By Dinesh Mohan
The future of quality assurance (QA) and engineering (QE) is undergoing a significant transformation. The focus now has shifted from mere cost-effectiveness and product quality to prioritising customer experience (CX). It’s no longer just about whether the software works but about how well it meets the experiential demands of users, necessitating a thorough understanding of the business, its services/products, and the technologies employed.
Recent findings from the Business Transformation Index 2023 reveal a concerning trend. 76% of firms are falling short on their business transformation initiatives with an alarming 66% missing the mark when it comes to criteria like—staying within budget, timely delivery, or solution reliability. This uptick from 50% in 2022, is a wake-up call highlighting the critical need for robust QA/QE practices, irrespective of the industry. Enter AI in QA—the game changer.
How can AI offer a turnaround?
No longer seen as just a final checkpoint, Quality Assurance and Engineering is now an integral part of the entire software development lifecycle. The narrative now is shift left or get left behind emphasising the importance of early and frequent testing. And AI is enabling this shift by automating various tasks. AI-powered tools analyse code, generate test cases, and execute tests automatically, freeing up testers to focus on more complex activities.
The integration of AI into QA engineering is empowering testers to:
• Automate repetitive tasks and focus on more strategic testing activities
• Move beyond reactive testing towards predictive analysis—predicting and preventing defects from occurring in the first place
• Optimise test data management and improve test coverage
• Generate insightful test reports and provide actionable recommendations
• Collaborate effectively and share knowledge across teams
Artificial Intelligence (AI) as a transformative force also helps overcome the challenges of:
- Budgetary Control: To provide cost-effective testing, minimise manual labour, and maximise resource allocation, software testers must embrace AI-driven QA/QE models.
- Timely Delivery: A collaborative effort between human intuition and AI-driven insights lends a hand in identifying the potential bottlenecks early.
- Solution Reliability: Analysis of vast datasets by AI facilitates predictive maintenance along with empowering testers to pre-emptively address potential software glitches or failures.
However, building a competent QA/QE practice isn’t just about ticking boxes; it’s also about fostering talent. The big question is: In this era of digital transformation-
Will AI-driven automation eclipse the need for human QA/QE professionals?
We’re witnessing a blurring of boundaries between industries. Innovations in one sector are rapidly influencing others. This crossover, while improving customer experience, challenges quality engineers to adapt to diverse technological landscapes.
The fear that AI might render human QA professionals obsolete is, in my opinion, unfounded. Instead, AI is redefining career paths in QA/QE. Testers equipped with AI proficiency are today invaluable assets.
In fact, the changing dynamics have led many companies to rethink the traditional developer-tester ratio. Consider this, ten years ago, a typical ratio of QA testers to developers might have been 1:10 or 2:10. This means that for every 10 developers, there would be 1 or 2 QA testers. Fast forward to today, this ratio is starting to shift, and it is not uncommon to see ratios of 3:10 or 5:10. This means that there are now more QA testers for every developer.
This ratio is likely to continue to increase in the future due to the increasing complexity of software, the growing importance of quality, and the rise of agile development methodologies.
While automation is gaining momentum, the human element in software testing remains irreplaceable. AI can smartly automate processes, but can never wholly replicate the nuanced understanding and decision-making capabilities of a human tester. Manual testing is absolutely necessary in cases such as—exploratory testing, usability testing, security testing, edge-case testing, providing a second layer of validation, identifying new test cases, and providing feedback to developers.
The future of QA/QE in the AI-Augmented world
It’s true that the comparison between manual and automated testing might currently lean heavily towards automation, yet we can’t overlook the value of manual intervention. The undeniable fact is that AI can revolutionise Technology but the ingenuity, creativity, and intuitive power of People remain the most crucial and irreplaceable parts of the Process.
Quick, efficient delivery of high-quality products/services is a hallmark of successful businesses. And a synergistic approach where AI and automation enhance human capabilities rather than replace them is central to this achievement.
In a future where AI and human ingenuity coalesce to redefine software testing, how can we use technological advancements as a springboard for innovation and excellence?
The author is head of delivery and operations, digital practice, Expleo