Latest news with #testautomation
Fast Company
27-05-2025
- Business
- Fast Company
Achieve scalable test automation with AI-native testing
Most testing strategies collapse under the weight of modern software development demands. But speed requirements continue to increase while application complexity grows, which creates an impossible equation for traditional approaches. AI-native testing is a fundamental reimagination of quality assurance (QA) at scale. After working with hundreds of organizations for testing upgrades, I've noticed one consistent pattern: Test automation used to break at scale, however, that's not the case anymore. With AI-native testing capabilities, teams could amplify developer experience and QA efficiency, ultimately accelerating the release velocity without compromising the quality of product. Every quality strategy exists somewhere on what I call the scale-intelligence matrix. Picture this: Bottom-Left Quadrant: Manual testing provides intelligence, but lacks scale. Top-Left Quadrant: Traditional automation offers scale without adaptability. Bottom-Right Quadrant: Exploratory testing delivers insights without consistency. Top-Right Quadrant: AI-driven testing combines scale with intelligence. Organizations struggling with testing effectiveness inevitably discover they've optimized for just one dimension—either scale or intelligence—but never both simultaneously. Four essential pillars help organizations move into that coveted top-right quadrant: 1. STRATEGIC FOUNDATION Most organizations attempt to layer AI onto existing frameworks and wonder why they hit walls. Scalable AI testing begins with reimagining your testing foundation from first principles. True scale emerges through architectural transformation: Domain-Focused Structure: Structure tests around business domains rather than application layers. Testing boundaries align with business functions, allowing independent scaling of different capabilities. Capturing Testing Intent: Recording actions limits adaptability. Capturing the 'why' behind each test creates space for AI to determine optimal execution paths as interfaces evolve. Moving Beyond Test Pass/Fail: Binary paradigms create brittleness. Confidence scoring reflects the reality of modern applications, quantifying behavior likelihood rather than enforcing absolute correctness. 2. INTELLIGENCE AMPLIFICATION Limiting AI to test execution represents the most common implementation mistake. Genuine scalability demands intelligence across the entire testing lifecycle: Autonomous Test Generation: Application structure, user journeys, and historical defects contain patterns AI can leverage to generate relevant tests—expanding coverage without expanding effort. A retail client discovered 23 critical edge cases previously overlooked when implementing autonomous generation. Dynamic Test Prioritization: Resources remain finite regardless of scale. AI continuously evaluates which tests deliver maximum value based on recent changes, historical failures, and business impact, ensuring optimal resource allocation. Predictive Analytics: Code changes, test patterns, and system behavior contain early signals of quality issues. Advanced AI models identify potential defect clusters before they manifest, shifting quality from reactive to proactive. 3. INFRASTRUCTURE UPGRADES AI strategies cannot exceed the limitations of infrastructure. Scalable AI testing requires a complete rethinking of the execution environment: Ephemeral Environments: Static, persistent test environments create bottlenecks. On-demand environments that spawn, execute, and disappear allow massively parallel testing without environment contention. Distributed Test Orchestration: Centralized execution hits scalability ceilings quickly. Decentralized nodes scaling horizontally under intelligent orchestration create virtually unlimited capacity. Real-Time Data Pipelines: Feedback delays cripple AI effectiveness. Streaming pipelines processing test results and system telemetry enable dynamic adaptation of testing strategies. 4. ORGANIZATIONAL TRANSFORMATION Technology transformation without corresponding organizational change leads to sophisticated solutions delivering minimal value. Successful implementations require: T-Shaped Expertise: Teams need both broad testing knowledge and deep AI specialization. Hybrid professionals bridge the gap between quality goals and AI capabilities. Decision Intelligence: Data without decision frameworks creates paralysis. Clear processes for interpreting AI insights and converting them to actions unlock the full value of testing intelligence. Learning Loops: AI systems improve through feedback. Structured processes for teams to validate, correct, and enhance AI-driven testing decisions create continuous improvement cycles. THE STAGES OF IMPLEMENTATION Scaling AI testing requires deliberate progression. Here are three stages you can expect to go through: Stage 1: Augmentation: Target specific high-value, low-risk capabilities like test maintenance or data generation. Quick wins build confidence while developing organizational expertise. Stage 2: Automation: Graduate to automating entire testing workflows, allowing AI to orchestrate complete testing cycles with human oversight. Stage 3: Autonomy: Self-optimizing testing systems continuously improve based on results and changing application needs, with humans focusing on strategy rather than execution. CONTINUOUS EVOLUTION Successful AI testing programs view quality as a continuous evolution rather than a fixed destination. Mature implementations incorporate: Capability Expansion: Regular evaluation of emerging AI capabilities with integration of those delivering maximum value. Model Refinement: Systematic improvement of AI models through new data, algorithms, and training approaches. Strategic Alignment: Regular reassessment of how testing AI supports broader business and technology objectives. THE PATH FORWARD Organizations that are able to achieve extraordinary results with AI testing have one fundamental perspective. They view AI as a transformation rather than just another cool adoption. Scaling with automation requires a lot more than just taking incremental steps. It needs reimagining QA for the different environments that we work in, and to also take into consideration speed, complexity, and the scale, and use that to grow relentlessly. If you think these strategic foundations, the amplified intelligence, the evolved infrastructure, and the transformed organizational workflows can help your organization break through the traditional constraints, I think it's absolutely worth taking the leap forward and improving QA at scale.
Forbes
21-05-2025
- Business
- Forbes
Balancing Speed And Security: DevOps And Test Automation In The Cloud
Harini Shankar is a technology leader with expertise in quality assurance, test automation, security, devops and cloud-native engineering. getty DevOps has become a foundation of today's fast-paced software development as organizations continue to scale their cloud native applications. But it's becoming challenging to maintain both speed and security. Teams are forced to deliver at a fast pace, but adhering to security and compliance requirements can lead to bottlenecks that slow down the releases. Organizations need to understand that there's a workaround for this. When security and automation are embedded into DevOps workflows and pipelines, organizations can accelerate their releases without compromising cybersecurity. In this article, I cover best practices based on my experience helping DevOps teams balance speed and security while implementing robust and efficient test automation within cloud environments. One of the major mistakes that organizations make is not prioritizing security—it's considered a final checkpoint rather than a proactive part of the process. This mindset often manifests in last-minute security vulnerabilities, forcing developers to go back and spend additional time and effort fixing vulnerabilities that should have been caught earlier. • Incorporate static code analysis (SAST) and automate it to detect vulnerabilities in source code before deployment. • Add automated unit tests and security scans into CI/CD pipelines. • Use test-driven security (TDS) to deny security test cases before actual coding begins. Deployment cycles and releases can be interrupted when manual security testing methods are implemented. When security tests are automated along with functional tests, DevOps teams can maintain velocity without compromising security compliance. • Detect vulnerabilities in running applications with dynamic application security testing (DAST). • Automate infrastructure-as-code (IaC) scanning to help prevent misconfigurations in the cloud. • Implement software composition analysis (SCA) to identify vulnerabilities in open-source dependencies. Security gates can prevent vulnerable builds from progressing, but you'll need to configure them properly so they don't cause delays. Security gates must be designed to balance enforcement with flexibility. • Compliance checks can be automated by defining security policies using tools like Open Policy Agent or Sentinel. • Implement workflows that have automated approvals to prevent deployment delays. Allow minor issues to be flagged for later review without slowing deployment. • Continuously monitor and adjust security metrics as needed. Just focusing on pre-deployment testing isn't sufficient. Organizations need to pay attention to security and functional validation after releases. Continuous monitoring is critical to detect real-world security threats and performance issues. • Employ real-time logging and monitoring in cloud environments to track security events. • Leverage automated canary deployments to validate security patches without the need for a full-scale application rollout. • Use security tools, such as Datadog, to identify anomalies and any policy violations. Applications are becoming more distributed. As a result, APIs and microservices are becoming primary targets for security threats. Security models developed for monolithic applications aren't able to keep up with the complexity of microservice architecture and may fail to provide enough protection. • Use methods such as contract testing to help ensure that API changes don't introduce vulnerabilities. • Implement automated penetration testing for APIs, such as Postman or Burp Suite. • Enforce stricter authentication and authorization with OAuth 2.0 and API gateways. Organizations that treat security as a proactive approach and not as an afterthought are more likely to succeed. But it must be a seamless part of the DevOps process. When organizations embrace continuous test automation, security scanning and compliance, teams can achieve both speed and security in cloud environments. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
