Software Testing and Defect Life Cycle are like two brothers from the same mother. They always go hand in hand when a project is being developed in the organizational paradigm.
In this article, we will tell you all about the different stages of the Defect Life Cycle that the software testers go through to provide a seamless and bug-proof product to their customers. Also, there are some frequently asked questions provided in the article to let you know about the queries testers answer daily.
The role of mobile apps in the currently thriving modernized setup has acquired an imperative stature. It is primarily to enhance the scale of convenience both at the consumer and enterprise-level that mobile apps have grown to become a major hit!
For the success of any software or application, there are different stages of testing. App quality is the foremost factor that predicts the success of a particular mobile application. Over the years, mobile app testing trends have shown serious variations. The sole reason for this shift can be attributed to the rising count of mobile devices.
Well, the main.ts is the entry point for the Angular application. Here, we’re not able to query instances of services or get them injected because we’re outside of the Angular application. It looks like that storage object is completely decoupled from the Angular application as well, so any dependency injection you’re after you’d have to do yourself.
Also of note: while I can’t find any explicit documentation on this, Amazon’s example of this Storage class has all of its properties as static, so I’m not sure if the configuration is expecting an instance of a class at all.
Today we held an internal, yep, an internal only mini Red Hat Summit.
This is a chance for talks to be presented select content to an internal worldwide audience at Red Hat, but that does not mean that all the content is confidential. That means I would be remiss if I didn’t share my session content.
Welcome to the future. Life is good, but it can be better. And why shouldn’t it be? All you need is to want it. Think about finally having everything you always wanted.
— Max Lord (Wonder Woman 1984)
I love side projects. They give me the opportunity to flex my creative muscles and tinker with tech like the Internet of Things (IoT) in new ways. Fortunately, I didn’t have to look far for my next one; a common conundrum for pet owners fueled this concept for an IoT dog collar.
My dog had been out in the backyard for a while. When I decided it was time to bring him back into the house, I couldn’t find him anywhere! After several minutes of searching and calling his name, I found him napping in the shade of a tree. If this scenario sounds all too familiar to you, then this post is for you!
The beginning of 2020 put the world in a hard situation. The coronavirus outbreak and its distribution has affected not only particular countries but the global economy on the whole. SMBs and enterprises worldwide are experiencing the catastrophic impact of the COVID-19 pandemic.
To cope with unexpected difficulties, companies are compressing their budgets and searching for the ways to reduce their costs. They are striving to optimize a number of processes to cut app development expenses and save efforts since they have no extra money to pay for outsourced development services.
For this document, we have created an Angular UI application and containerized it using NGINX for OpenShift 4.3.
By default, OpenShift Container Platform runs containers using an arbitrarily assigned user ID. This provides additional security against processes escaping the container due to a container engine vulnerability and thereby achieving escalated permissions on the host node.
Machine learning and artificial intelligence, in general, have been on everyone’s lips for some time now. While the topic of AI is in the foreground in the media, most people (especially the management) still don’t know how machine learning is best applied.
Ultimately, machine learning can be described as a synergetic relationship between man and machine. Machine learning in practice requires the application of the scientific method and human communication skills. Successful companies have the analytical infrastructure, know-how, and close collaboration between analysts and business professionals to translate these synergies into ROI.
Just a couple of years ago, serverless was a sparsely used technology, recognized mostly for its cost-efficiency. Today, we’re seeing a radical shift in the attitude towards serverless. Developers have begun to realize that serverless not only minimizes operational overhead but can considerably enhance core applications in production. As cloud vendors continue to develop their serverless offerings, serverless is being used in production more than ever before.
In this blog post, we explain what serverless is, dive into some of its major advantages, and point out some factors contributing to its growing popularity.
