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| − | <b>The Full Stack: AI for Requirements Management
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| − | </b><br>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.
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| − | </b><br>Watch the experts tackle engineering complexity in real-time in this Requirements Management episode of the Full Stack, from [[IBM]] Watson IoT.
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Revision as of 11:56, 26 February 2023
Youtube search...
...Google search
Being semi-structured, requirements led themselves to Natural Language Processing (NLP) nicely.
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Business Requirements using AI (ChatGPT) with Deirdre Caren
This webinar will help business analysts understand and leverage Artificial Intelligence (AI) to improve their requirements gathering. This month we had one of the most exciting webinar series yet! Deirdre Caren from Agora Insights hosted a webinar about using artificial intelligence (AI) to gather accurate business requirements. It's a highly-discussed topic, and attendees could get insights into some important questions like
"Will AI take over the Business Analyst role?" and "How do we stay relevant in a rapidly changing landscape?"
The process Deirdre explained during the webinar was based on "The Knowledge Brief" part 7. However, she emphasized that understanding the previous six parts is crucial since they provide context for the organization's needs. Without this context, businesses can't harness the full potential of AI technology. Deirdre also discussed the potential role of AI in business analysis. While AI can provide insight into information, it lacks the creativity and context of human problem-solving abilities. Attendees agreed that business analysts could still play a crucial role in utilizing AI tools for data analysis and using their intuition and experience to stay relevant in the fast-changing world. By combining business analysis practices with AI technology, business analysts can improve project outcomes. The webinar was informative and motivating, encouraging attendees to keep up with technological advancements and understand their advantages and limitations. Deirdre's insights based on "The Knowledge Brief" part 7 highlighted the importance of context and its significant impact on the successful use of AI technology in business analysis.
Here are the 5 tips for business analysts that come from this session:
- Understand the "The Knowledge Brief®" parts 1-6 to provide context for your organization's needs.
- Develop a solid understanding of the business analysis profession through certification and training.
- Use AI as a supplement to your skills, not as a replacement.
- Combine business analysis practices with AI technology to improve project outcomes.
- Keep up with technological advancements and understand their advantages and limitations.
The Knowledge Brief
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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.
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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
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User Stories Using OpenAI ChatGPT (as a Business Analyst)
Can OpenAI's ChatGPT produce requirements? The answer shocked me, and it will likely shock you.
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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.
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ExtractorAI Presentation
IMP Consulting - Artificial Intelligence comes to Compliance--and it can save you time, money and lower your risk.
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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?
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Why does DOD struggle in using Machine Learning to automate decision making?
Software Engineering Institute | Carnegie Mellon University Watch Elli Kanal discuss
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Requirements Management Tools
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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“).
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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 https://www.ibm.com/products/engineering-lifecycle-management-ext
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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 https://www.microfocus.com/en-us/services/devops-solutions Micro Focus Link: https://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.
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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. https://www.ibm.com/products/requirements-management
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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 https://extensions.polarion.com/
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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: https://bit.ly/2L7Fn2K To learn more about Xray: https://www.getxray.app/ To learn more about Methoda: https://www.methoda.co.il/en/ To learn more about Amdocs: https://www.amdocs.com To try out Structure for Jira: https://alm.works/structure-amdocs
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Discipline
(testable, cohesive, complete, consistent, atomic, traceable, unambiguous, prioritized, and solution-agnostic):
The Expert
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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
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