Seq2Seq and Joint Learning Based Unix Command Line Prediction System

TL;DR

This paper introduces a novel Seq2Seq-based system that leverages a knowledge base to improve UNIX command line prediction accuracy, addressing the steep learning curve for new users.

Contribution

It presents a new approach using Seq2Seq models with a knowledge base, outperforming previous probabilistic inference methods in command prediction.

Findings

Achieved higher prediction accuracy than existing methods.

Demonstrated adaptability in command line interface prediction.

Enhanced user assistance in UNIX command line usage.

Abstract

Despite being an open-source operating system pioneered in the early 90s, UNIX based platforms have not been able to garner an overwhelming reception from amateur end users. One of the rationales for under popularity of UNIX based systems is the steep learning curve corresponding to them due to extensive use of command line interface instead of usual interactive graphical user interface. In past years, the majority of insights used to explore the concern are eminently centered around the notion of utilizing chronic log history of the user to make the prediction of successive command. The approaches directed at anatomization of this notion are predominantly in accordance with Probabilistic inference models. The techniques employed in past, however, have not been competent enough to address the predicament as legitimately as anticipated. Instead of deploying usual mechanism of recommendation systems, we have employed a simple yet novel approach of Seq2seq model by leveraging continuous representations of self-curated exhaustive Knowledge Base (KB) to enhance the embedding employed in the model. This work describes an assistive, adaptive and dynamic way of enhancing UNIX command line prediction systems. Experimental methods state that our model has achieved accuracy surpassing mixture of other techniques and adaptive command line interface mechanism as acclaimed in the past.