Systems | Development | Analytics | API | Testing

Software testing is struggling to keep pace with modern release cycles. More code, faster deployments, fragmented tools, and increasing quality demands are creating bottlenecks for QA, engineering, and DevOps teams. Discover how Perforce Autonomous Testing transforms the way teams validate software by bringing functional, performance, web, mobile, and desktop testing together through a unified AI-driven experience. Using natural language, teams can define testing intent, automate execution, orchestrate complex workflows, and gain actionable insights faster than ever before.

Traditional test automation is slowing teams down with brittle scripts, constant maintenance, and growing complexity. Perforce Autonomous Testing changes the game with enterprise-grade AI-powered testing that helps teams create, execute, maintain, and analyze tests using natural language—without scripts, frameworks, or ongoing upkeep.

What if testing could finally keep up with your release speed? With Perforce Autonomous Testing, teams can simply describe what they want to validate, and AI takes care of execution, orchestration, and analysis across functional and performance testing. Built on a unified testing platform, Perforce brings everything together into a single workflow to eliminate manual setup, reduce reliance on specialists, and deliver faster, in-sprint feedback.

Why do organizations choose open source software? Is it primarily about cost savings, avoiding vendor lock-in, digital sovereignty, long-term support, trusted transparency, or community-driven innovation?

Is open source licensing still a major challenge for enterprises—or has security become the bigger concern? In this clip from the 2026 State of Open Source Report webinar, experts from the Eclipse Foundation, Open Source Initiative (OSI), and Perforce OpenLogic discuss how open source adoption challenges have evolved over the past decade. While licensing complexity was once a top concern for organizations, the maturation of the open source ecosystem and the widespread adoption of well-established licenses have helped reduce that burden.

What does the EU Cyber Resilience Act (CRA) mean for open source software, software supply chain security, and regulatory compliance? In this clip from the 2026 State of Open Source Report webinar, experts discuss how the European Union's Cyber Resilience Act (CRA) is reshaping software security requirements for organizations that develop, distribute, and integrate software products. The panel explores why the CRA is one of the most significant regulatory developments affecting the software industry and how it impacts both commercial software vendors and the open source ecosystem.

Software teams move fast — until they hit a data bottleneck. Meet the Delphix DevOps Data Platform from Perforce. Delphix automates the delivery of compliant, production-quality, and AI-ready data so teams can validate and release software at AI speed. With Delphix, you can: Provision and refresh virtual datasets in minutes, not days or weeks. Automatically discover and mask sensitive data while preserving relationships across systems.

AI-assisted code remediation, a new feature in Perforce Static Analysis tools QAC and Klocwork, speeds up development by providing immediate fix suggestions to reported issues. Launch faster, with confidence.

When AI Starts Shipping Code: Managing the Collision Between Human and AI-Generated Code AI agents don't wait for reviews. They generate code overnight, work across the same codebase in parallel, and produce more changes than any human team can realistically process — creating a new kind of bottleneck we call the Merge Wall. In this session, Perforce engineering leaders break down what happens when human and AI-generated code collide at scale — and how leading teams are building the visibility, governance, and coordination layers required to keep up.

AI testing against live LLM APIs can quietly drive massive token costs across development, QA, and CI/CD pipelines. Every test execution consumes real tokens—at production rates—creating hidden, variable costs that scale with your AI adoption. In this video, discover how leading enterprises are eliminating LLM token spend using service virtualization. Learn how BlazeMeter intercepts API calls, simulates realistic AI responses (completions, embeddings, and large payloads), and enables full-scale testing without invoking live models.