Digital.ai Quality Improvement
AI-powered analytics for more effective and efficient software quality management.
Help your development and quality teams address:
Ineffective software testing efforts
Limited understanding of quality risk factors
Predicting and preventing release quality issues
Prioritizing quality improvements projects that add value
Improve quality effectiveness
Identify test coverage gaps between production & QA that diminish the delivered value. Increase test execution coverage for applications/value stream/teams with consistently lower defect-removal efficiency
Prevent defect leakage
Identify and take action on key risk factors that are driving up the number of predicted production defects. Drill down and analyze risk factors in order to determine systemic root cause in order to enable targeted action and learning.
Remove pipeline blockers
Identify value streams with the top opportunities to accelerate flow by relieving blocked pipelines that are affected by quality issues. Analyze the impact of blocked pipeline on overall lead time and take targeted action.
Benefits of Quality Improvement
Reduce rework
• Prevent defect leakage
• Increase test effectiveness
Improve quality
• Improve pipeline quality
• Increase code coverage
• Improve the digital customer experience
Reduce release risk
• Predict release quality risks
• Avoid release rollbacks
Enterprise-size problems require enterprise-grade solutions
AI-powered analytics improve software quality by preventing defect leakage, reducing release quality risk, and increasing test effectiveness. The Digital.ai Platform brings together software lifecycle orchestration targeted AI and software lifecycle orchestration solutions that improve software delivery efficiency, reliability, and business impact.
How continuous testing can help make the right tradeoffs between protection and customer experience