STAREAST 2018 - Big Data, Analytics, AI/Machine Learning for Testing

Software continues to revolutionize the world—impacting nearly every aspect of our work, family, and personal life. Artificial intelligence (AI) and machine learning (ML) are playing key roles in this revolution through improvements in search results, recommendations, forecasts, and other predictions. AI and ML technologies are being used in platforms for digital assistants, home entertainment and automation, medical diagnosis, customer support, and autonomous vehicles. Testing practitioners are recognizing the potential for advances in AI and ML to be leveraged for automated testing—an...

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,...

After its highly hyped introduction decades ago and followed by a long, quiet “winter,” artificial intelligence (AI) has slowly crept back into our consciousness. While our Siri and Alexa assistants entertain us, machine learning (ML) has brought new conveniences into our lives with solutions including Nest and Netflix. Today, AI brings us to the tantalizing brink of the autonomous vehicle. The sea change of this 4th Industrial Revolution has begun to disrupt industry after industry. The emerging capabilities of these fascinating machines demand our attention as AI starts to be applied in...

Advances in artificial intelligence (AI) and machine learning (ML) are leading to a new generation of software, which is becoming self-adaptive, autonomous, and smart. Academic researchers and industry practitioners are investigating how these new AI and ML technologies can be leveraged to improve software testing and testing services. A handful of testing-as-a-service (TaaS) vendors already offer services that use AI bots to perform some functional and performance testing. How well do they live up to their claims? Can they be used as an effective substitute or supplement for human testers...

The past few years have brought a sea change in the way applications are architected, developed, and consumed—increasing both the complexity of testing and the risk of software failures. Given the trends that impact both architectures (cloud, microservices, and APIs) and processes (DevOps, agile, and continuous delivery), how can software testing keep pace with modern application delivery? Enter continuous testing. Wayne Ariola explores the three main differences between continuous testing and test automation. He explores why and where traditional test automation falls short in today’s...

Managing the quality and performance of complex systems requires more than simply executing test cases and running load tests. You need to perform careful analysis of test results and production metrics. The sheer amount of data generated in production and testing makes analysis a huge challenge that is often left wanting. With the magic of machine learning (ML) and the application of data science techniques, you have the opportunity to derive valuable and actionable information from big data. Gopal Brugalette shares the basic concepts behind ML, covering clustering, classification...

As more applications are hosted on servers, they produce immense quantities of logging data. Quality engineers should verify that apps are producing log data that is existent, correct, consumable, and complete. Otherwise, apps in production are not easily monitored, have issues that are difficult to detect, and cannot be corrected quickly. Tom Chavez presents the four steps that quality engineers should include in every test plan for apps that produce log output or other machine data. First, test that the data is being created. Second, ensure that the entries are correctly formatted and...

API-based applications have become ubiquitous in the past few years. Yet many of us still really don't know what an API is from the inside out. Moreover, many of us don't know much about how to comprehensively test them! Join Paul Merrill to learn what everyone is doing and talking about related to APIs: microservices, REST, endpoints, requests, request methods, and responses. Paul demonstrates four free tools you can use to directly interact with a simple API: curl, the browser, Postman, and Java (with Rest Assured). Learn techniques for testing APIs at the service level and how to test...