agile

Even today, communication breakdowns are a primary cause of software project failures. Marcia Buzzella’s research shows that increasing the success rate of projects across agile, DevOps, and waterfall methods requires a balance of social and technical capabilities. Social interactions enable us to assess situations and course correct in ways machines cannot. By strengthening your individual social capabilities (i.e., improving communication techniques and building supportive relationships), you can help transform how testing activities are perceived and help stakeholders understand how testing objectives align with overall project goals. Marcia offers guidance on assessing your current communication skills—what you do well and what needs to improve—and tactics for enhancing your skills and the skills of your team.

Substantial confusion exists about the roles and responsibilities of test management when using an agile software development process. Agile seeks to streamline project management and leadership under the role of a ScrumMaster. But what does this mean for test managers? How do they stay involved in the process? What role do they fill? Is it possible that test managers are no longer needed? Join Jeffery Payne for a collaborative dialog to discuss the pros and cons of a variety of test management models he has seen used by companies who have adopted agile. Learn how to best position yourself within the agile model to add value and continue to support your test teams. Take home practical knowledge on how test management is done successfully within agile projects.

Selenium has been the cure for free and low-cost browser testing for years, and—in the world of agile, mobile, DevOps, and browserless interfaces—it is showing its age. Comparing Selenium to what’s coming, Jason Arbon says that machine learning and data analytics will become the new standard for test automation. With Selenium, test engineers suffer from the pains of broken element identification; broken, buggy, and partially implemented mobiletest capabilities; exploding costs of building abstraction layers on their apps; brittle test code when the application under test changes; and absence of any context or test logic in the framework. Jason unwraps the work that several startups and projects initiated in the past twenty-four months are actively doing to fix these issues with new general-purpose, test frameworks that combine machine learning and data analytics.

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