Earlier this year, Deloitte 2023 Quality Engineering Trends ReportThe content is necessary to “adapt to common quantitative easing challenges in unfamiliar times, such as spending, innovation, operating in a post-pandemic society, and capitalizing on new growth opportunities,” the organization argues. Covers important elements.

digital engineering We spoke with Rohit Pereira, Principal and Quality Engineering Practice Leader, Deloitte Consulting LLP, about the findings and trends in QE.

DE: How has the role of QE in the product development/design/manufacturing process changed, especially in the years and beyond of the pandemic?

Rohit Pereira: Quality will continue to play a pivotal role in product delivery, but the pandemic has accelerated the growth of digital, cloud and modernization-related transformations. This required a change in operating model and the need for rapid deployment to market. For example, his QE team at our company helped clients move quickly to a virtual engagement model that delivers quality delivery by leveraging:

1. Automate AI-related testing at scale across the test delivery lifecycle, from test management to test design to test execution.
2. Shift left in delivery by running code to identify design and performance bottlenecks early in the delivery lifecycle.
3. Shift Right, powered by chaos engineering, proactively identifies production issues before they occur.

Quality engineering is a value driver that enables organizations to reduce delivery costs and identify new innovations that can be applied to technology delivery and business operations.

How would you explain the decline in hardware sales due to QE? How is the need for hardware reduced in scenarios like this? What are the alternatives to the need to use these tools? ?

Rohit Pereira: The move to cloud-native/cloud-enabled solutions almost eliminates the need to set up dedicated test environments, helping reduce hardware expenditures. Hardware and tools are no longer a potential bottleneck to testing as they are with cloud deployments. Test environments can be created on demand to support automation within the test life cycle and simulation of production-like scenarios.

How extensively are companies actually using metaverse/virtual platforms for quality engineering?

Rohit Pereira: As the digital landscape changes, immersive customer experiences across devices and platforms are becoming increasingly important to drive product personalization. Although automation tools and technologies for testing metaverse/virtual platforms are currently primitive and expensive, we believe that the industry is increasingly adopting AI and generative AI-based testing solutions. These AI-based predictive modeling solutions are not only leveraged to interact with digital avatars on metaverse/virtual platforms, but also capture user experience at each step of the customer journey to identify areas for improvement and It is also used to build product and service offerings focused on needs. Some elements of the Metaverse are all around us, but we still have work to do to reach its full potential.

What needs to happen within the industry to make metaverse/virtual testing as acceptable as physical testing (or at least as an acceptable alternative for some phases of testing)?

Rohit Pereira: The metaverse can engage diverse users across the global ecosystem and drive cloud testing at scale. However, for this to really happen, more users will need to join the metaverse and move it into the mainstream. Widespread adoption of the metaverse is underway, but a central testing challenge is leveraging the right tools and skills to both test the metaverse and leverage it in testing.

Deloitte believes that as the Metaverse Framework matures and its use cases evolve, it will see widespread user adoption across industries given the consumer engagement opportunities it presents to businesses. This improves the overall reach and early feedback of new products and technologies being introduced globally, and makes virtual testing more acceptable.

How is simulation being used to improve quality engineering, and how is the role of simulation technology changing?

Rohit Pereira: AI-powered, self-evolving testing solutions enable organizations to simulate end-to-end customer journeys, gain valuable insight into customer behavior using predictive test modeling, and leverage it to better meet customer needs. We are now able to customize our products and services based on AI-based predictive testing models are also used to simulate production-like scenarios, generate synthetic test data that mimics production data at scale, and identify areas of impact for adopting risk-based testing models. I’m here.

Can you explain chaos engineering and how it helps from a quality perspective? Is there a role for quantitative easing in physical commodities?

Rohit Pereira: Simply put, the goal of chaos engineering is to proactively identify pressure points in the application under test and proactively address them to avoid breaking production when it matters. This is especially important for physical products, which are exposed to many forms of stress through contact, environmental factors, and other external factors. Chaos engineering takes a scientific approach to intentionally engineering vulnerabilities and impediments to identify, analyze, and address potential breakpoints. The goal is to proactively “break the system” so that it doesn’t break in production.

The paper also discussed the challenges of synthetic and test data management. Do users (or potential users) trust this kind of data for testing/training?

Rohit Pereira: Synthetic data is being leveraged at scale by many organizations to mitigate the risk of PHI/PII breaches in the underlying environment and leverage datasets that more comprehensively mimic operational datasets. This reduces data conflicts between teams operating in the same non-production environment and enables more efficient and effective testing across teams at scale. Additionally, this removes the need for test data anonymization while generating large amounts of test data for large-scale transaction validation.

Deloitte develops mechanisms through AI and ML-based algorithms that can analyze production data and use that information to generate meaningful synthetic test data that mimics production outcomes without impacting PII/PHI. Developed to increase user trust and confidence.

What impact might the evolution of quality engineering have on other stages of product design? Can changes be made?

Rohit Pereira: Quality engineering is increasingly seen as an integrator (environment, release, DevOps – CI/CD, etc.) rather than a separate phase following build/development. This is especially true for large-scale transformation programs where quality engineering is integrated, enabling business process innovation and increasing confidence in delivery results.