Open-source OPA4T platform enables smarter software testing with AI

In today’s fast-paced software development environments, testing is both critical and resource-intensive. To address this challenge, Turkish project partner Orion Innovation has developed the OPA4T (Optimisation, Prioritisation, and Analysis for Test cases) tool as part of the ITEA project SOSIS. This AI-powered, data-driven framework enhances test analysis by combining scenario optimisation, prioritisation, and log analysis. The result: faster test cycles, earlier defect detection, and more efficient use of engineering resources.

AI-driven root cause analysis and test intelligence

OPA4T converts raw Cucumber Selenium test logs from CI/CD processes into actionable insights through automatic parsing, semantic segmentation, and multi-tool LLM-powered analysis. Its key features include:

• Root cause analysis: Dashboards and conversation-based AI deliver detailed error descriptions, corrective steps, and contextual insights.
• AI assistant: A natural language interactive tool enabling QA teams to query and instantly analyse test execution data.
• Test execution analytics: Smart graphing capabilities for trend analysis, performance metrics, and cross-job comparisons.
• GitLab integration: Real-time webhook-driven automation ensures test reports are ready for analysis within minutes.

In practice, this means that when a CI/CD pipeline fails, OPA4T allows teams to instantly identify the root cause, understand which commits and scenarios are affected, and receive AI-powered recommendations resolve the issue without the need for manual review.

In this video, you can watch a demonstration of the OPA4T tool:

A scalable path towards intelligent quality assurance

The open-source OPA4T platform integrates advanced AI methods such as Retrieval-Augmented Generation (RAG), hybrid vector search, and multi-agent function calls. This provides vendor independence, scalability, and adaptability across industries. Initial evaluations demonstrate significant reductions in repetitive test runs, faster detection of high-risk errors, and improved traceability of test coverage.

Beyond automating routine quality assurance tasks, OPA4T actively improves decision-making processes for developers, test specialists, and engineering managers. The SOSIS project therefore offers a transformative solution for organisations seeking smarter, faster, and more reliable software delivery.

Development continues on Agentic AI features, including the AI Risk Assessment Agent, which calculates risk scores for each test, and the Coder Agent, which executes prioritised test lists after user approval.

More information: https://itea4.org/project/sosis.html and https://www.sosis-itea4.eu/.