Tips for Developing a Testing Strategy for DevOps Success
To achieve rapid time-to-market, businesses have embraced DevOps, which places a premium on speed and efficiency. But speed is not the only measure of DevOps success. To release better software faster, enterprises must optimize testing strategy and embed a culture of quality within their DevOps processes.
In this webinar, you will learn:
- How BDD can transform QA from a bottleneck to a speed enabler
- How to integrate quality and increase visibility throughout the SDLC
- How to help your VPs and Directors gauge the success of their current quality initiatives
RecordedDec 6 201856 mins
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Jeff Wilkinson, Managing Director at Accenture; Wayne Ariola, CMO at Tricentis
The testing industry is flooded with doomsday predictions: “The Testing Center of Excellence is dead! Testers will not be needed in the future! Robots will replace testers!”
While there is no question that today’s technologies have driven the evolution of the job, it is increasingly evident that successfully leveraging them will only extend the need for skilled testers. We are rapidly infusing bots, artificial intelligence and machine learning into quality processes – but doing so meaningfully requires a sound quality strategy and skilled testers at the helm.
In this session, Jeff Wilkinson, Managing Director at Accenture, explores what all this means for the future of testing, and what today’s testers must do in order to stay relevant. You’ll also hear about Accenture’s “Touchless Testing” approach, which employs these concepts.
Maria Boykins, Sr. Team Lead, Quality Engineering, Chick-fil-A
Chick-fil-A strives to create remarkable experiences — whether it’s great food and great service or great software. Advancing all three of these goals, the company recently transformed Quality Engineering from a small manual testing group to a powerful, influential arm of the engineering organization.
This session shares the mindset shift and process changes that got them there, “small wins” the team experienced along the way, and the best practices and techniques that add up to create high quality products.
Your testing expertise is great preparation for data warehouse and big data testing—which is fast-becoming a new opportunity for testers. If your organization isn’t already recruiting traditional testers to safeguard data quality, they probably will soon. Are you up for the challenge?
If so, watch two data warehouse testing leaders for the webinar Data Warehouse Testing—The Next Opportunity for QA Leaders. Wayne Yaddow is a data warehouse testing consultant who literally wrote the book on data warehouse. Raj Kanuparthi is a senior quality leader who drove the big data testing initiative at one the world’s leading financial service companies. Tricentis has gotten them together to give you a realistic look at why quality leaders are making data testing an integral part of their quality process, and the role that Tricentis Tosca experts are playing in these transformations.
- Why data testing is important
- How it compares to “traditional” software testing
- Where data warehouse testing fits into the broader quality strategy
- How leading enterprises got started with data warehouse testing
- The top challenges they faced
- The importance of automation—and how Tricentis helps
Most developers would tell you that their code is ready to release at any time. Most testers would disagree. This leads us to the single most important question associated with Continuous Delivery:
Does the release candidate have an acceptable level of risk?
In a highly-automated, rapid delivery process, you can’t make release decisions based on multiple, independent, and primarily binary (pass or fail) tests. You need a rapid assessment of the impact to the end-user experience—and immediate insight into whether releasing a particular build could cause palpable damage to the business.
If you’re not confident that you can answer this critical question accurately—on-demand—then Tricentis to learn how you can attain the required insight.
- How to establish a baseline to measure risk in the context of application delivery
- How risk coverage differs from code and test coverage
- Why, where, and how to measure risk within the automated release process