Possible Solutions For Requirements Creep
By: Aswin Vijayakumar
Tags: Deep Learning, Software Engineering, Programming, Abstract Syntax Tree, Requirements Engineering

A Presentation

The text passage must include use-case description, problem description, identifiable features that may lead to an Agile Storyboard. Each text passage is allocated specific value for Story Points. From the Story Board, features are extracted into Subject Area, Features Sets and Features.

Agile Requirements Refinery (ARR)

(1) Fit Criterion (2) Traceability (3) Refinery (4) Deployment

ARR for (4) Deployment

Separating configuration of project from the project itself. Making the configuration sit on a separate location and deploying the project from an IDE that manages project configuration such as composer.json / POM, etc.

ARR for (2) Traceability

The Dotted notations shown on accordion, which shows the status of the requirements. If it is a subject area then it shows number of feature sets, if a feature, it shows the requirement description in single liner.

ARR for (3) Traceability

Patch Management using Refinery for the Sprint. For example, managing logic for composer.json, such as specifications for version usages, by using git-svn.

ARR for (1) Fit Criterion

Setup validation constraints for the substring off requirement for every fit criterion requirement represented as an FDD.

ARR for (2) Traceability

Search through the requirements from child to parent and vice-versa.

ARR for (1) Fit Criterion

The project blastoff stage will expose the metrics discussed in the first stakeholder meeting such as accuracy, benchmark, rest interfaces, implementation roadmap, estimated cluster points, cluster points cascaded and staged similar to a Gantt Chart.

ARR for (3) Refinery

The cluster of requirements are categorized into a distance metric based on different AST based parsing algorithms. The DEA data is obtained by a covariance matrix using Word2Vec and the results of AST parsing. A pre-configured AST based parsers are made available and the document routed to golden record set.

ARR for (1) Fit Criterion

Convert the DEA problem to an inertia bias problem using the given DMU(s) using a Gaussian from Word2Vec and outlier theory. This will size the problem further based on applications of decision theory. This will ensure the Gantt Chart cascading is performed properly.

I have a reason to believe this because Cognitive Representations are Systematic and Compositional. So if we adhere to the sample AST parsed requirements, then that would be confirming the bias problem. Once an estimated Gantt Chart is done, it is easy to plan for the later stages.

ARR for (2) Traceability

Decision for GO/NO-GO is made based on passes through Refinery on based on pre-computed metrics such as Gini Impurity and Min. Mean Square Estimation.

ARR for (4) Deployment

This deals with local environment based deployments, such as shell scripts, that run and execute a Docker box for example.

ARR for (1) Refinery

The inertia bias problem is approximated with pi, to find the new DMU(s) to solve the stewardship problem. Each AST tree is measured using depth vs breadth, as well as the area it covers, the tree depth is decided based on resolution of requirements and the tree split is decided based on dimensional information of the representation problem such as subject area, feature sets and features will have a split size of 2.

Aswin Vijayakumar