Are you worried about your organization’s ability to cope up with the complexity of delivering at high velocity with excellent quality in multi-speed IT landscape and hybrid environments? Read some thoughts here about some Quality Engineering paradigms in DevOps world that we, Digital Assurance Services- Tech Mahindra, have implemented successfully with our customers.

In this talk, we will confront misconceptions about what accessibility is and isn’t while taking an interactive look at how to test for usable, accessible and inclusive applications.

In this talk, Liz introduces the Cynefin framework to help make sense of different types of situations and how to approach them: the obvious ones, the complicated ones which require expertise, the complex ones in which outcomes emerge, and the chaotic ones that we’re usually trying to avoid. Find out how these simple concepts can help us counter our innate human desire for predictability, enabling change and innovation; not just in software development, but in every aspect of our lives.

Agile Development and Continuous Delivery are rapidly becoming the de facto standard for software development. The key business driver behind these initiatives: delivering new functionality to your customers faster. This is easy to state in theory but much harder to achieve in practice: the level of quality of your services must be maintained! If your system goes down once a week every week, your customers are unlikely to be happy.

Are you tired of spending endless hours maintaining your automated test cases? Do you dread looking at test automation reports because of the tedious tweaks you know you’ll need to make from the latest updates from dev? Has your team lost its faith in test automation?

The ultimate goal of a DevOps approach is to deliver high-quality features to your customers at the pace they need. High performing DevOps shops point to continuous testing and test automation as key contributors to their success.

Accelerated delivery is now mandatory, especially for digital transformation. That is the reason continuous delivery, low-code, no-code development is getting utilized everywhere. Application teams need to release the new features faster or update the features faster.

As automation knocks on the door once again, for many there is a continued fear, when will I be automated away? While manual testing will continue for the foreseeable future, it never hurts to get introduced to the concepts of what might come next as a manual tester! The world of software development and software testing continues to change, and as it does, testing and development come to meet in the contested territory of DevOps.

Since AI driven test generation was first introduced in 2017, much has been learned. Millions of test steps were generated and executed, finding thousands of bugs. We will dig into how the technology works, where it works and doesn’t.

The ultimate goal of a DevOps approach is to deliver high-quality features to your customers at the pace they need. High performing DevOps shops point to continuous testing and test automation as key contributors to their success.

Since AI driven test generation was first introduced in 2017, much has been learned. Millions of test steps were generated and executed, finding thousands of bugs. We will dig into how the technology works, where it works and doesn’t.

Agile Development and Continuous Delivery are rapidly becoming the de facto standard for software development. The key business driver behind these initiatives: delivering new functionality to your customers faster. This is easy to state in theory but much harder to achieve in practice: the level of quality of your services must be maintained! If your system goes down once a week every week, your customers are unlikely to be happy.

Sure, machine learning is nothing new. And artificial intelligence is nothing new. However, when we are starting to leverage these not-so-new “intelligence” tools in our work as test engineers, that is quite new. This webinar gives some advice based on some research over the last 2 years of burgeoning ideas about how we are going to conduct performance testing and engineering work with AI and ML.

Agile Development and Continuous Delivery are rapidly becoming the de facto standard for software development. The key business driver behind these initiatives: delivering new functionality to your customers faster. This is easy to state in theory but much harder to achieve in practice: the level of quality of your services must be maintained! If your system goes down once a week every week, your customers are unlikely to be happy.

Here is your opportunity to ask any questions to Michael Bolton. Michael is a consulting software tester and testing teacher who helps people solve testing problems that they didn’t realize they could solve.

Sure, machine learning is nothing new. And artificial intelligence is nothing new. However, when we are starting to leverage these not-so-new “intelligence” tools in our work as test engineers, that is quite new. This webinar gives some advice based on some research over the last 2 years of burgeoning ideas about how we are going to conduct performance testing and engineering work with AI and ML.

Since AI driven test generation was first introduced in 2017, much has been learned. Millions of test steps were generated and executed, finding thousands of bugs. We will dig into how the technology works, where it works and doesn’t.

Machine Learning (ML) or artificial intelligence (AI) tools are the hottest topics right now in the testing industry, however, QA managers and manual testers have little to no knowledge about it. Thus, Yarin, the co-founder of TestCraft will go deeply into the details of ML functionality and explain the importance of ML & codeless Selenium – with real examples, statistics, and applications.

Are you on your way to be an independent consultant or starting a Software Testing startup? Do you feel it is more like a leap of faith or is it a very calculated step taken only when you are 100% sure of success? In order to succeed, there are some general heuristics that need to be taken care of, irrespective of which business you start but then there are specifics about the nature of business and subject matter involved that hold true for a particular kind of enterprise.

The value of machine learning is rooted in its ability to create models that guide future actions and to discover patterns missed by the naked eye. Machine learning methods are vastly superior in analyzing potential customer churn across data from multiple sources such as transactional, social media, and CRM sources.