2021-22年世界质量报告（英）-72页_9mb

World Quality Report 2021-22 Summary

Core Content

The World Quality Report 2021-22 provides an in-depth analysis of current trends in quality assurance (QA) and testing, emphasizing the evolving role of QA in a post-pandemic world. It highlights the integration of QA into agile and DevOps practices, the increasing importance of intelligent test automation, and the growing adoption of artificial intelligence (AI) and machine learning (ML) in testing. The report also explores the impact of digital transformation in the physical world, known as Intelligent Industry, and the challenges and opportunities in test environment management (TEM) and test data management (TDM).

Main Trends and Views

1. QA Orchestration in Agile and DevOps

• Trend: QA and testing are becoming more integrated into the development process, moving from a separate discipline to a shared responsibility.
• Challenge: Agile teams still face a lack of professional test expertise, as the boundaries between development and testing blur.
• Outcome: Around two-thirds of respondents reported significant improvements in productivity, software quality, and cost of quality.
• Shift: The focus is shifting from "time to market" to "productivity, quality, and cost" as the new test triangle.
• Recommendation:
  • Invest in real-time insights.
  • Embrace multi-skilling and upskilling.
  • Adopt an engineering mindset.
  • Focus on customer experience and business objectives.

2. Test Automation

• Trend: Organizations continue to prioritize test automation, aiming for faster, more agile, and higher-quality testing.
• Challenge: Despite the benefits, some teams lack confidence in having the right automation strategy.
• Outcome: Test automation is seen as improving the testing process rather than just outcomes, but the demand for automation is increasing.
• Recommendation:
  • Standardize the use of test automation.
  • Expand end-to-end lifecycle test automation.
  • Invest in automation skills and tools.
  • Focus on automation that addresses the most challenging aspects of quality delivery.

3. Artificial Intelligence and Machine Learning

• Trend: AI and ML are becoming more prevalent in QA and testing, moving from theoretical concepts to practical implementation.
• Challenge: Organizations are still in the early stages of AI integration, with a need for better data integrity and process maturity.
• Outcome: Almost half of respondents are willing to act on the intelligence provided by their AI and ML platforms.
• Recommendation:
  • Drive the use of AI, but don’t be driven by it.
  • Focus AI efforts on areas that matter most.
  • Start the journey now.
  • Use AI as part of overall QA management.

4. Test Environment Management (TEM) and Test Data Management (TDM)

• Trend: There is a gradual shift of test environments to the cloud, though progress remains slow.
• Challenge: Ensuring compatibility between cloud-based and legacy applications remains a concern.
• Outcome: Organizations are becoming more capable of spinning up test data and environments on demand.
• Recommendation:
  • Emphasize real-time environment and data availability.
  • Don’t let future goals obscure current needs.
  • Factor in data analytics for TDM.

5. Intelligent Industry

• Trend: Organizations are increasingly interested in digitizing physical processes, leveraging embedded software, 5G, edge computing, AI, and IoT.
• Challenge: The transition is still in early stages, with a need for investment in skills and tools.
• Outcome: Digital transformation is seen as a path to efficiency, quality, and improved customer experience.
• Recommendation:
  • Invest in innovation labs and MVP development.
  • Invest in your QE teams for Intelligent Industry growth.
  • Increase focus on security and resilience.
  • Secure management buy-in by demonstrating feasibility.

Key Insights

• New Realism: QA teams are more realistic about their capabilities and the future of testing, moving from a focus on "can we do it?" to "we know we can."
• Remote Testing: Remote access to test systems and environments has become a top priority, reflecting the growing reliance on SaaS and cloud solutions.
• Shift Right: Testing is moving further to the right in the software development lifecycle (SDLC), emphasizing early and continuous testing for faster delivery.
• Skills Development: There is a strong emphasis on developing and upskilling QA and testing teams to keep pace with evolving technologies and methodologies.
• Customer-Centric Focus: The shift from IT quality to production quality highlights the growing importance of customer experience and business outcomes.

Sector Analysis Highlights

• Automotive: Focus on digital transformation and integration of AI and IoT in vehicle systems.
• Consumer Products, Retail, and Distribution: Emphasis on agility and real-time customer engagement.
• Energy, Utilities, and Chemicals: Increased use of AI and automation for operational efficiency.
• Financial Services: Greater investment in security and compliance, with a focus on AI and ML for fraud detection and risk management.
• Healthcare and Life Sciences: Rapid adoption of digital tools for patient care and regulatory compliance.
• High-Tech: Push for innovation and speed in product delivery.
• Government and Public Sector: Focus on security, resilience, and efficient public service delivery.
• Telecom, Media, and Entertainment: Emphasis on customer experience and real-time data processing.

Key Recommendations

• QA Orchestration: Invest in insights, embrace multi-skilling, adopt an engineering mindset, and focus on what matters.
• Intelligent Test Automation: Standardize automation, use it end-to-end, and invest in targeted use cases.
• AI and ML: Use AI as a support tool, not a replacement, and start implementing it now.
• TEM and TDM: Prioritize real-time availability, maintain compatibility with legacy systems, and incorporate data analytics.
• Intelligent Industry: Invest in innovation and your teams, and ensure security and management support.

Conclusion

The 2021-22 edition of the World Quality Report reflects a more pragmatic and realistic approach to QA and testing, driven by the challenges of the pandemic and the ongoing digital transformation. The report underscores the importance of agility, automation, and AI in achieving higher software quality and customer satisfaction. It also emphasizes the need for continuous learning and investment in both people and tools to support the evolving landscape of quality engineering.