Difference between revisions of "Requirements Management"

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[https://www.youtube.com/results?search_query=ai+Requirements+management YouTube]
 
[https://www.youtube.com/results?search_query=ai+Requirements+management YouTube]
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[https://www.bing.com/news/search?q=ai+Requirements+management&qft=interval%3d%228%22 ...Bing News]
 
[https://www.bing.com/news/search?q=ai+Requirements+management&qft=interval%3d%228%22 ...Bing News]
  
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* [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]
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* [[Case Studies]]
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* [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
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** [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
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* [[Architectures]] for AI ... [[Generative AI Stack]] ... [[Enterprise Architecture (EA)]] ... [[Enterprise Portfolio Management (EPM)]] ... [[Architecture and Interior Design]]
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** [[Development]] ...[[Development#AI Pair Programming Tools|AI Pair Programming Tools]] ... [[Analytics]] ... [[Visualization]] ... [[Diagrams for Business Analysis]]
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* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
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** [[Risk, Compliance and Regulation]]
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* [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]]
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* [[Strategy & Tactics#Business Strategy/Consulting|Business Strategy/Consulting]]
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* [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
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* [[Generative AI for Business Analysis]]
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* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
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* [[Generative AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]] ... [[Microsoft]]'s [[BingAI]] ... [[You]] ...[[Google]]'s [[Bard]]
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* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
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* [[AI Governance]]
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* [[Prompt Engineering (PE)]] ... [[Prompt Engineering (PE)#PromptBase|PromptBase]] ... [[Prompt Injection Attack]]  
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* [[Traditional Architecture]]
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* [[Python]] ... [[Generative AI with Python|GenAI w/ Python]] ... [[JavaScript]] ... [[Generative AI with JavaScript|GenAI w/ JavaScript]] ... [[TensorFlow]] ... [[PyTorch]]
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* [[Enterprise Portfolio Management (EPM)]]
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* [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] ... [[Game Design | Design]]
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* [[Architectures]] supporting machine learning
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* [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
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* [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]]
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* [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]]  ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ... [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]]
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* [[Python]]   ... [[Generative AI with Python]] ... [[Javascript]] ... [[Generative AI with Javascript]] ... [[Game Development with Generative AI]]
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* [[Attention]] Mechanism  ...[[Transformer]] ...[[Generative Pre-trained Transformer (GPT)]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
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* [[Assistants]] ... [[Hybrid Assistants]] ... [[Agents]] ... [[Negotiation]] ... [[Langchain]]
 
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* [[Natural Language Processing (NLP)]]  ...[[Natural Language Generation (NLG)|Generation]] ...[[Large Language Model (LLM)|LLM]]  ...[[Natural Language Tools & Services|Tools & Services]]
 
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* [[Attention]] Mechanism  ...[[Transformer]] Model  ...[[Generative Pre-trained Transformer (GPT)]]
 
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* [[Diagrams for Business Analysis]]
 
 
* [https://adtmag.com/pages/topic-pages/ai.aspx Application Development Trends - ADTmag]
 
* [https://adtmag.com/pages/topic-pages/ai.aspx Application Development Trends - ADTmag]
 
* [https://apps.dtic.mil/dtic/tr/fulltext/u2/1053222.pdf  Artificial Intelligence: The Bumpy Path Through Defense Acquisition | Eric J. Ehn]
 
* [https://apps.dtic.mil/dtic/tr/fulltext/u2/1053222.pdf  Artificial Intelligence: The Bumpy Path Through Defense Acquisition | Eric J. Ehn]
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* [[Defense]]: [https://acqnotes.com/acqnote/tasks/requirements-development-overview Requirements Development] | [https://acqnotes.com DOD AcqNotes]
 
* [[Defense]]: [https://acqnotes.com/acqnote/tasks/requirements-development-overview Requirements Development] | [https://acqnotes.com DOD AcqNotes]
 
* [https://thebrazilianba.com/2023/01/24/chat-gpt-as-a-tool-for-business-analysis/ The Brazilian Business Analysis]
 
* [https://thebrazilianba.com/2023/01/24/chat-gpt-as-a-tool-for-business-analysis/ The Brazilian Business Analysis]
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* [https://www.xenonstack.com/blog/ai-in-requirement-management AI in Requirements Management | Benefits and its Processes | Jagreet Kaur - XENONSTACK]
  
  
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Being semi-structured, requirements led themselves to [[Natural Language Processing (NLP)]] nicely.
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Being semi-structured, requirements led themselves to [[Natural Language Processing (NLP)]] nicely
  
  
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"Will AI take over the Business Analyst role?" and "How do we stay relevant in a rapidly changing landscape?"
 
"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.
+
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:
 
Here are the 5 tips for business analysts that come from this session:
  
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* Understand the "The Knowledge Brief®" parts 1-6 to provide context for your organization's needs.
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* 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.
 
* 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.
 
* Use AI as a supplement to your skills, not as a replacement.
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<youtube>aB0tnT36UaI</youtube>
 
<youtube>aB0tnT36UaI</youtube>
 
<b>Applying Machine Learning Techniques to the Flexible Assessment of Requirements Quality
 
<b>Applying Machine Learning Techniques to the Flexible Assessment of Requirements Quality
−
</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.
+
</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 [[context]]s, 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 [[context]]s, 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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* [https://www.wordtemplatesonline.net/requirements-analysis-template/ 20+ Requirements Analysis Templates & Examples | Word Templates Onine]
 
* [https://www.wordtemplatesonline.net/requirements-analysis-template/ 20+ Requirements Analysis Templates & Examples | Word Templates Onine]
 
* [https://www.fp7-adventure.eu/wp-content/uploads/2012/04/D2.3_Requirement-Analysis-Report_v1.0_final.pdf Requirements Analysis Report | UVI, INESC, TUDA, UVA (lead), TANet, AZEV, ABB] ...Adventure: The Plug-and-Play Virtual Factory
 
* [https://www.fp7-adventure.eu/wp-content/uploads/2012/04/D2.3_Requirement-Analysis-Report_v1.0_final.pdf Requirements Analysis Report | UVI, INESC, TUDA, UVA (lead), TANet, AZEV, ABB] ...Adventure: The Plug-and-Play Virtual Factory
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* [https://dl.icdst.org/pdfs/files/f9c7c841bf3dbeb523ec2b51f1cd5bc1.pdf User Requirements Analysis Report | D M Sergeant, S Andrews, and A Farquhar] ...Embedding a VRE
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* [https://dl.icdst.org/pdfs/files/f9c7c841bf3dbeb523ec2b51f1cd5bc1.pdf User Requirements Analysis Report | D M Sergeant, S Andrews, and A Farquhar] ...[[Embedding]] a VRE
  
 
= The Expert =
 
= The Expert =

Latest revision as of 13:03, 6 November 2024

YouTube ... Quora ...Google search ...Google News ...Bing News



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



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

Creating a Requirements Template Using ChatGPT
2,800 views Streamed live on Feb 7, 2023 ChatGPT for Business Analysts Business analysis templates are available for sale on the web, but there's no longer any need to spend money purchasing these templates. Get the links here: https://bablocks.com/live/

CHAPTERS

  • 00:00 - We're live. Welcome.
  • 03:35 - The goal of the session is to create a requirements template using chatgpt.
  • 04:20 - Our agenda and Emal's secret agenda.
  • 05:36 - Links shared during the workshop.
  • 07:18 - "Can you give me a business requirements template?".
  • 10:15 - "Can you please give me some examples of functional requirements?"
  • 17:05 - "Can you give me an example of a REQUIREMENT that addresses the login feature?"
  • 19:25 - "Can you write me a USE CASE for this?"
  • 22:18 - "Can you write this in USER STORY format instead?"
  • 23:30 - Why use cases are better than user stories (in most situations).
  • 25:23 - Live chat comments.
  • 30:56 - "Can you give me some examples of non-functional requirements (NFR)?"
  • 35:18 - "Can you give me some examples of performance requirements?"
  • 42:22 - "Can you give me an example of response times?"
  • 43:48 - "Can you give me the EPICS for this project?"
  • 45:28 - "Can you give me the verification criteria for a login feature?"
  • 47:18 - "Can you give me stronger authentication rules for credential security requirements?"
  • 50:25 - Don't spend any more of your money buying templates.
  • 51:28 - "Can you write me a user story in GHERKIN format?"
  • 51:59 - Will chatGPT replace business analysts?
  • 56:40 - Live chat comments.
  • 57:48 - Can ChatGPT create visual models and diagrams?
  • 58:53 - Will Microsoft integrate ChatGPT into Jira or other tools?
  • 1:01:30 - When is your next BA course starting up? (BAPC)
  • 1:06:20 - Is the permissions matrix the right tool for describing features and functions?
  • 1:07:28 - Connect with me on LinkedIn: https://www.linkedin.com/in/emalbariali/

In this workshop, you will learn.

  • 1) What elements we should consider when creating a template
  • 2) How ChatGPT can help us build our own custom requirements template
  • 3) Other use cases of ChatGPT for business analysts

How to Use ChatGPT as a Business Analyst | AI and Business Analysis
In this video, I’ll show you how to use ChatGPT for business analysis work. Specifically, I’ll be asking the AI bot to draft me a use case for combining accounts. You’ll see that the bot can do a really great job at providing you a starting point, but you’ll still need to be mindful of and incorporate your business rules, goals, and other important factors to ensure that the final output aligns with the organization's objectives. If you’re looking to stay ahead of the curve, I encourage you to incorporate ChatGPT into your BA workflow.

Claim your use case template here: http://bit.ly/3YSqpSK

Learn about the Requirements Discovery Checklist Pack here: http://bit.ly/41fKKD2

Sign up for a free workshop to kickstart your success as a BA: https://bit.ly/3SzmF5c

Watch my video on how to write a use case here: https://bit.ly/3xM1jJj

Connect with us on: Website: https://bit.ly/3EfMmnm

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.

How to use ChatGPT to write product requirements, user stories & answer case studies
How to use ChatGPT to write product requirements, user stories & answer case studies. If you are a business analyst, product manager or a consultant, you spend lot of time doing market research or writing user stories and product requirements. ChatGPT could be a life saver for you by providing vital information and quite quickly. With little effort and customization, you can use it write user stories or product requirements. You can even use it to solve McKinsey style problems or case studies.

Impact of AI on Business Analysis with Author Paolo Sammicheli
In this episode, Paolo talks about the demand for Business Analysts in a world of AI. The purchase or read Paolo's book go here: https://paolo.sammiche.li/ About Paolo: Paolo Sammicheli is an Italian entrepreneur, International Speaker, and Author of the books "Scrum for Hardware," the first significant publication in the world on the topic, and "Scrum in AI - Artificial Intelligence Agile Development with Scrum and MLOps," with the foreword by Jeff Sutherland, co-author of Scrum and the Agile Manifesto. Today Paolo works as an Agile Business Coach, specialized in Scrum, Scrum@Scale, Kanban, Design Thinking, and Lean Startup, helping organizations uncover better ways to delight their customers. Scrum and Scrum@Scale Trainer for Scrum Inc, Lean-Agile Procurement Trainer, and Management 3.0 Facilitator. Executive Certificate in Management and Leadership from MIT Sloan School of Business. Link to purchase the foundational template: https://baknowledgeshare.com/downloads/business-analysis-foundational-templates/ Visit our website at https://baknowledgeshare.com/downloads/cbap-ccba-ecba-key-term-flashcards/ to purchase flashcards for business analysis key terms. Thank you for visiting this channel. Do consider subscribing to this channel to get notified of updates. Blog: https://baknowledgeshare.wordpress.com/

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.

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.

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

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

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

Domain Knowlege Using OpenAI ChatGPT (as a Business Analyst)
OpenAIs ChatGPT is a complete game-changer for business analysts. In this live session, we used this tool to quickly learn about Salesforce, the sales domain, and the insurance domain.

Requirements Management Tools

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“).

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.

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/

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

Discipline

(testable, cohesive, complete, consistent, atomic, traceable, unambiguous, prioritized, and solution-agnostic):

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

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