Capgemini2018年世界质量报告WQR英文2018972页4mb

World Quality Report Summary

Core Content

The World Quality Report (WQR) by Capgemini, Sogeti, and Micro Focus is a comprehensive analysis of current trends and challenges in quality assurance (QA) and testing. Based on a survey of 1,700 executives across 10 sectors and 32 countries, the report highlights the evolving role of QA from a mere defect-finding function to a critical enabler of customer satisfaction and business outcomes.

Main Trends and Insights

1. Shift in QA Objectives

• End-user satisfaction is now the top objective of QA and testing strategies, with a weighted average score of 5.85.
• Enhancing customer experience and enhancing security are the second and third most important objectives in IT strategy.
• This shift reflects the increasing customer-centricity of IT, driven by digital transformation, agile, and DevOps adoption.

2. Adoption of Agile and DevOps

• 99% of organizations use DevOps in at least some of their projects.
• The "Quality at Speed" paradigm is central to these methodologies, enabling faster innovation and delivery of high-quality software.
• However, some organizations focus on speed at the cost of quality, leading to the use of hybrid frameworks combining Agile with Waterfall to meet regulatory and cultural needs.

3. Test Automation

• The level of test automation is still low (14–18%), and this is a major bottleneck for QA maturity.
• 61% of respondents face difficulties automating QA processes due to frequent application changes.
• 48% struggle with predictable and reusable test data and environments.
• 46% lack skilled test automation resources.
• The report recommends a phased approach to automation: first optimize testing, then implement basic automation, and finally adopt intelligent, self-adaptive automation.

4. Test Data and Environments Management

• 31% of testing still occurs in permanent environments.
• 58% of respondents still rely on manually generated test data.
• 66% use spreadsheets for test data generation.
• 62% use copies of production data for testing.
• The lack of appropriate test environments and data remains the top challenge for agile testing and the second biggest bottleneck for automation.

5. Role of Artificial Intelligence (AI)

• 57% of respondents have AI projects in QA and testing already in place or planned.
• 45% use AI for intelligent automation.
• 36% use AI for predictive analytics.
• 35% use AI for descriptive analytics.
• AI is expected to be a major disruptive force in QA and testing over the next 2–3 years, enabling self-generating, self-running, and self-adapting testing activities.
• Challenges include identifying appropriate AI applications and acquiring new skills such as AI QA strategists, data scientists, and AI test experts.

6. Emerging Technologies

• IoT adoption has increased from 83% to 97%.
• Blockchain is being increasingly adopted, with 60% of respondents already using it or planning to.
• The convergence of AI, ML, and analytics is expected to drive self-learning and self-aware systems, significantly enhancing QA and testing efficiency.

7. Cost and Efficiency

• The average spend on QA and testing is 26%, stable from last year.
• Waterfall-based testing and outsourcing have reduced costs for core IT and legacy systems.
• However, digital transformation, cloud migration, and agile/DevOps have led to increased spending on infrastructure, tools, and reorganization.
• Future investments in test environment virtualization, test data management, and analytics may increase this proportion to 30% over the next 2–3 years.

8. Skills Requirements

• QA and testing roles are becoming more diverse and specialized.
• Functional automation and domain testing skills are now critical.
• SDET (Software Developer Engineer in Test) profiles are in high demand.
• AI and analytics expertise is increasingly required, with 36% needing a better understanding of AI's impact on business processes.
• Test environment and data management skills are also in high demand, with 29% needing more knowledge in TDD and BDD.

Key Recommendations

1. Increase Automation Levels

• Organizations should adopt a phased approach to automation:
  • First, optimize testing.
  • Then implement basic automation.
  • Finally, move towards intelligent and self-adaptive automation.

2. Implement Non-Siloed Approaches

• Test data and environment provisioning should be handled through a centralized, lifecycle-driven approach.
• This will lead to better reusability of infrastructure, greater efficiency, and reduced delays in testing processes.

3. Develop Quality Engineering Skills

• Prioritize agile test specialists with functional automation and domain testing skills.
• Recruit and reskill SDETs with advanced automation, white-box testing, development, and orchestration platform skills.
• Ensure AI and analytics expertise is integrated into QA teams to support self-learning, self-aware systems.

Conclusion

The World Quality Report 2018-19 underscores the transformation of QA and testing into a strategic function that directly impacts business outcomes and customer satisfaction. While automation, AI, and DevOps are driving this change, challenges in test data and environment management and skills development remain significant. Organizations must address these challenges through centralized strategies, phased automation, and cross-functional skill development to achieve QA and testing maturity and support their digital transformation goals.