5 Requirement Lessons from a Robot

Executive Summary

Robots! They’re taking our jobs! Well, maybe if you’re in the manufacturing business… but how about requirements engineering? Just for fun, we unleashed a home-cooked neural network on a trove of 68 requirements documents to see if it could whip us up a set of 45 functional requirements that passed the QVscribe Quality Analysis test. The results? Surprisingly not bad! But before we get to the lessons learned from our favorite robot, let’s give you a little background info on the project.

We thought, “let’s take this neural network for a spin and see what it can do with requirements documents,” too. A pet project by one of our teams uses a character-level language model that determines the next character in sequence (plus some word-based sequence determination) based on probabilities derived from the training data.

As you probably know, real and freely available requirements documents are a little harder to source than fictional works. In the end, we dug up 68 good-looking documents from a mishmash from a variety of industries: software, medical devices, aerospace, and shipping – including documents from NASA. Each document was pre-processed to draw out the requirements themselves and fed in as training data to the robot.

In this guide, we go through the five key lessons for writing requirements from our Robot.

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