Testing Agile

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Capgemini’s World Quality Report 2024-25, developed in collaboration with Sogeti and OpenText, offers comprehensive insights into the evolving landscape of Quality Engineering (QE) and testing. This 16th edition emphasizes the integration of Generative AI (Gen AI) in QE processes, highlighting its transformative impact on test automation and overall quality practices.

Key Highlights:

• Adoption of Generative AI: The report reveals that 68% of organizations are either actively utilizing Gen AI (34%) or have developed roadmaps following successful pilot implementations (34%). Test automation is the leading area where Gen AI is making an impact, with 72% of respondents reporting faster automation processes as a result of Gen AI integration.
• Emphasis on Data Quality: Data quality has been rated as critically important or of very high priority by many organizations, underscoring its significance in effective QE practices.
• Sustainability in QE: The report indicates that 98% of organizations acknowledge the critical importance of sustainability in their operations. However, only 25% are measuring the environmental impact of their overall IT development, while 44% are tracking the impact of testing activities. Furthermore, only 34% of respondents are implementing efficient Quality Engineering practices to drive sustainability.
• Challenges in Test Automation: Despite advancements, organizations face challenges in automating their testing environments. A lack of comprehensive test automation strategies and reliance on legacy systems were identified by 57% and 64% of respondents, respectively, as key barriers to advancing automation efforts.

The report also provides sector-specific analyses, offering tailored insights for industries such as automotive, manufacturing, consumer products, healthcare, public sector, financial services, telecommunications, and energy. These analyses help organizations understand and address unique challenges within their respective sectors.

For a detailed exploration of these findings and more, you can access the full report on Capgemini’s official website.

Let’s dive into it more…

The Promise of Generative AI in Testing: Hype or Reality?

Report’s Perspective

According to the report, 68% of organizations are either actively using Generative AI (Gen AI) or have roadmaps to implement it, primarily in test automation. The report attributes significant gains to Gen AI, with 72% of respondents reporting accelerated automation as a result of AI integration. The narrative suggests that Gen AI has moved from a novelty to a near-mainstream tool.

Reality Check

While Gen AI indeed holds transformative potential, the practical application of AI-driven test automation remains limited. In many QA teams, challenges like data complexity, environmental stability, and the variability of test cases make automation adoption challenging, especially in highly regulated industries.

Moreover, the shift to Gen AI requires substantial training and adjustments, which are hurdles for teams still entrenched in traditional practices. While large enterprises may have the resources to explore AI-powered automation, smaller organizations face a more challenging journey. Additionally, QA professionals express concerns about relying on AI without consistent validation, as misconfigured AI could lead to overlooked defects.

Emphasis on Data Quality: A Repeated Aspiration

Report’s Perspective

Data quality takes center stage in the report, with many respondents identifying it as crucial to effective Quality Engineering. The report pushes AI as a primary tool for managing and optimizing test data, while also recognizing the persistent issues of data bias and lack of transparency.

Reality Check

Data quality is indeed a crucial factor in QA, especially as AI-driven testing gains traction. However, despite years of focus on data quality, many organizations struggle to manage and maintain the consistency of test data. Effective data management remains a perennial challenge due to data privacy regulations, infrastructure limitations, and the complexity of maintaining high-quality datasets across environments.

While AI offers promising solutions for test data management, the real-world effectiveness of these tools often falls short of the vision portrayed in the report. In practice, managing test data frequently involves manually intensive processes, and without significant investment, the quality remains a common bottleneck in QA pipelines.

Sustainability in Quality Engineering: Real Commitment or Marketing Hype?

Report’s Perspective

Capgemini’s report states that 98% of organizations acknowledge sustainability as a critical priority, with 44% tracking the environmental impact of testing activities and 34% implementing sustainable Quality Engineering practices.

Reality Check

While sustainability is increasingly on companies’ agendas, the reality is that many organizations lack a concrete framework for integrating sustainable practices into Quality Engineering. Even in enterprises committed to environmental goals, measuring the specific impact of testing activities on sustainability remains nebulous, as tracking the environmental footprint of testing workflows is complex and costly.

In reality, sustainability initiatives are often focused on areas with more visible impact, such as data center efficiency or supply chain practices. QA activities frequently fall to the wayside due to limited visibility into their environmental impact and the perception that their contribution to sustainability is minimal.

Test Automation: Still an Uphill Battle for Many

Report’s Perspective

The report notes that test automation remains a challenge, with 57% of respondents citing a lack of comprehensive strategies and 64% pointing to legacy systems as barriers. Despite these hurdles, test automation is regarded as essential, with organizations exploring AI-driven solutions to bridge gaps.

Reality Check

Test automation has long been a focal point in QA, yet real-world progress remains slow. Organizations often face an uphill battle with automating testing, particularly for complex or legacy systems. The need for specialized skills, coupled with infrastructure constraints, continues to hinder full-scale automation adoption.

Furthermore, while AI is positioned as a solution to automation challenges, the reality is that it often introduces new complexities. The costs and efforts required to implement and maintain AI-driven automation tools are substantial, making it feasible primarily for larger organizations. Consequently, many QA teams still rely on manual testing or low-code/no-code solutions as practical alternatives.

Sector-Specific Trends: Tailored Recommendations, but Generic Challenges

Report’s Perspective

Capgemini provides sector-specific insights for industries such as automotive, healthcare, financial services, and public sector, offering tailored recommendations for testing and quality challenges in each field.

Reality Check

While sector-specific recommendations are valuable, many of the issues discussed are universal across industries. For example, the need for robust automation and data quality practices applies to all sectors, and the same bottlenecks—legacy systems, skill gaps, and limited budgets—are common across industries. QA professionals in various sectors often encounter the same hurdles, and broad recommendations, while helpful, may not address the unique challenges of specific fields deeply enough to guide meaningful change.

Conclusion: Bridging the Gap Between Vision and Reality

Capgemini’s World Quality Report 2024-2025 paints an optimistic picture of the future of Quality Engineering, with AI, data quality, and sustainability at the forefront. However, the report may overestimate the current readiness of organizations to fully embrace these technologies. While AI and sustainable practices in QE are undoubtedly valuable, real-world QA teams face complex and deeply entrenched challenges that require more than a one-size-fits-all approach.

To succeed with quality assurance in the future, companies need to aim for realistic, practical improvements instead of drastic changes. The best approach is to introduce AI and sustainable practices step-by-step into current QA processes, which will be easier to manage and more effective than trying to transform everything all at once.

My Perspective: As we adapt to AI-driven changes in quality engineering, it’s crucial to strike a balance between technical coding skills and foundational QA expertise. Quality cannot be fully automated; it requires a human touch to address complex user needs and edge cases that AI might miss. A truly robust approach will value both the contributions of skilled testers and the efficiencies AI brings.

At the end of the day, a human user—not a machine—will be interacting with your software. While AI and automation can handle repetitive testing tasks efficiently, they lack the nuanced understanding of how real users engage with software. Humans bring insight into user behavior, empathy, and an instinct for usability that machines simply cannot replicate. Ensuring that quality assurance includes this human perspective is essential for creating applications that are not only functional but also intuitive and enjoyable to use.

By valuing both automated processes and the insights of skilled testers, companies can deliver software that meets real-world needs and expectations. As we lean on AI and automation for speed and efficiency, let’s remember that true quality is best measured by user satisfaction and the seamless experience they have with our product.