the historical sequence of commands I issued in the past, and of the directories in which I ran them in, it could offer a good preselection of the commands likely to follow. In 90 percent of the cases, users would be able to find the next command and run it again. A dash of artificial intelligence accelerates and improves the whole thing, too. Figure 1 shows an example of a flowchart for a shell session. The edges in the graph mark the transitions between the commands and the percentages next to them the probability – derived from the history file – of a certain transition taking place. All paths originating from a state therefore add up to 100 percent.

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