What characteristics research into software development tools? This talk explores how research can help understand why some tools are effective and some are not and can help drive to the development of more effective tools for software developers.
The Elusive Nature of Context: Why We Need It and Were We Might Find ItGail Murphy
Keynote at CASCON 2016. Describes the need for software to support the work patterns of humans so that the software works for humans instead of humans working for the software.
Impactful SE Research: Some Do's and More Don'tsGail Murphy
The document discusses ways for software engineering research to have impact. It recommends focusing on real-world problems, being open to opportunities, collaborating, adapting, and being generous with one's work. The document cautions against only focusing on "home runs", putting research above people, thinking innovation only happens in academia, ignoring issues of scale, relying only on open source, and neglecting the importance of messaging. It then describes Gail Murphy's career which exemplifies these principles through her academic works on Reflexion Models and Hipikat and her role in founding Tasktop to commercialize the Mylar technology, demonstrating impacts across multiple domains.
Is Continuous Adoption in Software Engineering Achievable and Desirable? Gail Murphy
ICSE 2016 Software Engineering in Practice keynote.
Continuity in software development is all about shortening cycle times. For example, continuous integration shortens the time to integrating changes from multiple developers and continuous delivery shortens the time to get those integrated changes into the hands of users. Although it is now possible to get multiple new versions of complex software systems released per day, it still often takes years, if ever, to get software engineering research results into use by software development teams. What would software engineering research and software engineering development look like if we could shorten the cycle time from taking a research result into practice? What can we learn from how continuity in development is performed to make it possible to achieve continuous adoption of research results? Do we even want to achieve continuous adoption? In this talk, I will explore these questions, drawing from experiences I have gained in helping to take a research idea to market and from insights learned from interviewing industry leaders.
The Rationale for Continuous Delivery by Dave FarleyBosnia Agile
The production of software is a complex, collaborative process that stretches our ability as human beings to cope with its demands.
Many people working in software development spend their careers without seeing what good really looks like.
Our history is littered with inefficient processes creating poor quality output, too late to capitalise on the expected business value. How have we got into this state? How do we get past it? What does good really look like?
Continuous Delivery changes the economics of software development for some of the biggest companies in the world, whatever the nature of their software development, find out how and why.
Identify Development Pains and Resolve Them with Idea FlowTechWell
With the explosion of new frameworks, a mountain of automation, and our applications distributed across hundreds of services in the cloud, the level of complexity in software development is growing at an insane pace. With increased complexity comes increased costs and risks. When diagnosing unexpected behavior can take days, weeks, or sometimes months, all while our release is on the line, our projects plunge into chaos. In the invisible world of software development, how do we identify what's causing our pain? How do we escape the chaos? Janelle Klein presents a novel approach to measuring the chaos, identifying the causes, and systematically driving improvement with a data-driven feedback loop. Rather than measuring the problems in the code, Janelle suggests measuring the "friction in Idea Flow", the time it takes a developer to diagnose and resolve unexpected confusion, which disrupts the flow of progress during development. With visibility of the symptoms, we can identify the cause—whether it's bad architecture, collaboration problems, or technical debt. Janelle discusses how to measure Idea Flow, why it matters, and the implications for our teams, our organizations, and our industry.
The document summarizes research in software engineering and development practices. It discusses several studies that have provided evidence for practices like rigorous inspections reducing errors, Conway's Law relating organizational structure to system structure, and physical distance not affecting post-release fault rates as much as distance in the organizational chart. The document advocates building development practices around these empirical facts and calls for continued work to systematically synthesize research evidence and practices.
A Technological Revolution in Automated Software DevelopmentGraham Kendall
This presentation argues that sofrware development has lagged behind other engineering disciplines (such as 3D printing) and that there is a need for a significant breakthrough so that software development can be done by a home user who does not posses a high level of technical programming knowledge
You are a young researcher on your first independent position. What can you do to get your research work funded? How do you frame your work, find the right partners, address the funding body?
Slides from Andreas Zeller's presentation at the New Faculty Symposium at ICSE 2017, Buenos Aires, Argentina.
The Elusive Nature of Context: Why We Need It and Were We Might Find ItGail Murphy
Keynote at CASCON 2016. Describes the need for software to support the work patterns of humans so that the software works for humans instead of humans working for the software.
Impactful SE Research: Some Do's and More Don'tsGail Murphy
The document discusses ways for software engineering research to have impact. It recommends focusing on real-world problems, being open to opportunities, collaborating, adapting, and being generous with one's work. The document cautions against only focusing on "home runs", putting research above people, thinking innovation only happens in academia, ignoring issues of scale, relying only on open source, and neglecting the importance of messaging. It then describes Gail Murphy's career which exemplifies these principles through her academic works on Reflexion Models and Hipikat and her role in founding Tasktop to commercialize the Mylar technology, demonstrating impacts across multiple domains.
Is Continuous Adoption in Software Engineering Achievable and Desirable? Gail Murphy
ICSE 2016 Software Engineering in Practice keynote.
Continuity in software development is all about shortening cycle times. For example, continuous integration shortens the time to integrating changes from multiple developers and continuous delivery shortens the time to get those integrated changes into the hands of users. Although it is now possible to get multiple new versions of complex software systems released per day, it still often takes years, if ever, to get software engineering research results into use by software development teams. What would software engineering research and software engineering development look like if we could shorten the cycle time from taking a research result into practice? What can we learn from how continuity in development is performed to make it possible to achieve continuous adoption of research results? Do we even want to achieve continuous adoption? In this talk, I will explore these questions, drawing from experiences I have gained in helping to take a research idea to market and from insights learned from interviewing industry leaders.
The Rationale for Continuous Delivery by Dave FarleyBosnia Agile
The production of software is a complex, collaborative process that stretches our ability as human beings to cope with its demands.
Many people working in software development spend their careers without seeing what good really looks like.
Our history is littered with inefficient processes creating poor quality output, too late to capitalise on the expected business value. How have we got into this state? How do we get past it? What does good really look like?
Continuous Delivery changes the economics of software development for some of the biggest companies in the world, whatever the nature of their software development, find out how and why.
Identify Development Pains and Resolve Them with Idea FlowTechWell
With the explosion of new frameworks, a mountain of automation, and our applications distributed across hundreds of services in the cloud, the level of complexity in software development is growing at an insane pace. With increased complexity comes increased costs and risks. When diagnosing unexpected behavior can take days, weeks, or sometimes months, all while our release is on the line, our projects plunge into chaos. In the invisible world of software development, how do we identify what's causing our pain? How do we escape the chaos? Janelle Klein presents a novel approach to measuring the chaos, identifying the causes, and systematically driving improvement with a data-driven feedback loop. Rather than measuring the problems in the code, Janelle suggests measuring the "friction in Idea Flow", the time it takes a developer to diagnose and resolve unexpected confusion, which disrupts the flow of progress during development. With visibility of the symptoms, we can identify the cause—whether it's bad architecture, collaboration problems, or technical debt. Janelle discusses how to measure Idea Flow, why it matters, and the implications for our teams, our organizations, and our industry.
The document summarizes research in software engineering and development practices. It discusses several studies that have provided evidence for practices like rigorous inspections reducing errors, Conway's Law relating organizational structure to system structure, and physical distance not affecting post-release fault rates as much as distance in the organizational chart. The document advocates building development practices around these empirical facts and calls for continued work to systematically synthesize research evidence and practices.
A Technological Revolution in Automated Software DevelopmentGraham Kendall
This presentation argues that sofrware development has lagged behind other engineering disciplines (such as 3D printing) and that there is a need for a significant breakthrough so that software development can be done by a home user who does not posses a high level of technical programming knowledge
You are a young researcher on your first independent position. What can you do to get your research work funded? How do you frame your work, find the right partners, address the funding body?
Slides from Andreas Zeller's presentation at the New Faculty Symposium at ICSE 2017, Buenos Aires, Argentina.
Beyond DevOps: Finding Value through RequirementsGail Murphy
DevOps practices have enabled faster delivery of software features. However, there remains a gap in consistently tracking how features connect to customer and organizational value. Requirements engineering needs to play a key role in identifying and linking features to value, as well as tracking value delivery and reassessing features over time. This will allow organizations to focus on delivering value rather than just features through their software development processes.
Tutorial on Using Amazon Mechanical Turk (MTurk) for HCI ResearchEd Chi
1. The document discusses using crowdsourcing platforms like Mechanical Turk for conducting user studies and collecting data for human-computer interaction (HCI) research.
2. It describes experiments where crowdsourced workers provided ratings of Wikipedia articles that correlated reasonably well with expert ratings, with some initial issues around gaming that were addressed through task design changes.
3. It provides tips for using crowdsourcing effectively for HCI research, such as using verifiable questions to ensure quality, balancing objective and subjective tasks, and considering different incentive mechanisms.
The document provides 12 tips for preparing a successful grant proposal for the European Research Council (ERC). The tips include understanding the ERC process and guidelines; starting the proposal process many months before the deadline; reserving several weeks solely for writing; getting feedback from multiple reviewers outside your specialty; leveraging local expertise in EU funding; emphasizing your unique achievements and qualifications; proposing a high-risk, high-reward project with a clear title and structure; getting straight to the point without jargon; polishing the proposal extensively; and recognizing that following the tips does not guarantee success but prevents misunderstandings. The overall aim is to craft a proposal that clearly communicates your message to reviewers in a manner that stands out against competing
DevDay 2016: Dave Farley - The Rationale for Continuous DeliveryDevDay Dresden
Many people working in software development spend their careers without seeing what good looks like. Our history is littered with inefficient processes creating poor quality output, too late to capitalise on the expected business value. How have we got to this state? How do we get past it? What does good really look like?
Continuous Delivery changes the economics of software development, find out how and why.
Pathways to Technology Transfer and Adoption: Achievements and ChallengesTao Xie
Dongmei Zhang and Tao Xie. Pathways to Technology Transfer and Adoption: Achievements and Challenges. In Proceedings of the 35th International Conference on Software Engineering (ICSE 2013), Software Engineering in Practice (SEIP), Mini-Tutorial, San Francisco, CA, May 2013. http://people.engr.ncsu.edu/txie/publications/icse13seip-techtransfer.pdf
This document provides an overview of TRIZ, the theory of inventive problem solving. It begins with definitions, explaining that TRIZ is a Russian acronym that was developed by Genrich Altschuller as a systematic approach to innovation. The document then outlines some classic and new TRIZ tools, such as the substance-field analysis for modeling problems, and the ideal final result process for defining desired solutions. It also discusses how TRIZ can be applied in various areas and influence different user types. In the overview, the document maps out examples of how TRIZ tools are interrelated and can be combined to solve problems in a systematic, non-random way.
The document summarizes a presentation about using Theory of Constraints (TOC) and the Russian Theory of Inventive Problem Solving (TRIZ) to speed up product innovation. It discusses how TOC and TRIZ can help break the dilemma between getting products to market quickly while avoiding design compromises. TRIZ is introduced as a methodical process for innovation based on analyzing over 3 million patents to identify patterns of technical evolution and invention. Both TOC and TRIZ provide techniques for resolving contradictions during problem solving without tradeoffs. The presentation aims to show how focusing TRIZ with TOC can rapidly create breakthrough products that satisfy market needs for speed.
Dark Matter, Public Health, and Scientific ComputingGreg Wilson
This document discusses the lack of computational skills among many scientists and proposes ways to improve this situation. It notes that most scientists focus on their research and do not engage with new technologies. Short, practical training courses targeting graduate students can help more scientists learn basic skills like shell scripting, version control, and testing. These skills allow scientists to be more productive and make new technologies more accessible, even if the skills are not publishable. The document advocates teaching a bit of computing in every course, hosting workshops, and changing incentives to better support computational reproducibility and open science.
Software Carpentry and the Hydrological Sciences @ AGU 2013Aron Ahmadia
This document discusses bringing computational skills training to hydrologists through Software Carpentry workshops. It notes that while many hydrologists are focused on their research, computational methods are now essential. Software Carpentry teaches practical skills like the Unix shell, version control with Git, Python and R programming, and databases. These intensive, short workshops have been effective at training graduate students. The document encourages hydrologists to host their own workshops and support computational literacy by discussing code and practices in their papers.
The document discusses best practices for long-term IT projects. It covers various topics like analysis, architecture, testing, code reviews, continuous integration/deployment, monitoring, metrics, automation, documentation, and communication. For each topic, it lists relevant concepts and principles, along with quotes from experts in software development.
NASA 's use of TRIZ (systematic innovation methodology)Richard Platt
This is the public presentation from NASA where they used a systematic innovation methodology known as TRIZ on "Improving Innovation Through TRIZ for the Microgravity Project Managers Working Group" for the NASA Glenn Research Center - Engineering Development Center at Lewis Field (2004). This is a companion presentation that was conducted by GE to NASA on TRIZ's use and application at their company. (See link here: https://www.slideshare.net/rplatt/general-electric-overview-presentation-of-triz-deployment-presented-to-nasatrizoverviewtonasa)
Michelin Using TRIZ in the Product Development of Tweel Richard Platt
This is a presentation on How Michelin Tires used TRIZ to develop their Tweel design that has been making its way into the commercial market for its application on multiple automotive and wheeled vehicle applications
The landscape for software testing has never been so broad. Applications today interact with other applications through APIs. And in return they leverage legacy systems, while they grow in complexity from one day to the next in a nonlinear fashion. So what does that mean for analysts, developers, and testers?
The 2016-17 World Quality Report suggests that AI will help. “We believe that the most important solution to overcome increasing QA and Testing Challenges will be the emerging introduction of machine-based intelligence,” the report states.
We have witnessed the mobile and computer revolution — now similarly — artificial intelligence (AI) is revealing its potential; not only by the way we live, but also within the majority of industries,. And software testing is no exception.
Facebook and Google aren’t the only companies applying AI techniques. In this session, we will explore how software testers can leverage AI and how tools may need to evolve. For instance, Helix ALM accelerates the development-to-release process, catches bugs earlier, and supports the transition to new development techniques.
In this webinar, we will also discuss three key elements that will significantly change software development with the evolution of “Artificial Intelligence”.
Short TRIZ Workshop for the University of the PhilippinesRichard Platt
This document summarizes a presentation on TRIZ (Theory of Inventive Problem Solving) given at the University of the Philippines. It discusses what TRIZ is, how it can help with engineering programs and product development, and examples of its use in universities and corporations. The presentation includes an exercise using the 40 Principles of TRIZ to solve problems in a game-like format.
Towards Mining Software Repositories Research that MattersTao Xie
- The document discusses challenges in achieving real-world impact from machine learning and software engineering research. It notes research may take 15-20 years from publication to widespread adoption in products.
- It provides examples of successful research with later impact, such as the LLVM compiler framework developed at the University of Illinois.
- For university groups, it suggests balancing producing high-quality research with training students, focusing on problems that matter now or in the future, collaborating with industry, and occasionally achieving unexpected impacts like the Whyper system. Starting a spin-off company is also discussed.
A presentation for the MBA on Digital Business Project Management in Pontifical Catholic University Rio Grande do Sul (PUCRS). The goal was to give an intro to Deep Learning and some of the challenges we have with ML in Production (MLOps).
Since 2008, over 100 students from 16 universities have worked in distributed teams on open source projects for course credit. Using Basie (http://basieproject.org) as an example, this talk explains how we have made that work. This talk was given at PyCon 2010 in Atlanta on February 20, 2010.
Good project from scratch - from developer's point of viewPaweł Lewtak
Slides for my talk at PHPExperience 2018 in São Paulo.
It's about 10 things I believe are important in order to have a successful long-term IT project.
120 years ago the emergent field of experimental psychology became embroiled in debates as to whether plateaus in performance are real (or not) and if so whether they were due to periods in which league-stepping methods (originally defined as a hierarchy of habits that enabled experts to step leagues while novices were ``bustling over furlongs or inches'') were being acquired (or not). 20 years ago both the human-computer interaction and cognitive science communities were seized with concerns over performance plateaus (i.e., extended periods of stable suboptimal performance) from experts. I briefly review this history with the aim of drawing distinctions between performance asymptotes and performance plateaus, and argue that remediating one is the domain of design while remediating the other is the domain of training.
The document discusses human centric innovation powered by information and communication technology. It describes how hyperconnectivity has increased dramatically over time due to growing data usage. New technologies allow collecting data from many sources and analyzing it to gain insights. This enables applications like optimizing transportation systems using real-time location data. The presentation provides examples of how Fujitsu applies these approaches, such as using sensors to monitor lettuce growth and help Airbus improve productivity. It argues that connected infrastructure and data can empower people by making information and opportunities more accessible.
Trends and technology are dramatically disrupting the accounting profession! This presentation, given at the IMA Meonske Conference on 4/25/14 highlights the major trends and what accountants can do about it.
Beyond DevOps: Finding Value through RequirementsGail Murphy
DevOps practices have enabled faster delivery of software features. However, there remains a gap in consistently tracking how features connect to customer and organizational value. Requirements engineering needs to play a key role in identifying and linking features to value, as well as tracking value delivery and reassessing features over time. This will allow organizations to focus on delivering value rather than just features through their software development processes.
Tutorial on Using Amazon Mechanical Turk (MTurk) for HCI ResearchEd Chi
1. The document discusses using crowdsourcing platforms like Mechanical Turk for conducting user studies and collecting data for human-computer interaction (HCI) research.
2. It describes experiments where crowdsourced workers provided ratings of Wikipedia articles that correlated reasonably well with expert ratings, with some initial issues around gaming that were addressed through task design changes.
3. It provides tips for using crowdsourcing effectively for HCI research, such as using verifiable questions to ensure quality, balancing objective and subjective tasks, and considering different incentive mechanisms.
The document provides 12 tips for preparing a successful grant proposal for the European Research Council (ERC). The tips include understanding the ERC process and guidelines; starting the proposal process many months before the deadline; reserving several weeks solely for writing; getting feedback from multiple reviewers outside your specialty; leveraging local expertise in EU funding; emphasizing your unique achievements and qualifications; proposing a high-risk, high-reward project with a clear title and structure; getting straight to the point without jargon; polishing the proposal extensively; and recognizing that following the tips does not guarantee success but prevents misunderstandings. The overall aim is to craft a proposal that clearly communicates your message to reviewers in a manner that stands out against competing
DevDay 2016: Dave Farley - The Rationale for Continuous DeliveryDevDay Dresden
Many people working in software development spend their careers without seeing what good looks like. Our history is littered with inefficient processes creating poor quality output, too late to capitalise on the expected business value. How have we got to this state? How do we get past it? What does good really look like?
Continuous Delivery changes the economics of software development, find out how and why.
Pathways to Technology Transfer and Adoption: Achievements and ChallengesTao Xie
Dongmei Zhang and Tao Xie. Pathways to Technology Transfer and Adoption: Achievements and Challenges. In Proceedings of the 35th International Conference on Software Engineering (ICSE 2013), Software Engineering in Practice (SEIP), Mini-Tutorial, San Francisco, CA, May 2013. http://people.engr.ncsu.edu/txie/publications/icse13seip-techtransfer.pdf
This document provides an overview of TRIZ, the theory of inventive problem solving. It begins with definitions, explaining that TRIZ is a Russian acronym that was developed by Genrich Altschuller as a systematic approach to innovation. The document then outlines some classic and new TRIZ tools, such as the substance-field analysis for modeling problems, and the ideal final result process for defining desired solutions. It also discusses how TRIZ can be applied in various areas and influence different user types. In the overview, the document maps out examples of how TRIZ tools are interrelated and can be combined to solve problems in a systematic, non-random way.
The document summarizes a presentation about using Theory of Constraints (TOC) and the Russian Theory of Inventive Problem Solving (TRIZ) to speed up product innovation. It discusses how TOC and TRIZ can help break the dilemma between getting products to market quickly while avoiding design compromises. TRIZ is introduced as a methodical process for innovation based on analyzing over 3 million patents to identify patterns of technical evolution and invention. Both TOC and TRIZ provide techniques for resolving contradictions during problem solving without tradeoffs. The presentation aims to show how focusing TRIZ with TOC can rapidly create breakthrough products that satisfy market needs for speed.
Dark Matter, Public Health, and Scientific ComputingGreg Wilson
This document discusses the lack of computational skills among many scientists and proposes ways to improve this situation. It notes that most scientists focus on their research and do not engage with new technologies. Short, practical training courses targeting graduate students can help more scientists learn basic skills like shell scripting, version control, and testing. These skills allow scientists to be more productive and make new technologies more accessible, even if the skills are not publishable. The document advocates teaching a bit of computing in every course, hosting workshops, and changing incentives to better support computational reproducibility and open science.
Software Carpentry and the Hydrological Sciences @ AGU 2013Aron Ahmadia
This document discusses bringing computational skills training to hydrologists through Software Carpentry workshops. It notes that while many hydrologists are focused on their research, computational methods are now essential. Software Carpentry teaches practical skills like the Unix shell, version control with Git, Python and R programming, and databases. These intensive, short workshops have been effective at training graduate students. The document encourages hydrologists to host their own workshops and support computational literacy by discussing code and practices in their papers.
The document discusses best practices for long-term IT projects. It covers various topics like analysis, architecture, testing, code reviews, continuous integration/deployment, monitoring, metrics, automation, documentation, and communication. For each topic, it lists relevant concepts and principles, along with quotes from experts in software development.
NASA 's use of TRIZ (systematic innovation methodology)Richard Platt
This is the public presentation from NASA where they used a systematic innovation methodology known as TRIZ on "Improving Innovation Through TRIZ for the Microgravity Project Managers Working Group" for the NASA Glenn Research Center - Engineering Development Center at Lewis Field (2004). This is a companion presentation that was conducted by GE to NASA on TRIZ's use and application at their company. (See link here: https://www.slideshare.net/rplatt/general-electric-overview-presentation-of-triz-deployment-presented-to-nasatrizoverviewtonasa)
Michelin Using TRIZ in the Product Development of Tweel Richard Platt
This is a presentation on How Michelin Tires used TRIZ to develop their Tweel design that has been making its way into the commercial market for its application on multiple automotive and wheeled vehicle applications
The landscape for software testing has never been so broad. Applications today interact with other applications through APIs. And in return they leverage legacy systems, while they grow in complexity from one day to the next in a nonlinear fashion. So what does that mean for analysts, developers, and testers?
The 2016-17 World Quality Report suggests that AI will help. “We believe that the most important solution to overcome increasing QA and Testing Challenges will be the emerging introduction of machine-based intelligence,” the report states.
We have witnessed the mobile and computer revolution — now similarly — artificial intelligence (AI) is revealing its potential; not only by the way we live, but also within the majority of industries,. And software testing is no exception.
Facebook and Google aren’t the only companies applying AI techniques. In this session, we will explore how software testers can leverage AI and how tools may need to evolve. For instance, Helix ALM accelerates the development-to-release process, catches bugs earlier, and supports the transition to new development techniques.
In this webinar, we will also discuss three key elements that will significantly change software development with the evolution of “Artificial Intelligence”.
Short TRIZ Workshop for the University of the PhilippinesRichard Platt
This document summarizes a presentation on TRIZ (Theory of Inventive Problem Solving) given at the University of the Philippines. It discusses what TRIZ is, how it can help with engineering programs and product development, and examples of its use in universities and corporations. The presentation includes an exercise using the 40 Principles of TRIZ to solve problems in a game-like format.
Towards Mining Software Repositories Research that MattersTao Xie
- The document discusses challenges in achieving real-world impact from machine learning and software engineering research. It notes research may take 15-20 years from publication to widespread adoption in products.
- It provides examples of successful research with later impact, such as the LLVM compiler framework developed at the University of Illinois.
- For university groups, it suggests balancing producing high-quality research with training students, focusing on problems that matter now or in the future, collaborating with industry, and occasionally achieving unexpected impacts like the Whyper system. Starting a spin-off company is also discussed.
A presentation for the MBA on Digital Business Project Management in Pontifical Catholic University Rio Grande do Sul (PUCRS). The goal was to give an intro to Deep Learning and some of the challenges we have with ML in Production (MLOps).
Since 2008, over 100 students from 16 universities have worked in distributed teams on open source projects for course credit. Using Basie (http://basieproject.org) as an example, this talk explains how we have made that work. This talk was given at PyCon 2010 in Atlanta on February 20, 2010.
Good project from scratch - from developer's point of viewPaweł Lewtak
Slides for my talk at PHPExperience 2018 in São Paulo.
It's about 10 things I believe are important in order to have a successful long-term IT project.
120 years ago the emergent field of experimental psychology became embroiled in debates as to whether plateaus in performance are real (or not) and if so whether they were due to periods in which league-stepping methods (originally defined as a hierarchy of habits that enabled experts to step leagues while novices were ``bustling over furlongs or inches'') were being acquired (or not). 20 years ago both the human-computer interaction and cognitive science communities were seized with concerns over performance plateaus (i.e., extended periods of stable suboptimal performance) from experts. I briefly review this history with the aim of drawing distinctions between performance asymptotes and performance plateaus, and argue that remediating one is the domain of design while remediating the other is the domain of training.
The document discusses human centric innovation powered by information and communication technology. It describes how hyperconnectivity has increased dramatically over time due to growing data usage. New technologies allow collecting data from many sources and analyzing it to gain insights. This enables applications like optimizing transportation systems using real-time location data. The presentation provides examples of how Fujitsu applies these approaches, such as using sensors to monitor lettuce growth and help Airbus improve productivity. It argues that connected infrastructure and data can empower people by making information and opportunities more accessible.
Trends and technology are dramatically disrupting the accounting profession! This presentation, given at the IMA Meonske Conference on 4/25/14 highlights the major trends and what accountants can do about it.
In this session we will explore the new approach to customer experience in recruiting, which is all about developing relationships and maintaining the conversation about the living brand that is your organization. Personalizing the experience before you even have an opportunity is more important than ever, this approach to talent management is a strategy that helps you create a pool for your company, complements your corporate culture, and aligns with business objectives. We will also review innovative and dynamic strategies for onboarding, retaining and engaging staff.
During this Spotlight Webinar, attendees will hear:
The role customer experience plays in talent development.
How to develop your organization’s living brand.
Employee engagement powerhouse tips.
How Humans Think - UX and Content Marketing - Cait Vlastakis Smith - Centerli...Centerline Digital
UX & Content Marketing: Navigating How Humans Think
Humans are strange, complex, fickle creatures. That said, it's our job as designers, content creators and marketers to deeply understand our audience as humans, not just "users" or "buyers." Why? So we stop making content people don't need, want or care about, and start delivering greater value.
The biggest challenge we face is putting aside our own personal preferences and biases. The best way tackle this challenge is by diving head first into audience research to understand who we're talking to. In essence: We have to think less like marketers and more like our audience.
This presentation given at DMFB explores action-based methods to begin untangling how people think. Throughout the presentation, we explored:
1) How the brain is structured to process information
2) Key questions to ask ourselves during content and design planning to help us think more like our audience
3) User experience (UX) research methods to apply throughout content marketing efforts, such as interviews and contextual inquiries
4) How to synthesize varying depths of customer insights into strategic outputs to guide content creation, and
5) A step-by-step "audience first" content planning guide that serves as a jumpstart tool for building content marketing programs around humans
Uncovering what motivates people, surfacing unknown needs and gathering insights will ultimately help us figure out how we can serve them better. Unpacking the answers to “Why” fuels user experience research. And when applied to content marketing, it paints a clearer picture of our audience and helps us create meaningful content and user-centered experiences that win attention, respect and loyalty.
For more information, please visit http://www.centerline.net or find me on Twitter at @caitvsmith.
A quick snapshot of important leadership lessons learned in business. Success hinges on teamwork, trust, ethics and a new lesson—lead by example.
I heard about this contest on www.slideology.com
Ethics is fundamentally about doing the right thing for people, not about merely complying with laws. Yet incorporating ethics into our design practice can be challenging. Our tools, processes, education, and the cultures we work in too often have limited to no support. Even the discussion can make people uncomfortable. Consider changing the conversation and rethinking ethical design. Talk about carrots (value) and not sticks (legality). Develop methods and practices to make ethics a core human-centered design constraint. (This was presented at UXPA 2017 in Toronto, Canada.)
The document discusses staying human in emerging technology. It notes that the impact of bad design decisions can slow adoption of new technology. It emphasizes that you are not the user, your certainties are assumptions, and most of what you think you know is not useful. The answers discovered today will likely be wrong tomorrow. It recommends thinking negatively, knowing nothing, talking to humans, building something tangible, and testing everything.
Slides from my keynote w/ Capgemini in Copenhagen - looking at how Microsoft, GE, KLM and Uber use Salesforce Marketing Cloud to innovate, disrupt and build customer relationships faster.
Human-Centric Design: How Design Thinking Can Drive Change and Deliver ValueCognizant
Through an iterative process of observation, ideation, rapid prototyping and testing, design thinking can help organizations craft a meaningful experience that seamlessly meshes the physical and digital interactions of people, processes and things.
Disruption and digital disruption 2016Dirk Laverge
disruption in our economy, and the impact of digital transformation
1) trends
2) drivers for change
3) business models
4) how to respond to the disruption?
Digital disruption refers to using digital technologies to disrupt existing businesses and industries. It requires companies to rethink their entire business model, not just technology, and adopt the mindset of a digital disruptor. This involves thinking differently and focusing on giving customers what they really want. Companies must exploit digital platforms, have an ongoing relationship with customers, and view failure as feedback. To behave as a digital disruptor, companies should pursue adjacent opportunities to provide new customer benefits, give customers a total product experience, and let customer needs guide their innovation. They must also disrupt themselves by establishing digital disruption as a priority, identifying barriers between departments, and designing teams to find disruptive opportunities.
Going Beyond Digital Disruption: harnessing the power of 'Design Thinking'SAP Customer Experience
The customer journey has no boundaries, it flows between channels, devices and departments.
* How is your marketing organization dealing with providing a seamless customer experiences across your entire company?
* Are your sales and marketing forging a strong, collaborative alliance that enables them to work in harmony?
* Do your marketing and commerce teams have the same vision of the right customer experience?
Disruptive innovation is only achievable if you are findings the problems worth solving, and empowering yourself and your teams with the right problem solving tools.
Nicholas keynote will explore how SAP has been harnessing the power of ‘Design Thinking’ reveals the linkages between “design” and disruptive products, services, and even business model innovation.
Presentation based on Harvard Business Review article: "What is Disruptive Innovation?", by Clayton M. Cristensen, Michael E. Raynor, and Rory McDonald – December, 2015 issue.
The theory of disruptive Innovation was introduced in the article: "Disruptive Technologies: Catching the Wave", by Joseph L. Bower and Clayton M. Christensen from the HBR january–february 1995 issue.
Making Effective, Useful Software Development ToolsGail Murphy
The document discusses making effective software development tools for humans. It presents examples of tools that either successfully or unsuccessfully bridge the mismatch between how developers work and how tools are designed. The Whyline debugging tool reduces debugging time by directly supporting developers' inquisitive nature. Mylyn improves productivity by leveraging how developers work in tasks and use episodic memory. In contrast, defect prediction tools showed no behavioral change when flags were added to code reviews, failing to bridge theory and practice. Overall, the document argues tools should be designed around how humans work, not just computers.
Machine Learning for (DF)IR with Velociraptor: From Setting Expectations to a...Chris Hammerschmidt
achine Learning for DFIR with Velociraptor: From Setting Expectations to a Case Study
By Christian Hammerschmidt, PhD - Head of Engineering/ML, APTA Technologies
Machine learning (ML) or artificial intelligence (AI) often comes with great promise and large marketing budgets for cybersecurity, especially in monitoring (such as EDR/XDR solutions). Post-breach, it often turns out that the actual performance falls short of its promises.
In this talk, we’ll briefly look at ML for DFIR: What tasks can ML solve, generally speaking? What requirements do we have for a useful ML system in cybersecurity/DFIR contexts, such as reliability, robustness to attackers, and explainability? What makes ML difficult to apply in cybersecurity, e.g. when thinking about false alerts or attackers attempting to circumvent automated systems?
After discussing the basics, we look at ML for velociraptor:
How can we process forensic data collected with VQL using machine learning (with a typical Python/Jupyter/scikit-learn/PyTorch stack)?
And how can we build artifacts that run ML directly on each endpoint, avoiding central data collection?
The talk concludes with a case study, showing how we significantly reduced time to analyze EVTX files in incident response cases, saving thousands of USD in costs and reducing time to resolution.
Bio: Chris Hammerschmidt did his PhD research on machine learning methods for reverse engineering software systems. Now, he’s heading APTA Technologies, a start-up building machine learning tools to understand software behavior .
Affiliation: APTA Technologies, https://apta.tech
Data science is not Software Development and how Experiment Management can ma...Jakub Czakon
Working on data science projects that are run as if they were software development can sometimes feel like trying to fit a square peg in a round hole. In this talk, I will explain why that happens and what people do to try and fix it. Lately, in the context of machine learning, the concept of experiment management, which treats ml experiments as first-class citizens, has been gaining a lot of traction. I will discuss what it is, what are the benefits of using it, and how you can apply it in your work to make run your projects more efficiently.
Is Agile Data Science just two buzzwords put together? I argue that agile is a very practical and applicable methodology, that does work well in the real world for all sorts of Analytics and Data Science workflows.
http://theinnovationenterprise.com/summits/digital-web-analytics-summit-london-2015/schedule
As data science workloads grow, so does their need for infrastructure. But, is it fair to ask data scientists to also become infrastructure experts? If not the data scientists, then, who is responsible for spinning up and managing data science infrastructure? This talk will address the context in which ML infrastructure is emerging, walk through two examples of ML infrastructure tools for launching hyperparameter optimization jobs, and end with some thoughts for building better tools in the future.
Originally given as a talk at the PyData Ann Arbor meetup (https://www.meetup.com/PyData-Ann-Arbor/events/260380989/)
The document discusses the Lean Startup methodology. Some key points:
1. Lean Startup advocates rapid prototyping and customer feedback to quickly test assumptions and evolve products faster than traditional methods.
2. It encourages frequent releases, often multiple times per day, to incorporate customer input through practices like continuous deployment.
3. A central tenet is reducing waste by increasing customer contact to test assumptions early and avoid incorrect directions. This aims to decrease the time to find product-market fit.
4. The document then provides an example story of a company applying Lean Startup tactics like prototyping ideas, using metrics to analyze customer behavior and decisions, and remaining flexible to change based on learning.
This document discusses pragmatic software innovation and value creation in software teams. It advocates for opportunistically pursuing innovation in everyday projects through a learning process across knowledge areas. Pragmatic software innovation involves inquiring into problem domain needs and finding valuable ways to meet those needs. The document presents examples of how means and ends are inseparable and change through iterations as new opportunities and understandings emerge.
The document discusses prototyping location apps with real data. It describes generating realistic datasets of people moving around cities by gathering check-in data from Foursquare tweets and visualizing the check-ins on maps. It also discusses generating social networks by extracting people and connection data from Wikipedia and DBpedia, including types of entities and links between pages. Code examples are provided to load and filter this data using Pig scripts on Amazon EMR.
Quality engineering in a world with AI and IoTSTePINForum
The document discusses how quality engineering is changing in an AI and IoT world. It notes that the new world of IT involves things like IoT connected devices, online marketing, continuous supply tracking, and just-in-time production. It also discusses how software is becoming more distributed through microservices and continuous delivery allows for thousands of teams to deploy updates 50 million times per year. Other topics covered include chaos engineering to test systems reliability, using machine learning to help with code analysis and failure prediction, and focusing on fast detection and response to failures rather than trying to prevent them.
Accelerating Innovation through Graph ThinkingNeo4j
The document discusses accelerating innovation through graph thinking using Neo4j's Innovation Lab. The Innovation Lab is a 3.5 day workshop that guides participants through generating use cases, modeling data, building prototypes, and presenting to executives. The lab focuses on discovery, technology, and viability to help organizations innovate using a connected data approach. Over 30 projects have shown decreased validation time and increased accuracy of use cases developed in the Innovation Lab.
Do you have an "analytics"? How analytics tools workSPLYT
Originally presented April 24, 2014 to Orlando's Tech on Tap meetup by Paul Fleetwood, Sr. Software Engineer at SPLYT. Paul gives an overview of how analytics platforms work and how SPLYT's revolutionary process delivers better answers to all your questions.
The document discusses how Rise Science, a young company focused on sleep improvement and user enjoyment, embraced a monolithic architecture to allow their small teams to iterate quickly without breaking things. It outlines some of the obstacles they faced with separate data silos and tooling, and how centralizing their data in a single database and using Django as their framework helped break down those silos and enabled self-serve analytics. This approach created a positive feedback loop where non-technical users could access data whenever needed, freeing up the data team to focus on more strategic problems.
Embracing the Monolith in Small Teams: Doubling down on python to move fast w...PyData
The document discusses how Rise Science, a young company focused on sleep improvement and user enjoyment, embraced a monolithic architecture to allow their small teams to iterate quickly without breaking things. It outlines some of the obstacles they faced with separate data silos and tooling, and how centralizing their data in a single database and using Django as their framework helped break down silos and enabled self-serve analytics to improve their data culture and product development cycles.
A workshop to demonstrate how we can apply agile and continuous delivery principles to continuously deliver value in machine learning and data science projects.
Code: https://github.com/davified/ci-workshop-app
HR Analytics: Using Machine Learning to Predict Employee Turnover - Matt Danc...Sri Ambati
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/-qfEOwm5Th4.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
In this talk, we discuss how we implemented H2O and LIME to predict and explain employee turnover on the IBM Watson HR Employee Attrition dataset. We use H2O’s new automated machine learning algorithm to improve on the accuracy of IBM Watson. We use LIME to produce feature importance and ultimately explain the black-box model produced by H2O.
Matt Dancho is the founder of Business Science (www.business-science.io), a consulting firm that assists organizations in applying data science to business applications. He is the creator of R packages tidyquant and timetk and has been working with data science for business and financial analysis since 2011. Matt holds master’s degrees in business and engineering, and has extensive experience in business intelligence, data mining, time series analysis, statistics and machine learning. Connect with Matt on twitter (https://twitter.com/mdancho84) and LinkedIn (https://www.linkedin.com/in/mattdancho/).
Playing Nice in the Product Playground #StrataHadoopIntuit Inc.
Intuit's Anu Tewary, Lucian Lita and Jonathan Goldman talk about how data scientists, engineers and product managers can work together to create innovative data products at O'Reilly Strata +Hadoop World 2015.
Bios:
Anuranjita Tewary is Director of Product Management at Intuit. She was a founder at Level Up Analytics, which was acquired by Intuit. Her previous roles have been data scientist at LinkedIn, and product management at AdMob and Microsoft. Anu is the founder of The Technovation Challenge, a global programming and entrepreneurship program for girls. The program is in its fifth year and has had over 3,500 participants from over 40 different countries. Anu holds a PhD in Applied Physics from Stanford and BS degrees in Physics and Math with Computer Science from MIT.
Lucian Lita is Director of data engineering at Intuit. Previously founder of Level Up Analytics (now Intuit), lead engineering, analytics at BlueKai (now Oracle), data scientist at Siemens healthcare. Received his PhD in computer science from Carnegie Mellon.
Jonathan Goldman is Director of Data Science and Analytics at Intuit. He co-founded Level Up Analytics, a premier data science consulting company focused on data science, big data, and analytics which Intuit acquired in 2013. From 2006–2009 he led the product analytics team at LinkedIn which was responsible for creating new data driven products. While at LinkedIn he invented the People You May Know product and algorithm which was directly responsible for getting millions of users connected and more engaged with LinkedIn. He received a PhD in physics in 2005 from Stanford where he worked on quantum computing and a BS in physics from MIT.
To view the presentation, visit: http://youtu.be/kkTXGtHrWAw
Insight Types That Influence Enterprise Decision Makers (Christian Rohrer a...Rosenfeld Media
Christian Rohrer: "Insight Types That Influence Enterprise Decision Makers"
Enterprise UX 2015 • May 13, 2015 • San Antonio, TX, USA
http://enterpriseux.net
[@IndeedEng] Large scale interactive analytics with Imhotepindeedeng
Link to video: https://www.youtube.com/watch?v=IZ-kC6ut1Lg
In a previous talk, we explained how we developed Imhotep, a distributed system for building decision trees for machine learning. We went on to describe how we build large scale interactive analytics tools using the same platform. This has kept our engineering and product organizations focused on key metrics by analyzing test results. It also gives our marketing organization timely and accurate insight into our data - allowing us to identify opportunities, spot trends, and learn about our job seekers. In this talk, Zak Cocos, who leads our Marketing Sciences team, and Product Manager Tom Bergman will discuss and provide examples of the valuable insights that can be gained by using Imhotep with almost any data set.
Weapons of Math Instruction: Evolving from Data0-Driven to Science-Drivenindeedeng
Donal McMahon, Director of Data Science at Indeed, presented how to transition from data-driven to science-driven product development. You’ll make better business decisions. It’s provable!
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This document discusses architecting flow in software engineering. It argues that software engineering involves multi-person, multi-version development across different scales, from individual components to integrated systems. It emphasizes that information flow between technical and social tools can improve productivity and quality, especially with higher levels of socio-technical congruence. The document presents examples of information flow within and between systems, and how tool architectures that support information flow along value streams can help track important items like features, defects, risks, and debts across teams and tools.
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The document discusses how software architecture and tool architecture impact a continual flow of value in software development. It argues that software architecture needs to evolve gracefully over time to enable value delivery, and that tools should support human and tool interactions to facilitate appropriate architectural decisions. However, tool architecture is often ignored despite likely interactions with software architecture. More research is needed to understand these relationships and how tool architecture can support software architecture evolution.
Software that requires maintenance and evolution presumably has value that causes the producers of the software—individuals and organizations—to invest in these activities. Given that there is almost always more that any given software package or product can provide, software producers should be motivated in enabling maintenance and evolution activities and should be interested in the software engineering research efforts that are undertaken to address identified pain points. Yet, despite efforts by providers of research results (software engineering researchers) and interest by recipients (software producing individuals and organizations), a gap remains and too few research results make their way into use. In this keynote talk from ICSME 2021, I focus on research results that take the form of software tools for software producers and explore what this gap is and how the gap might be bridged. This exploration aims to provide some practical tips for how to orient research to create usable and useful software tools.
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Keynote at International Conference on Performance Engineering (ICPE) 2020.
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Sven will talk about Atlassian’s journey from a monolith to a multi-tenanted architecture and how it affected the way the engineering teams work. You will learn how we shifted to service ownership, moved to more autonomous teams (and its challenges), and established platform and enablement teams.
Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.
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Hand Rolled Applicative User ValidationCode KataPhilip Schwarz
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
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Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
1. Human-Centric
Software Development Tools
Gail C. Murphy
University of British Columbia
Tasktop Technologies
@gail_murphy
A more restrictive license has been
chosen given use of licensed images.
11. 11
Why Does Context Matter?
Context
defines scope of research
narrows investigation of mismatches
narrows analysis of applicable theories
sets the stage for assessment
21. 21
What Kinds of Theories Exist?
Theories
Create:
- program comprehension
- socio-technical congruence
…
Borrow:
- social presence theory
- information foraging
…
26. 26
Is it Enough to Build it?
Assessment
Need to assess if tool is effective
Need to assess if tool can be used by humans effectively
If effective…try to match theory to “why”
28. 28
Mylyn
Assessment
Industrial Trial at IBM (2004)
Longitudinal field study (2005)
16 accepted users (99 started study)
13/16 user’s edit ratio improved with Mylyn
Adoption (today)
2 million downloads per month
37. 37
Epilogue
Summary
Invention Innovation
It takes almost as
much creativity to
understand a good
idea as to have it
in the first place.
— Alan Kay
Both “I”s are important
Innovation builds from
earlier inventions and
innovations
Research is the base
Doesn’t happen
enough
38. 38Thanks to…
Mylar + Early Tasktop: Tasktopians circa 2013
Mik Kersten, Rob Elves, Shawn Minto, Davor Cubranic, Taivo Evard, Nathan Hapke,
Wesley Coelho, Meghan Allen, Leo Dos Santos, Steffen Pingel
*Many* amazing grad
students and post-docs
Including Sarah Rastkar, Roberto Bittencourt, Thomas Fritz, Brian de Alwis,
John Anvik, Jan Hannemann, Martin Robillard, Robert Walker, Elisa
Baniassad, Gene Lee, Giovanni Viviani, Sedigheh Zolkatof, Sylvie Foss,
Deepak Azad, Rahul Jiresal, Nick Sawadsky, Alex Bradley, Owen Ou,
Petecharat Viriyakattiyaporn, Kaitlin Sherwood, Izzet Safer, Lyndon Hiew,
Trevor Young, Reid Holmes, Annie Ying, Albert Lai, Emerson Murphy-Hill,
Michalis Famelis, Marc Palyart Neil Ernst, and David Shepherd.