This year’s Conference of the Association for Software Testing CAST is literally around the corner and we cannot wait. Bags packed, conference agendas at the ready, we are good to go. CAST holds a special place in our calendars… and in our hearts, because it’s the event where we really dig deep into the practice of software testing. As the Association of Software Testers succinctly put it, CAST is where the industry meets to discuss “What the heck testers really do!” The agenda for this year is packed with sessions reflecting the most important emerging practices and technologies that are disrupting testing. In addition to taking a practical look at what testers do, we would expand that description of the event to include “What do testers really do…and how can we do it better?”
A topic Infostretch will focus on during this year’s conference asks just that question in relation to using machine learning for predictive quality analytics. Specifically, we will look at defect detection using JIRA, a tool we all use every day.
These days it’s more important than ever to speed up release cycles while maintaining quality. JIRA dashboards are the ubiquitous tool to keep track of projects and provide useful metrics for effective project management. However, this data also contains very useful insights which can lead to better allocation of testing and development resources for future releases of a product.
In our speaking session “Mining JIRA Data for Defect Detection” Infostretch’s Sivakumar Anna, will present an approach to using machine learning to glean predictive insights from JIRA defect data. The aim of the session is to examine how JIRA can be used as an effective tool for predictive analytics, as well as improving its use for project management. Ultimately, we want to help testers use JIRA more effectively to speed up test and release cycles. At a broader, organizational level, it will allow for better allocation of testing and development resources, leading to faster testing cycles overall. Siva will guide you through a scenario of how a dashboard tool can be integrated with JIRA, and using real-world examples, he will outline a specific scenario of how JIRA data can be moved from raw defect data to predictive analytics and follow it with actionable recommendations to help advance your QA maturity.
If you’d like to find out more about how machine learning can be used to glean predictive quality analytics from JIRA data, don’t miss Infostretch’s speaking session on Thursday, August 17th at 3:05 pm.
Finally… while you’re at CAST, don’t forget to stop by our booth and say hello. You can enter to win a Fitbit Blaze! If you can’t get to us in person at CAST, you can still discover how to leverage predictive analytics to improve your QA capabilities. Why not take our QE Self-Assessment Quiz to find out how your organization measures up against industry best practices, and learn what steps you can take to promote QE.
Yesterday, I went on a short drive to check with my InfoNeers on what testing means to them. And as you’d see below, I found some very fascinating answers from these bug...