Automated testing is vital for every software development organization's quality assurance activities. Dorothy Graham and Seretta Gamba demonstrate how to classify issues that occur during test automation. The authors maintain that certain test results have root causes that can be categorized as patterns that require specific mitigation strategies.
Taking lessons from the lean business model, Matt Heusser explains how a tester can present different values and properly set expectations with the team using the lean test canvas. His approach starts with defining who the customer is and ends with key qualitative measures that will be used to ensure success.
Speaking from his experience with test-centered design projects, Jon Hagar explores some testing pitfalls that could have been avoided if the right test strategy had been chosen. You won't find a better, easier-to-understand explanation of a practical test strategy.
Improving product quality is often a very difficult task for even the best software development organizations. Rajini says the additional benefits of automation include benchmarking, code scanning analysis, end-to-end test cases, and compatibility validation.
In the first installment of this article, Dr. James Whittaker discussed turning testing on its head—to revitalize and improve the value of late-stage testing. James also discussed ideas behind empowering your dogfooders, testers, and the crowd to significantly and efficiently improve software quality. In part two, Jason Arbon discusses the research and engineering experimentation behind realizing these ideas into new tools and processes.
The testing craft is sometimes fascinated with high-tech, expensive tools that are intended to help managers keep up to date on what's going on. Yet, sometimes heavyweight tools aren't necessary. Michael Bolton describes how Paul Holland, a senior test manager, uses a decidedly low-tech approach to track and illustrate the testing story.
Modern applications operate in highly integrated environments, and critical systems rely on massive amounts of data that likely contain sensitive information. Discover useful strategies for preparing your baseline, handling interfaces, designing input data, and planning for output results.