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Despite being an open-source operating system pioneered in the early 90s,UNIX based platforms have not been able to garner an overwhelming receptionfrom amateur end users. One of the rationales for under popularity of UNIXbased systems is the steep learning curve corresponding to them due toextensive use of command line interface instead of usual interactive graphicaluser interface. In past years, the majority of insights used to explore theconcern are eminently centered around the notion of utilizing chronic loghistory of the user to make the prediction of successive command. Theapproaches directed at anatomization of this notion are predominantly inaccordance with Probabilistic inference models. The techniques employed inpast, however, have not been competent enough to address the predicament aslegitimately as anticipated. Instead of deploying usual mechanism ofrecommendation systems, we have employed a simple yet novel approach of Seq2seqmodel by leveraging continuous representations of self-curated exhaustiveKnowledge Base (KB) to enhance the embedding employed in the model. This workdescribes an assistive, adaptive and dynamic way of enhancing UNIX command lineprediction systems. Experimental methods state that our model has achievedaccuracy surpassing mixture of other techniques and adaptive command lineinterface mechanism as acclaimed in the past.

Seq2Seq and Joint Learning Based Unix Command Line Prediction System