Product teams often struggle to balance between release frequency and defect leakage. The trade-off is difficult to manage for quality engineering services teams involved in testing products with many possible configurations, products with third-party dependency, products that need specific equipment and lab set-up, and hardware products. While almost every organization wants to achieve 100% test automation, not all product teams within the enterprise can achieve that due to inherent nature of the products. To improve the quality of the testing and optimize beyond test automation, it is useful to use historical data to bring in the power of Artificial Intelligence and Machine Learning (AI/ML).
Mphasis Cognitive Quality Engineering (CQE) is an AI/ML platform that helps enterprises achieve improved quality, accelerated time-to-market, and cost optimization by prescribing decisive actions throughout testing life cycle. CQE can be utilized by product engineering teams and enterprises with large pool of applications with high interdependencies.
With the defect-prediction based test prioritization CQE models, engineering teams are aware about likelihood of failure of test cases at the start of the sprint. This helps to fix any bugs faster or avoiding them altogether without over-burning the team and abandoning story points. Mphasis CQE platform integrates easily with industry wide testing and software management tools such as Jira, ALM, and Test Rail.
Embedding quality into the development lifecycle is the ideal way to address the increasing complexity of software products and ensure foolproof products. Mphasis’ AI-driven quality engineering services leverages its Cognitive Quality Engineering (CQE) platform to enhance quality, accelerate time-to-market, and lower testing costs.
To ensure sustained quality across the software development lifecycle, the industry is making a shift from traditional quality assurance (QA), which is focused on testing, to quality engineering (QE), which emphasizes intelligent, continuous quality throughout development. Mphasis leverages AI/ML, intelligent automation, test optimization, and continuous testing practices to enable the QA to QE transformation.
Artificial Intelligence analyzes historical testing and defect data to predict failures, prioritize test cases, optimize test coverage, and reduce defect leakage. Mphasis CQE platform uses AI/ML to prescribe decisive actions throughout the testing lifecycle, improving quality, accelerating time-to-market, and achieving cost optimization.
QA detects defects through testing at regular intervals or at the end of the software development lifecycle, while Quality Engineering proactively embeds quality throughout development using automation, AI, analytics, and continuous testing. Mphasis helps enterprises evolve from reactive QA processes to predictive, AI-powered QE practices with its Cognitive Quality Engineering (CQE) platform.
AI-powered testing helps organizations reduce effort of test planning and execution, improve test coverage, prioritize high-risk test cases, and minimize defect leakage. With CQE, Mphasis has reduced regression testing effort by 20% and improved defect detection by 30% for a client.
DevOps environments are fast paced, and continuous quality engineering is essential for rapid releases and reliable software delivery. Mphasis supports DevOps with AI-driven test optimization, continuous testing, intelligent automation, and seamless integration with testing and software management tools such as Jira, ALM, and TestRail.
