In the era of agile and DevOps, release decisions need to be made rapidly—preferably, even automatically and instantaneously. Test results that focus solely on the number of test cases leave you with a huge blind spot. If you want fast, accurate assessments of the risks associated with promoting the latest release candidate to production, you need a new currency in testing: Risk coverage needs to replace test coverage.
With 2020 upon us, software development firms seeking to increase their agility are focusing more and more on aligning their testing approach with agile principles. Let’s look at seven of the key agile testing trends that will impact organizations most this year.
Teams everywhere are looking to speed up testing without sacrificing quality, so once again, some of the top articles last year were about continuous integration, machine learning, and—of course—how to best implement and use test automation. But readers were also interested in what they shouldn't be doing, with two high-ranking articles about test practices we should stop and a tool you may be misusing.
Testers gather lots of metrics about defect count, test case execution classification, and test velocity—but this information doesn't necessarily answer questions around product quality or how much money test efforts have saved. Testers can better deliver business value by combining test automation with regression analysis, and using visual analytics tools to process the data and see what patterns emerge.
The internet of things (IoT) continues to proliferate as connected smart devices become critical for individuals and businesses. Even with test automation, performing comprehensive testing can be quite a challenge.
Because enterprise applications are highly interconnected, development in stages puts a strain on the implementation and execution of automated testing. Service virtualization can be introduced to validate work in progress while reducing the dependencies on components and third-party technologies still under development.
Seretta Gamba, test manager at Steria Mummert ISS GmbH, discusses how she has been working on assembling test automation patterns—solutions that experienced practitioners have already found. She talks about her developmental background and why she is so interested in these patterns, which don't include any code or tools because those will eventually change, while the patterns will remain universal.
Rick Faulise, COO at PinkLion, talks about how to get involved in AI and how your company can highly benefit from it. AI has been around for a long time, but he says people need to change their mindsets about it in order to start implanting AI strategies. Rick explains how the bot software works and how it can be extremely useful in gathering information so that testers can look at the data for any program.
Melissa Tondi, QA professional services manager at Rainforest, talks about how fast the role of the tester is changing. She describes it as a pendulum, saying when she first started, everything was very user-focused, and now it is all about the technical teams. Melissa explains why it’s important to have a distribution of both technical and user-focused roles, then gives some strategies to find the right balance.
Kevin Pyles, director of QA at Domo Inc., discusses his keynote presentation, “From Zero to AI Hero.” He advises to start small with automation and Python, expanding as you discover how AI can add value to your job. He also talks about getting his hands dirty with AI and building internal tools without being afraid, saying that as a tester, you cannot be fearful of technology—you need to embrace the fun in it.
Most modern testing, especially in a DevOps model, uses a lot of telemetry to evaluate and monitor quality of experience for apps and services. In this interconnected world, there is power and risk in data. Ken Johnston will share his personal experiences dealing with US and European Union privacy regulations and the methods he and his team have implemented to mitigate the potential of significant penalties for the misuse of data. He will cover privacy-preserving techniques such as differential privacy and private enclave, what constitutes primary versus secondary uses of data, and how you should handle personally identifiable information (PII). You'll leave with a better understanding of how to keep data private and secured, as well as how to keep your team adhering to privacy best practices and regulations.
Serverless cloud applications are rapidly moving into the mainstream. In this model, teams focus on developing and deploying code on a known technology stack and runtime, with fixed interfaces for application, database, and network.