A recent report by Gartner predicted that, by 2025, AI will be involved in 80% of all software engineering tasks, dramatically changing the IT landscape within the enterprise. There will be more code that will be generated, and more applications will be infused with AI capabilities. This new paradigm will change what we test and how we approach quality in software development.
To ensure robust, reliable software products and remain competitive, enterprises in every sector must make the transition to Quality Engineering (QE). This shift isn’t just a trend to keep an eye on — it is, right now, a necessary evolution in the age of AI and automation.
From QA to QE: Adapting to new realities
What is QA?
Quality Assurance (QA) is a traditional approach focused largely during the test phase before the release of a software product. Identifying defects after code is written and application is considered a black box for testing. In QA, testing is done through inspection, regression suite automation, performance testing and security scans.
What is QE?
Contrastingly, Quality Engineering (QE) embeds quality into every stage of the software development life cycle (SDLC) in a holistic approach. QE integrates testing earlier and throughout the SDLC, from requirement gathering/user stories to production. QE deploys in-sprint continuous automation to improve speed, coverage, and quality of test. It focusses on developer velocity rather than just finding defects. This supports the speed and flexibility required in today’s enterprise.
AI: catalysts for change
The arrival of AI and especially generative AI (GenAI) has disrupted the tech landscape forever and has been a big accelerator for the QA to QE transition. In this recent whitepaper, Qualitest and Everest, a leading global research firm talks about the impact of AI on how the QA function delivers and highlights the possibility of improved efficiency. This efficiency will become increasingly important as the quality function within the enterprise will start to focus on AI governance, data quality, model evaluation, digital safety amongst others with the same budget or less.
AI in Quality Engineering
AI as a technology now has a broader and deeper impact on the entire testing life cycle, from writing test cases to analyzing user feedback in production. At Qualitest, AI is infused in all forms of testing, truly making Quality Engineering a reality. AI has improved efficiency and improved quality across a wide range of testing including but not limited to:
- Creating scenarios and test cases from user stories
- Spotting data quality issues and highlighting privacy concerns in data lake
- Identifying performance bottlenecks and patterns from execution logs
- Reducing technical debt in automation, improving agility and reducing maintenance effort
Qualitest foresaw the impact that AI would have on the business world and began to prepare a decade ago. Today, the incredible adaptability of our Qualitools is down to AI and automation. We use automation and AI within our QE tools to help customers transition from QA to QE and also prepare enterprise to handle the future state through our QE for GenAI apps offer.
Case study: Tech giant’s transition to Quality Engineering
Qualitest enabled a Fortune 500 company transitioned from QA to QE, resulting in:
- 55% reduction in time-to-market for new features
- Near zero post-release defects and
- 30% increase in developer velocity
The urgency of transition
As AI enters every facet of business, the margin for error shrinks dramatically. As the current tech landscape offers tools that make this changeover more reachable than ever. Embedding AI into QE practices allows for:
- Automation of routine tasks
- More focused resources on strategic activities
- Accelerated time-to-market
And enables enterprise to handle the complexities of AI applications, including:
- Bias detection and mitigation
- Hallucination prevention in generative AI
- Security vulnerabilities specific to AI systems
Roadmap to Quality Engineering
Typically, when a new enterprise client tells Qualitest they want to move to quality engineering best practices, we deploy our ‘3Cs’ assessment framework with its three distinct phases: converse, calibrate and counsel.
- Converse – how will we assess? Collect BU and application specific details through Surveys and health checks, Stakeholder interviews for a more in-depth dive of the processes and AI powered data analysis.
- Calibrate – what will be assessed? – Against industry maturity levels, 0-4 baselining of current state maturity for Test planning & design, Test execution, Test data, Test environment, Defect management, Test automation, Non-functional testing, Governance & metrics and Operating model.
- Counsel – what is the output? – Current state maturity report consisting of current state challenges, critical issues and identified gaps, Target state maturity definition and Recommended roadmap for transformation.
The future of Quality Engineering
As AI and automation continue to advance, QE will likely evolve to include:
- AI-driven autonomous testing systems
- Enhanced predictive analytics for proactive quality management
- Trustworthiness of AI infused applications
Quality Engineering: an imperative for modern enterprises
The transition from QA testing to quality engineering is not just about onboarding new best practices — it’s about reimagining your approach to software quality in the AI era. By entrenching quality throughout the development process, leveraging AI and automation, and fostering a culture of continuous improvement, enterprises can deliver reliable, ethical, and high-performing applications that meet the demands of today’s digital world.
The time to make this transition is now. In a landscape where AI-driven innovations are reshaping industries overnight, the risks of outdated quality practices are too great to ignore. Embrace quality engineering today, and position your enterprise at the forefront of the AI revolution.
Meet the author – Subbiah Muthiah
Subbiah Muthiah is Chief Technology Officer – Emerging Technologies at Qualitest. based in Chennai, India. Subbiah has extensive experience as a global thought leader in emerging technologies including Predictive and Generative AI, IoT – industrial and consumer, Telematics/Connected Car, QA for Blockchain /Web 3.0, Cloud Architecture, Cognitive Automation, Data Quality, AI Date Services, Mobile (protocol and applications), and UX.
Connect with Subbiah on LinkedIn.
