Artificial intelligence is leading a transformation in how large and unstructured data is rebuilt into meaningful insights and enabling humans to make better decisions. As we continue to experience the increasing domination of AI and its wide-ranging applications across industries, it would be fascinating to see its impact on the software industry, a key force in driving innovation.
Software testing is a vital step in the process of software development and managing the quality of the final product. Yet most Software Test Management solutions are predominently used as reactive tools to find and fix issues arising during the development process. But as software development becomes more complex and requires increasing collaboration, arming QA and product teams with an intelligent Software Test Management solution has become an indispensable business requirement.
With powerful artificial intelligence (AI) techniques, including machine learning and statistical methods, it is now possible to take a more proactive and lean approach to software testing and quality management. The software technology allows for collecting and mining large data sets, analyzing past patterns, understanding user behavior and determining defect trends, thus enabling QA and Test Managers to ask deeper and more intelligent questions and make better decisions along the way.
Zephyr's development and QA teams have leveraged these AI techniques in our own software development lifecycle to experience tremendous improvement in our ability to track down hot spots early on in the releases and helped us proactively adjust our planning as we approached the release date. The machine learning enabled decisions in test case execution have helped us reduce our QA time and increase our productivity immensely.
Banking on a solid proof of the impact of AI and machine learning on software quality and testing, we now want to empower our customers with path breaking predictive engines!
The NEW Zephyr platform
The latest Zephyr platform is built to enable companies gain competitive advantage by addressing their most critical business need- delivering higher quality software, faster! Teams can now take a proactive approach to software test management by developing prediction models that utilize extensive data mining, machine learning and iterative learning techniques that bring out the most critical testing questions to the forefront. With deeper insights into the software development cycle, teams are better equipped to implement software development testing best practices to maximize the process efficiency and reduce development costs.
Examine the Process Stability
This Zephyr model provides just the right overview of defect management for mapping defect trends across multiple releases. This helps to convey the stability of the product and establishes the optimum resource requirement that teams need to meet their delivery schedules.
With this model the QA and product teams would be able to answer discerning questions, such as:
- What is our current defect count? What’s our defect closure rate?
- How critical are the open defects in the software we have developed?
- What does our defect complexity look like?
Get Process Improvement Predictions
This model provides data on the number of test cases that have been automated for every project release. As the test automation coverage gets higher, teams can identify various stages at which efficiency can be improved and thus reduce the development costs. Teams can save their valuable time and gain maximum results out of each automation attempted by intelligently choosing the areas to automate based on the defect reports across projects.
This model helps teams ask vital questions, such as:
- Have we identified the right test cases to automate in order to optimize our release cycle?
- Have our QA teams prioritized the most important test cases at the start of the cycle?
- Have we addressed the under-performing test cases that were a problem over the past releases?
Evaluate the Process Efficiency
AI based models help establish patterns and derive relationships between past and current test cases to highlight all related executions with a probablity of failure. This allows teams to use an optimized and thinner test list and prevents users from running duplicate test cases. With this model teams can save their valuable time and reprioritize the test cases to make their test execution strategy more efficient and productive.
Learn more about the NEW Zephyr platform to elevate your software quality to the next level!