STAREAST 2022 - Test Strategy, Planning, Metrics

To be most effective, leaders—including development and testing managers, ScrumMasters, product owners, and IT managers—need metrics to help direct their efforts and make informed recommendations about the software’s release readiness and associated risks. Because one important evaluation activity is to “measure” the quality of the software, the progress and results of both development and testing must be measured. Collecting, analyzing, and using metrics are complicated because developers and testers often are concerned that the metrics will be used against them. Join Mike Sowers as he...

Anyone who has ever attempted to estimate software testing effort realizes just how difficult the task can be. The number of factors that can affect the estimate is virtually unlimited. The keys to good estimates are understanding the primary variables, comparing them to known standards, and normalizing the estimates based on their differences. This is easy to say but difficult to accomplish because estimates are frequently required even when we know very little about the project—and what we do know is constantly changing. Throw in a healthy dose of politics and a bit of wishful thinking,...

Repeated software incidents led to system downtime, lost revenue, and a decrease in customer service. Team debriefs revealed that many of these incidents were preventable failures that occurred because (a) a lack of psychological safety on the team, (b) testing was considered the sole responsibility of testers, and (c) the team did not anticipate and prepared for what could go wrong. We first had to foster a psychologically safe environment where team members felt free to discuss obstacles to testing, such as a lack of environmental parity. A safe environment enabled the team to identify...

Synergies among AI and software testing include both leveraging AI for test automation, and devising methods for testing AI systems. Researchers, software vendors, and practitioners are already using AI to develop a new wave of test automation tools. These tools automatically explore, model, reason, learn, and test software applications. However, as with most AI and ML-based technologies, these tools raise concerns around whether or not we really trust and depend on them. ML-based systems tend to be highly dynamic and adaptable at runtime, thereby requiring extensive testing both offline,...

DevOps aims to reduce the distance between development (software developers and quality assurance specialists) and operation (experts responsible for rolling out software to production and managing the infrastructure). The goals of DevOps are to increase the speed of software delivery process, to resolve critical issues quickly, and to enable organization to serve their customers better. These goals indicate that in DevOps, quality assurance, in general, and testing, in particular, should be looked at differently as compared to traditional testing and even agile testing. Specifically, the...