Difference between revisions of "Requirements Management"
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| − | <b> | + | <b>Analyzing the Entire Program: Applying Natural Language Processing to Software Engineering |
| − | </b><br> | + | </b><br>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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| − | <b> | + | <b>Lecture - Requirements, Models, and Properties: Their Relationship and Validation |
| − | </b><br> | + | </b><br>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. |
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| − | <b> | + | <b>Analysis of Software Requirements with Natural Language Processing |
| − | </b><br> | + | </b><br>Prof. Lionel Briand University of Luxembourg, Luxembourg Huawei Workshop on Applications of Artificial Intelligence to Software Engineering December 15th 2017 |
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| − | <b> | + | <b>Applying Machine Learning Techniques to the Flexible Assessment of Requirements Quality |
| − | </b><br> | + | </b><br>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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| − | <b> | + | <b>AI for Requirements Management |
| − | </b><br> | + | </b><br>[[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 |
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| − | <b> | + | <b>ExtractorAI Presentation |
| − | </b><br> | + | </b><br>IMP Consulting - Artificial Intelligence comes to Compliance--and it can save you time, money and lower your risk. |
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| − | <b> | + | <b>Quick Bytes with CTO Sky Matthews: Thoughts on AI and requirements management |
| − | </b><br> | + | </b><br>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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| − | <b> | + | <b>Why does DoD struggle in using Machine Learning to automate decision making? |
| − | </b><br> | + | </b><br>Software Engineering Institute | Carnegie Mellon University Watch Elli Kanal discuss |
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| − | <b> | + | <b>The Full Stack: AI for Requirements Management |
| − | </b><br> | + | </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> | + | <b>The Full Stack: Requirements Management |
| − | </b><br> | + | </b><br>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 | ||
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Revision as of 14:18, 2 September 2020
Youtube search... ...Google search
- Case Studies
- Business Strategy/Consulting
- AI Governance
- Traditional Architecture
- Enterprise Portfolio Management (EPM)
- Architectures supporting machine learning
- Natural Language Processing (NLP)]]
- 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
Being semi-structured, requirements led themselves to Natural Language Processing (NLP) nicely.
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