Eran Kinsbruner

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About Eran Kinsbruner
Eran Kinsbruner is the chief evangelist at Perfecto (a Perforce company), product manager, and the author of the trilogy of books: 'The Digital Quality Handbook' , 'Continuous Testing for DevOps Professionals', and 'Accelerating Software Quality: AI and ML in The Age of DevOps', as well as a monthly columnist at the Enterprisers Project. He is a software engineering professional with nearly twenty years of experience at companies such as Matrix, Sun Microsystems, General Electric, Texas Instruments and NeuStar. He holds various industry certifications from ISTQB, CMMI, and others. Eran is a recognized mobile and web testing influencer and thought leader, as well as an experienced speaker in the major software engineering conferences. He is also a patent-holding inventor for a test exclusion automated mechanism for mobile J2ME testing, public speaker, researcher, and blogger. He can be found all over social media, including on Facebook, Twitter (@ek121268), LinkedIn, and his professional continuous testing blog - http://continuoustesting.blog
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Blog postThe digital landscape has significantly advanced over the past 1-2 years. The new normal in web applications include a shift toward progressive web apps (PWAs), advanced responsive websites, and Flutter apps. Ensuring both functional as well as API testing of such advance web applications is becoming harder and harder. While there are valid test automation … Continue reading "Expediting Web Testing with Puppeteer and Instant Test Automation Technology"
6 days ago Read more -
Blog postEssentially, Shift-Left is the practice finding defects and preventing them early in software delivery process. This is done by shifting the scanning of defects to improve code quality process to the left of Software Development Life Cycle (SDLC), which is usually composed of four phases of activities: design, develop, test, and release. Shift-Left also applies … Continue reading "Defect Detection and Security Prevention: How does Shift-Left Adoption Helps?"
2 months ago Read more -
Blog postMajority of organizations are already deep in their DevOps maturity. Most researches are showing over 40% that adopted the process, and are moving towards automated processes, shift-left, and fast delivery of value to customers. With that in mind, these organizations that span across different verticals from automotive, financial industries, gaming industries, and many more, need … Continue reading "How to Achieve both Coding Standard and Security Coverage Together with Safety Comp2 months ago Read more
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Blog postA decade of Agile, DevOps and Continuous Testing is behind us. I heard this week in a conference I was presenting the phrase “Agile is Dead” and other claims about testing being inefficient in many ways.
While many tools have evolved over the past years, and test automation practices and lessons learned have been communicated to practitioners, it is IMHO not enough to move the needle on the efficiency of test automation and continuous testing – especially in the demanding and ever cha4 months ago Read more -
Blog postAs more advanced technologies are entering the continuous testing landscape powered by AI/ML, organizations and especially, practitioners, are debating which is better, and why if any should they adopt codeless test authoring solutions? In this blog, I will be providing the various considerations to switch, and/or combine between the 2 test automation methods. TL,DR … Continue reading "Code-Based Test Automation vs. Codeless Automation"
9 months ago Read more -
Blog postAs we kick of a new decade of software development and testing, and as digital continuous to challenge test automation engineers that are trying to fit the testing within shorter then ever cycles, here are the top recommended software testing books to consider – the order isn’t the priority, they are all awesome books and equally recommended:
#1 Agile Testing – Lisa Crispin and Janet Gregory Readers will come away from this book understanding
How to get testers engaged in agile de1 year ago Read more -
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Blog postFor those who follow me, you already know that to build a continuous testing suites that scales, and that lasts for more than few software iterations, teams must follow rigorous processes.
When i say teams – I mean developers, test engineers and business testers.
This post isn’t about balancing the work load between personas, but rather on continuously sharpening the test suites value to the entire team.
As we close a decade and enter a ne1 year ago Read more -
Blog postGuest Blog Post by: Tzvika Shahaf, Director of Product Management at Perfecto & A Digital Reporting and Analysis Expert Intro One of the DevOps challenges in today’s journey for high product release velocity and quality, is to keep track of the CI Jobs/Test stability and health over different environments. Basically, this is one of the … Continue reading "Resolving The Quality Visibility of Continuous Testing Across The DevOps Pipeline Environments"
3 years ago Read more -
Blog postMobile testing by definition is an unstable, flaky and unpredictable activity. When you think you covered all corners and created a “stable” environment, still, your test cycle often get stuck due to 1 or few items. In this post, I’ll try to identify some of the key root causes for test automation flakiness, and suggest some preventive actions … Continue reading "Eliminating Mobile Test Automation Flakiness and More"
3 years ago Read more -
Blog postGuest Blog by Wendy Dessler
Photo by Alex Knight on Unsplash
According to USA Today, as many as 73 million jobs may not exist by 2030; however, we could see benefits like an increase in productivity and economic growth “counteract” job losses. But, will it? Will it not? That is a question several economists mull over.
What we can say, given our increasing reliance and incorporation of technology, we predict that coding will be a skillset job seekers will list on their3 years ago Read more
Titles By Eran Kinsbruner
Accelerating Software Quality: Machine Learning and Artificial Intelligence in the Age of DevOps
Sep 30, 2020
$9.99
The book “Accelerating Software Quality: Machine Learning and Artificial Intelligence in the Age of DevOps” is a complete asset for software developers, testers, and managers that are on their journey to a more mature DevOps workflow, and struggle with better automation and data-driven decision making.
DevOps is a mature process across the entire market, however, with existing Non-AI/ML technologies and models, it comes short in expediting release cycle, identifying productivity gaps and addressing them.
This book, that was implemented by myself with the help of leaders from the DevOps and test automation space, is covering topics from basic introduction to AI and ML in software development and testing, implications of AI and ML on existing apps, processes, and tools, practical tips in applying commercial and open-source AI/ML tools within existing tool chain, chatbots testing, visual based testing using AI, automated security scanning for vulnerabilities, automated code reviews, API testing and management using AI/ML, reducing effort and time through test impact analysis (TIA), robotic process automation (RPA), AIOps for smarter code deployments and production defects prevention, and many more.
When properly leveraging such tools, DevOps teams can benefit from greater code quality and functional and non-functional test automation coverage. This increases their release cycle velocity, reduces noise and software waste, and enhances their app quality.
The book is divided into 3 main sections:
• Section 1 covers the fundamentals of AI and ML in software development and testing. It includes introductions, definitions, 101 for testing AI-Based applications, classifications of AI/ML and defects that are tied to AI/ML, and more.
• Section 2 focuses on practical advises and recommendations for using AI/ML based solutions within software development activities. This section includes topics like visual AI test automation, AI in test management, testing conversational AI applications, RPA benefits, API testing and much more.
• Section 3 covers the more advanced and future-looking angles of AI and ML with projections and unique use cases. Among the topics in this section are AI and ML in logs observability, AIOps benefits to an entire DevOps teams, how to maintain AI/ML test automation, Test impact analysis with AI, and more.
The book is packed with many proven best practices, real life examples, and many other open source and commercial solution recommendations that are set to shape the future of DevOps together with ML/AI
DevOps is a mature process across the entire market, however, with existing Non-AI/ML technologies and models, it comes short in expediting release cycle, identifying productivity gaps and addressing them.
This book, that was implemented by myself with the help of leaders from the DevOps and test automation space, is covering topics from basic introduction to AI and ML in software development and testing, implications of AI and ML on existing apps, processes, and tools, practical tips in applying commercial and open-source AI/ML tools within existing tool chain, chatbots testing, visual based testing using AI, automated security scanning for vulnerabilities, automated code reviews, API testing and management using AI/ML, reducing effort and time through test impact analysis (TIA), robotic process automation (RPA), AIOps for smarter code deployments and production defects prevention, and many more.
When properly leveraging such tools, DevOps teams can benefit from greater code quality and functional and non-functional test automation coverage. This increases their release cycle velocity, reduces noise and software waste, and enhances their app quality.
The book is divided into 3 main sections:
• Section 1 covers the fundamentals of AI and ML in software development and testing. It includes introductions, definitions, 101 for testing AI-Based applications, classifications of AI/ML and defects that are tied to AI/ML, and more.
• Section 2 focuses on practical advises and recommendations for using AI/ML based solutions within software development activities. This section includes topics like visual AI test automation, AI in test management, testing conversational AI applications, RPA benefits, API testing and much more.
• Section 3 covers the more advanced and future-looking angles of AI and ML with projections and unique use cases. Among the topics in this section are AI and ML in logs observability, AIOps benefits to an entire DevOps teams, how to maintain AI/ML test automation, Test impact analysis with AI, and more.
The book is packed with many proven best practices, real life examples, and many other open source and commercial solution recommendations that are set to shape the future of DevOps together with ML/AI
Other Formats:
Paperback
$9.99
The Continuous Testing for DevOps Professionals book, is a definitive guide for DevOps teams covering practices required to excel in implementing and sustaining continuous testing (CT) in each step of the DevOps pipeline. The book was developed in collaboration with industry experts from the DevOps domain, that includes CloudBees, Tricentis, Testim.IO, Test.AI, Perfecto, and many more.
All profits from this book will be donated to http://code.org to support kids that wish to learn programming
The book addresses all the DevOps practitioners including software developers, testers, operation managers, and IT/Business executives. It consists of the 4 following sections:
1. Fundamentals of Continuous Testing
The first section of the book is fully focused on the definition of CT, the ways to build a CT plan and measure it, the role of each testing methodology within the DevOps pipeline, the continuous operation place within DevOps, the ways that practitioners can leverage smart test data to drive business-critical decisions, and in addition, an advanced overview on orchestrating the entire DevOps pipeline
2. Continuous testing for web apps
This section is fully dedicated to continuous testing of web applications that includes responsive web (RWD), progressive web apps (PWAs), leveraging headless browsers for testing web apps, accessibility testing 101, introduction to an innovative BDD framework (Gauge) for web testing, and more
3. Continuous testing for mobile apps
This section is focused on advanced mobile native apps techniques that cover the leading frameworks like Appium, Espresso, XCUITest, and in addition, provides a practical guide to testing complex react native apps using Appium.
4. Advancing continuous testing
The final section of the book is all about the future of continuous testing with a focus on machine learning and artificial intelligence and their roles in enhancing traditional testing practices. Also, this section addresses testing for IOT and OTT devices, and leveraging mock test data for CT.
All profits from this book will be donated to http://code.org to support kids that wish to learn programming
The book addresses all the DevOps practitioners including software developers, testers, operation managers, and IT/Business executives. It consists of the 4 following sections:
1. Fundamentals of Continuous Testing
The first section of the book is fully focused on the definition of CT, the ways to build a CT plan and measure it, the role of each testing methodology within the DevOps pipeline, the continuous operation place within DevOps, the ways that practitioners can leverage smart test data to drive business-critical decisions, and in addition, an advanced overview on orchestrating the entire DevOps pipeline
2. Continuous testing for web apps
This section is fully dedicated to continuous testing of web applications that includes responsive web (RWD), progressive web apps (PWAs), leveraging headless browsers for testing web apps, accessibility testing 101, introduction to an innovative BDD framework (Gauge) for web testing, and more
3. Continuous testing for mobile apps
This section is focused on advanced mobile native apps techniques that cover the leading frameworks like Appium, Espresso, XCUITest, and in addition, provides a practical guide to testing complex react native apps using Appium.
4. Advancing continuous testing
The final section of the book is all about the future of continuous testing with a focus on machine learning and artificial intelligence and their roles in enhancing traditional testing practices. Also, this section addresses testing for IOT and OTT devices, and leveraging mock test data for CT.
Other Formats:
Paperback
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