“AIOps” stands for “artificial intelligence in IT operations,” or using machine learning and data science to solve IT problems. AI can help with many IT functions, including detecting and remediating outages, monitoring availability and performance, and IT service management. Like with DevOps, a tester plays an important part with AIOps—they just have to determine what that is.
Many performance testers think that after a few years of experience, they automatically become performance engineers. However, it isn't that straightforward; the route to becoming performance engineer is a long and continuous journey. This article details the many things performance engineers need to do beyond performance testing, and it gives an outline for steps to take to advance your career.
Continuous operation tests find important bugs, partly as a result of their long operation and partly by increasing the probability of finding statistical bugs. However, CO tests have their own downsides. Mandating a periodic reset or reboot can work around these issues, as well as save time and cost for testing, reproduction, debugging, and fix verification.
Rigorous practices to reinforce performance and resilience, and testing continuously for these aspects, are great ways to catch a problem before it starts. And as with many aspects of testing, the quality of the performance practice is much more important than the quantity of tests being executed. Here are seven simple tips to drive an efficient performance and resilience engineering practice.
By incorporating performance testing early in a project lifecycle, software projects have a better chance to reach better quality and meet customer expectations. Baljeet Bilkhu shows the value of early performance testing.
To complement functional validation, software teams are expected to validate performance. But, according to Jun Zhuang, you must be prepared to invest time, personnel, and resources to benefit from performance testing.
Probably one of the most frustrating roles a manager has to master is how to know the true status of work being performed. To a developer, completing 80 percent of the work may be good enough, but is it even close to being really done? Masha Nehme shows techniques you can use to verify task completion.
The cloud and the rapid migration to mobile devices and the Internet of Things have made traditional software licensing schemes obsolete. Omkar describes new software monetization based on business, pricing models, and usage.
In this interview, Andreas Grabner of Dynatrace explains why you need to pay attention to your users' needs when you're doing your performance testing. He shares his performance testing approaches and explores the top problem patterns that you can learn to spot in your apps.
In this interview, Netflix’s Casey Rosenthal explains how to engineer trust within complex systems. He describes what it’s like to work at Netflix, how the company maintains complexity without sacrificing speed, and why all the teams don’t necessarily follow agile practices.
In this interview, TechWell speaks with Melissa Benua, a senior backend engineer for PlayFab. At STARWEST, she gave the presentation "Integration Testing as Validation and Monitoring." She also spoke at the Women Who Test event.
In this interview, Scott Barber talks about his experience in the industry as well as his multiple STAREAST presentations. This covers application performance testing as a simplified approach and a product owner's perspective on testing.
Managing the quality and performance of complex systems requires more than simply executing test cases and running load tests. You need to perform careful analysis of test results and production metrics. The sheer amount of data generated in production and testing makes analysis a huge challenge that is often left wanting. With the magic of machine learning (ML) and the application of data science techniques, you have the opportunity to derive valuable and actionable information from big data. Gopal Brugalette shares the basic concepts behind ML, covering clustering, classification, and predictive analysis. He shows you how to implement algorithms using open source tools and languages like Python and R.
We humans process millions of bits of information each day. In order to handle that data load, our brains have developed shortcuts to take advantage of patterns, shared knowledge, and experience. Unfortunately, sometimes those shortcuts lead us astray, causing us to draw inaccurate...
A hierarchy is an organizational network that has a top and a bottom, and where position is determined by rank, importance, and value. A holarchy is a network that has no top or bottom and where each person’s value derives from his ability, rather than position. As more companies seek the...
As organizations embrace agile and DevOps delivery models, non-functional performance testing becomes a challenge. While functional validation continues to mature in Agile, many organizations are either struggling to integrate application performance into the delivery model or are...