Requirements Management

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• Natural Language Processing (NLP)
• Application Development Trends - ADTmag
• Artificial Intelligence: The Bumpy Path Through Defense Acquisition | Eric J. Ehn
• AI driven requirements management - IBM
• Tools & Trends in Requirements Engineering | Hubert Spieß
• Why Agile Methodologies Miss The Mark For AI & ML Projects | Kathleen Walch - Forbes
• Managing workflow of customer requirements using machine learning | A. Lyutov, Y. Uyguna, and M. Thorsten Hütt - ScienceDirect
• reQlab | IT-Designers ...a state-of-the-art artificial intelligence tool improving natural language requirements. With its integration in Polarion, it can be used during the normal process of writing your requirements.
• Requirements Modeling Technology: A Vision For Better, Faster, And Cheaper Systems | Darrell Barker

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Being semi-structured, requirements led themselves to Natural Language Processing (NLP) nicely.

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Analyzing the Entire Program: Applying Natural Language Processing to Software Engineering A powerful, but limited, way to view software is as source code alone. Mathematical techniques, such as abstract interpretation and model checking, can indicate whether the program satisfies a formal specification. But, where does the formal specification come from? A program consists of much more than a sequence of instructions. Developers make use of test cases, documentation, variable names, program structure, the version control repository, and more. I argue that it is time to take the blinders off of software analysis tools: tools should use all these artifacts to deduce more powerful and useful information about the program. Researchers are beginning to make progress towards this vision. In this talk, I will discuss four initial results that find bugs and generate code, by making use of variable names, error messages, procedure documentation, and user questions.

Lecture - Requirements, Models, and Properties: Their Relationship and Validation Lecture by Mats Heimdahl, Ph.D. of University of Minnesota. Getting the system requirements right in a development project is crucial for success. One highly promising approach to rigorous requirements capture and definition is modeling of the requirements in formal notations. In such Model-Based Requirements Engineering, an initial set of natural language requirements forms the basis for an initial behavioral model of the intended system behavior and an initial formalization of the natural language requirements into formal requirements properties. Recent breakthroughs in formal verification now allow formal verification techniques to be used to analyze the set of requirements properties as well as the behavioral models. For example, the set of requirements properties can be checked for consistency and the behavioral model can be verified against the formalized requirements properties. The results from this analysis can then be used in an iterative requirements validation process where the analysis results serve as a basis for the modification, refinement, and extension of the set of requirements and/or the behavioral models to bring them in conformance with the truly desired (or notional) system requirements.

Analysis of Software Requirements with Natural Language Processing Prof. Lionel Briand University of Luxembourg, Luxembourg Huawei Workshop on Applications of Artificial Intelligence to Software Engineering December 15th 2017

Applying Machine Learning Techniques to the Flexible Assessment of Requirements Quality In the world of systems engineering, the importance of having high quality requirements is well known and that is why there are standards and guidelines that establish the characteristics that the requirements must have for considering them of good quality. To obtain quality measurements of the requirements it is common to use quantitative quality metrics based on established standards. However, the risk is to build assessment methods and tools that are both arbitrary and rigid in the parameterization and combination of metrics. This webinar is focused on the presentation of a flexible method to assess and improve the quality of requirements that can be easily adapted to different contexts, projects, organizations and quality standards, with a high degree of automation. In the method proposed, the domain experts contribute with an initial set of requirements that they have classified according to their quality, and their quality metrics are extracted. Then machine learning techniques are used to emulate the implicit expert’s quality function. A procedure to suggest least-effort improvements in bad requirements is also provided. The method is easily tailorable to different contexts, different styles to write requirements, and different demands in quality. The whole process of inferring and applying the quality rules adapted to each organization is highly automated.

AI for Requirements Management IBM is bringing the power of Watson AI to the engineering life cycle with Requirements Quality Assistant. Get started today! Learn more about IBM's Engineering Requirements Management solutions http://ibm.co/2UxZGJN

ExtractorAI Presentation IMP Consulting - Artificial Intelligence comes to Compliance--and it can save you time, money and lower your risk.

Quick Bytes with CTO Sky Matthews: Thoughts on AI and requirements management Learn more about AI and requirements management with IBM: https://ibm.co/2Xcbjrp IBM Watson IoT CTO Sky Matthews talks about how AI is playing a significant role in the systems that we build and also will play a big role in how we build these systems. How can engineers, developers and coders utilize and learn from the massive amounts of data that is generated from the design of things, and ultimately do it better?

Why does DoD struggle in using Machine Learning to automate decision making? Software Engineering Institute | Carnegie Mellon University Watch Elli Kanal discuss

The Full Stack: AI for Requirements Management Watch the experts tackle engineering complexity in real-time in this AI for Requirements Management episode of the Full Stack, from IBM Watson IoT. IBM's Engineering Requirements Management tools now feature the option to embed Watson AI to improve quality and minimize risk during the writing of requirements.

The Full Stack: Requirements Management Watch the experts tackle engineering complexity in real-time in this Requirements Management episode of the Full Stack, from IBM Watson IoT. IBM Engineering tools can provide valuable insights for systems engineers in the area of requirements management and automation

Requirements Management Tools

• The Best Requirements Management Tools Of 2020 | Ben Aston - dpm
• Artificial Intelligence & The Future Of Project Management (With Dennis Kayser From Forecast) | Ben Aston - dpm

Using Innoslate for Model Based Systems Engineering (MBSE) SPEC Innovations Dr. Steve Dam will walk you through the process of using Innoslate’s modeling and simulation capabilities while applying a MBSE methodology. At its core, Innoslate is a full model-based systems engineering tool. Within Innoslate, system models are formalized and capable of simulation to derive cost, schedule, and performance data. Your webinar will cover: Functional modeling Functional modeling is at the heart of how Innoslate derives new requirements and ensures logical accuracy. Physical modeling We can describe synthesizing the physical model in Innoslate with eight different diagrams, including the Asset Diagram, Layer Diagram, Block Definition Diagram, and Internal Block Diagram. Executing a model Innoslate includes a ‘Discrete Event Simulator’ to verify functional diagram’s logic, calculate cost, compute time, and quantify performance. Relating Requirements to Diagrams Requirements traceability ensures that the lifecycle and origin of a requirement is fully tracked. Innoslate includes relationship matrices to represent traceability relationships between entities in tabular view. Requirements Generation After modeling the system, often an engineer will derive textual requirements from the models by hand. Innoslate includes an automatic facility that generates requirements documents in a standard format (as outlined in “The Engineering Design of Systems: Models and Methods“).

IBM Requirements Management with Watson AI IBM Internet of Things IBM Engineering Lifecycle Management Extended helps teams keep on top of the complexity of developing smart, connected products. Running in the cloud or on premises, it helps systems engineers and software developers to deliver against requirements, respond efficiently to change and create high-quality designs faster—while controlling development costs and meeting compliance needs. Its tightly integrated tools cover the systems and software development lifecycle, including requirements management, modeling and simulation, quality management, configuration management and collaborative workflow planning and management. This demonstration explores how you can elevate your requirements management practice to help you and your teams of teams do what you do better. Learn more about Requirements Quality Assistant from IBM Watson IoT http://www.ibm.com/products/engineering-lifecycle-management-ext

Micro Focus Professional Services DevOps Model Office Watch this Micro Focus Professional Services Video to demonstrate our Model Office environment. For more information on our Model Office solution please visit our site at http://www.microfocus.com/en-us/services/devops-solutions Micro Focus Link: http://software/microfocus.com Micro Focus and HPE Software have joined to become one of the largest pure-play software companies in the world. Bringing together two leaders in the software industry, Micro Focus is uniquely positioned to help customers maximize existing software investments and embrace innovation in a world of Hybrid IT - from mainframe to mobile to cloud.

DOORS:101 An Introduction to DOORS 9.6 IBMfederal IBM Rational - Engineering Requirements Management DOORS® Family is a requirements management application for optimizing requirements communication, collaboration and verification throughout your organization and supply chain. It allows you to create relationships, trace dependencies, empower multiple teams to collaborate in near real-time and handle versioning and change management. http://www.ibm.com/products/requirements-management

Ultimate Visibility from High-level Planning to Micro-level Tracking PolarionSoftware In this webinar, you will learn how to hold your hi-level plans faithful and trivial to maintain and how to bring more transparency into cross-project dependencies and progress. In the second part, we will show you how to spot quickly available or overloaded resources and ensure you deliver projects on time and with maximized effectivity. Find out more on http://extensions.polarion.com/

Requirements Traceability with Jira The Atlassian Ecosystem (customers, partners & app vendors) frequently come up with very interesting and creative ways to overcome tough Software Development Lifecycle (SDLC) challenges. In our September webinar, our special guests, Erez Marcovich from Amdocs and Yaniv Shoshani from Methoda will share their expertise on how they together tackled Amdocs’ Requirements Traceability challenges using Jira, Structure, and Xray. To learn how to integrate Xray with Structure for Jira: http://bit.ly/2L7Fn2K To learn more about Xray: http://www.getxray.app/ To learn more about Methoda: http://www.methoda.co.il/en/ To learn more about Amdocs: http://www.amdocs.com To try out Structure for Jira: http://alm.works/structure-amdocs

The Expert

The Expert (Short Comedy Sketch) Starring: Orion Lee, James Marlowe, Abdiel LeRoy, Ewa Wojcik, Tatjana Sendzimir. Written & Directed by Lauris Beinerts Based on a short story "The Meeting" by Alexey Berezin Produced by Connor Snedecor & Lauris Beinerts Director of Photography: Matthew Riley Sound Recordist: Simon Oldham Production Designer: Karina Beinerte 1st Assistant Director: James Hanline Make-up Artist: Emily Russell Editor: Connor Snedecor Sound Designer: James Bryant Colourist: Janis Stals Animator: Benjamin Charles