As the shift from Quality Assurance (QA) to Quality Engineering (QE) gathers momentum, automation has emerged as an essential enabler of this transformation. In this latest model, automation is not just a tool for executing repetitive tests but a strategic approach that embeds quality throughout the software development lifecycle.
In the traditional QA model, automation testing was primarily used for regression testing and other repetitive tasks at the end of the development process. This approach often led to siloed teams, delayed feedback, and costly fixes late in the cycle. QE, on the other hand, emphasizes a holistic approach where quality is integrated from the beginning, driving the need for more sophisticated and proactive testing methods.
1. From post-development to shift-left and in-sprint automation
In the QA model, testing often occurred after development was complete, leading to delays and increased costs for defect resolution. QE adopts a shift-left strategy, moving testing activities earlier in the development process and integrating them within sprints. In API testing, shift left starts with linting the API specifications, followed by mocking the API to test it before deployment and generate test scripts from specifications. In-sprint automation enables immediate feedback on code changes, allowing teams to identify and address issues as they arise. This proactive approach not only reduces time to market but also ensures that quality is built into the product from the start.
2. From reactive testing to AI-powered predictive and prescriptive Testing
Traditional testing relied heavily on regression testing around the defects found using predefined scripts, which could be time-consuming. With AI-powered testing, AI/ML is leveraged to predict potential areas of defect and prioritize the test suite by analyzing historical defect trends and current changes to the code. This shift enhances the accuracy and efficiency of testing, enabling teams to handle high-risk areas earlier in the testing phase.
3. From integration testing to continuous automation
Integration testing, once a discrete phase in the development cycle, has evolved into continuous automation in QE. This approach embeds automated testing within continuous integration/continuous delivery (CI/CD) pipelines, allowing tests to run automatically whenever code changes occur. Continuous automation ensures that new features are consistently tested and validated, reducing the risk of integration issues and accelerating the overall development process.
4. From data testing to data quality assurance
In the age of data-driven applications, ensuring the quality of data is as important as testing the functionality of the software. QE expands traditional data testing beyond CRUD operations into data quality assurance, focusing on the accuracy, consistency, and integrity of data across the entire lifecycle. With the shift to QE, we see the inclusion of data observability which deals with monitoring the data pipeline to identify anomalies. AI-powered tools can then automatically detect data anomalies, validate data transformations, and ensure that data privacy and compliance standards are met.
5. From performance testing to performance engineering
Performance testing has traditionally been a reactive task performed towards the end of the development cycle. QE redefines this as performance engineering, an ongoing practice that proactively embeds performance validations at every stage of development. A prominent shift in this area includes using tools like K6 and Gatling during unit testing. Transitioning from synthetic performance tests to real-world usage simulations is another characteristic trend. Deploying tools that can continuously monitor application performance, identify bottlenecks, and suggest optimizations, ensures that performance standards are met before issues impact end-users.
6. From security scanning to DevSecOps
Security is no longer an afterthought or a separate phase; it’s embedded throughout the development process in QE. Implementation of DevSecOps by integrating security checks into CI/CD pipelines, using automated thread modeling, for example, ensures that vulnerabilities are identified and mitigated early. AI-driven security testing tools can continuously scan for threats, evaluate security postures, and provide real-time insights, making applications more resilient against evolving threats.
In traditional QA, functional and automation teams often worked in silos, leading to inefficiencies and misaligned objectives. QE promotes an automation-first approach that ensures all teams contribute and benefit from shared frameworks. Use of integrated platforms, to build a unified testing strategy and encourage reusability of test artifacts, encourages cross-functional collaboration, making quality everyone’s responsibility.
Dependencies on third-party integrations and fragmented processes can disrupt testing workflows. Automation testing in QE helps re-establish business processes and data flows, aligning them with the overall quality strategy. Implement data pipeline automation to verify data quality at every stage. This alignment ensures that testing supports business objectives, maintains data integrity, and optimizes end-to-end processes.
Effective change management requires collaboration and joint decision-making among stakeholders. QE leverages real-time dashboards, published KPIs to foster collaboration and align quality goals across teams. This transparency fosters a collaborative environment where quality metrics drive continuous improvement.
Legacy tools and fragmented frameworks often create bottlenecks in the testing process. QE addresses this by adopting a federated tool stack guided by blueprints, which standardizes and streamlines the testing environment. Automation tools are selected based on their ability to integrate seamlessly and support QE’s holistic approach, enabling teams to work more effectively and leverage the best technologies. Regularly evaluate and consolidate tools.
QE transformation needs a mindset shift from Quality as the testing team’s responsibility to Quality as shared responsibility across teams. Emphasize cross functional quality ownership with shared goals and effective agile rituals like retrospectives. Use automated quality gates to enforce standards. This transparency empowers teams to continuously refine their processes and drive better outcomes.
The integration of AI and automation into QE is just the beginning. As these technologies continue to evolve, we can expect even more advanced solutions that drive autonomous testing, predictive analytics, and smarter quality management. The future of automation testing in QE will see an increasing emphasis on resilience, adaptability, and continuous learning, helping organizations navigate the complexities of modern software development.
Qualitest offers in-sprint and shift-left automation solutions. Our in-sprint automation focuses on automating tests within the sprint, aiming for a target of 70% automation coverage. It involves optimizing test automation across UI, API, and DB layers, implementing in-sprint automation with frameworks like Qualiframe, and using service virtualization.
Our shift-left automation emphasizes early automation and testing, enabling the identification of defects and security vulnerabilities at an early stage of the development lifecycle . It involves implementing comprehensive test automation and non-functional testing (NFT) strategies, aligning test processes across teams, and introducing quality gates and intelligent coverage analysis.
These automation approaches help improve testing efficiency, reduce defects, and accelerate the delivery of high-quality software. If you would like to learn more about our cradle to grave approach to quality engineering, contact us.
Automation testing is at the heart of the shift from QA to QE, enabling organizations to move beyond traditional testing practices and embrace a more integrated, proactive approach to quality. By leveraging AI-powered solutions, continuous automation, and a KPI-driven mindset, QE transforms how quality is embedded into software development, making it faster, more efficient, and more aligned with business goals.
As software development becomes increasingly complex, automation testing will continue to evolve, providing the tools and capabilities needed to maintain high standards of quality. Embracing QE is not just about improving testing processes—it’s about reimagining the role of quality in the digital age and positioning your organization for success in an AI-driven world.
Balaji Ponnada is Associate Vice President and heads Test Automation & Phygital CoEs at Qualitest from India. Balaji has been a seasoned professional in software testing for 19 years, he is currently leading research and development in five key areas: Building solutions in Cognitive automation and AI in automation space, Consumer Internet of Things, Industrial Internet of Things, Automotive (Infotainment & Connected Cars) and Internet of Medical Devices. He is an expert consultant in Test Automation and seasoned professional in setting up test strategies for Phygital ecosystems.
Connect with Balaji on LinkedIn.
