IDC Technology white paper spotlight on Capgemini Engineering’s Intelligent Testing: The frequency and volume of software releases is accelerating, and automation is on the rise. To keep pace, QA organizations must embrace intelligent test.
“IDC believes implementing intelligent testing as part of application lifecycle management will grow in importance over the next several years to help organizations get a leg up on their competition.” – Peter Marston, Research Director, Application Development, Testing and Management Services, IDC
Over the last few years, the number of companies transitioning to DevOps and Agile for software development has been on the rise and for a good reason: these methods speed application development and deliver products to market faster and more cost-effective than traditional development processes. At the same time, many DevOps adopters are investing in microservices and containers which are driving higher levels of software automation.
But as the number and frequency of software deployments accelerate and automation expands, traditional test organizations are struggling to keep pace. As a consequence, the test becomes a choke point for product introductions and updates, which leads to product launch delays that negatively impact business performance.
To meet the challenge, test organizations need to develop comprehensive testing capabilities and improve test coverage. In addition, they need to increase process speeds while assuring higher quality and superior user experience. This combination requires the adoption of a new, intelligent approach for a test that relies on artificial intelligence (AI) and machine learning (ML) that predict and fix issues before the software is deployed.
According to market researcher IDC, there are three strategic reasons why organizations should embrace intelligent testing:
- Drive higher levels of intelligence within the testing discipline. AI and ML tools lead to more relevant tests that drive higher automation through the testing lifecycle.
- Employ higher levels of automation builds. Automation can help sustain a competitive advantage by establishing a foundation for continuous testing. Including AI and ML in test automation accelerates test efficiency and improves test optimization.
- Elevate the image of quality assurance within the organization. Repositioning test as a value-add service focused on defect prevention instead of a cost center that focuses on defect detection can improve the image of the organization.
The Capgemini Engineering Intelligent Test offering delivers on all three counts by helping customers transform their test strategies with an agile, end-to-end and comprehensive test approach. At the heart of the Capgemini Engineering, Intelligent Test solution is an AI/ML-infused test automation platform that features many intelligent testing use cases across a range of test lifecycle activities that evolve with an organization’s test ecosystem.
The Capgemini Engineering solution enables testing teams to spend less time on defect remediation and more time on proactive activities such as defect prevention and optimization of the development process. Key areas where the solution benefits engineering teams include code-defect prediction, test-case prioritization, and root-cause analysis.
Organizations that have used the Capgemini Engineering Intelligent Test solution have achieved significant business results including. Here are three recent examples:
A leading technology provider leveraged the Capgemini Engineering Intelligent Testing solution to automatically prioritize and cluster 5,000 test cases for its product achieving a 35% reduction in field defects and an 85% reduction in regression testing effort and time.
A leading communications technology provider applied Capgemini Engineering’s ML-based code defect prediction use case and classified more than 20 parameters to identify buggy areas. The application of the use case delivered an instantaneous result at the time of code check-in; improved code quality, especially in historically problematic areas; and identified 60% more issues before testing—all in the first three months of use.
A leading communications services provider implemented Capgemini Engineering’s risk-based and orthogonal array testing to improve software quality by focusing on critical test areas and optimizing test cases to improve test coverage. The result was a 25% reduction in the number of test cases and a 35% reduction in delivery times.
It’s widely expected that implementing intelligent testing will see solid growth over the next few years, according to IDC. To get started, organizations should consider the following:
Define clear and measurable goals and objectives. Outline specifically what continuous testing will and will not bring to your application testing and delivery activities. Use these goals and objectives as the foundation for defining how your organization will be successful in implementing comprehensive QA across all facets of application lifecycle management.
Assess the existing state of automation within application testing. Many organizations have pockets of automation within their application testing but fail to link automation to many facets of their application lifecycle management. It’s important to get a macro-level understanding of where automation can be applied within key testing functions and adjacent application lifecycle functions to understand maturity levels, and spot opportunities for driving value in other areas of application lifecycle management.
Develop a governance and performance-monitoring model. Even with increased autonomous computing in application testing, organizations still need to develop a governance and oversight model to monitor performance and explore areas for further automation. Establishing a set of resources that will guide, direct, and manage the program, such as a steering or management committee, will ensure that the program has line-of-business representation and buy-in.
IDC Technology Spotlight on Capgemini Engineering Intelligent Testing
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