Altice Labs is a leading telecommunications technology provider driving innovation in fiber-based networks and shaping the future of Advanced Connectivity, Business Support Systems and Operations, and Digital and Data-Driven Platforms through trailblazing solutions and products.
Across the organization, multiple engineering teams manage different validation processes, including software systems, infrastructure platforms, and hardware qualification.
This success story focuses on the hardware testing team responsible for validating Optical Line Terminal (OLT) platforms, the core equipment that connects telecom operators to thousands of end users, while ensuring interoperability with Optical Network Terminal (ONT) devices installed at customer premises.
These platforms power fiber access networks and support multiple generations of high-speed broadband technologies, ranging from Gigabit Passive Optical Network (GPON) to the latest multi-gigabit standards, such as 50G-PON.
With more than 15,000 automated tests and continuous daily builds, testing is a core pillar of product reliability. Ensuring performance, scalability, interoperability, and security across complex telecommunications environments requires a structured and highly traceable testing strategy.
The Challenge
Prior to using Xray, most validation activities at Altice Labs were manual.
“More than 10 years ago, most of our testing was done manually. That meant test coverage was limited, the time needed for each release was very high, and testers spent a lot of their time repeating the same tasks.”
Celso Covelo, System Test Manager at Altice Labs
Manual testing limited coverage and significantly extended release cycles. Only a restricted set of scenarios could be executed due to time and physical resource constraints. As fiber technologies evolved and product portfolios expanded, this approach became unsustainable.
Testing optical network equipment introduces significant technical complexity. A single OLT platform must support multiple fiber technologies and high-speed Ethernet uplinks, while ensuring interoperability across different ONTs, gateways, vendors, and chipset generations. Additionally, OLT families include models capable of supporting up to 32,000 clients.
Load environments can involve around 2000 ONTs connected simultaneously, where test outcomes may vary between runs. Identifying which failures require revalidation and understanding their impact is critical.
As automation expanded, additional challenges emerged:
- Managing more than 15,000 automated tests efficiently.
- Maintaining full traceability across requirements, tests, and defects.
- Standardizing reporting practices across teams.
- Addressing performance adjustments after migrating to Jira Cloud.
- Improving collaboration between development and testing teams.
Without a centralized solution, ensuring consistent coverage, visibility, and reporting became increasingly difficult.
The Solution
Altice Labs selected Xray as the primary test management tool within its SDLC. The SDLC framework applies across hardware development in fiber technologies, as well as OSS-BSS systems (Operations Support Systems and Business Support Systems used for managing telecom operations and customer services), connected home products, TV platforms, and emerging AI initiatives.
Xray now serves as the unified platform where requirements, tests, automation, and defects converge:
“We choose Xray as our central source of truth because it gives us one unified place to manage all our testing. It brings together requirements, tests, automation, and defects, giving us clear visibility and reliable traceability across the whole release cycle.”
Celso Covelo, System Test Manager at Altice Labs
All automated test definitions are centralized in Xray rather than distributed across code repositories. The execution layer queries Xray directly to determine which tests to run, ensuring that every result is tracked consistently and with full traceability.
Traceability became a foundational capability. Teams can clearly identify:
- Which requirement a test covers.
- When and where each test was executed.
- The historical status of executions.
- The impact of failed tests across the system.
This centralized structure replaced fragmented reporting approaches and custom-built tools, giving the organization a standardized view of progress and release readiness.
Automation at Scale with Intelligent Selection
Today, more than 95 percent of OLT tests are fully automated, totaling over 15,000 automated scenarios. Test specifications are written in Gherkin, with step definitions originally implemented in Ruby and now transitioning to Python 5.0 to benefit from new interfaces and broader library support.
The CI/CD pipeline supports daily, testable builds. However, running the entire test suite daily would take two days or more. To maintain fast feedback cycles, the team uses intelligent selection criteria to determine which subset of tests runs each night. Selection is based on recent failures, priority levels, execution history, and runtime optimization.
Full regression cycles are executed on weekends or when closing a specific version, ensuring complete validation without compromising day-to-day efficiency.
AI and Research-Driven Optimization
Altice Labs is also advancing internal initiatives focused on machine learning and AI-based optimization, including a collaboration with the University of Coimbra.
These initiatives aim to improve two critical stages of the testing lifecycle. The first is test selection, where historical execution data, failure patterns, and risk indicators are used to predict which tests should be prioritized during nightly runs. The second is result analysis, where models are explored to detect patterns in failures and accelerate root cause identification.
This innovation depends on structured and reliable testing data.
“Xray is critical here because it provides the structured data we need: execution history, priorities, and risk tags.”
Ricardo Cadime, Process Development Manager at Altice Labs
Although the AI initiative operates independently, the quality and organization of data stored within Xray form the foundation that enables this research.
The Results
With Xray embedded in its SDLC and CI/CD processes, Altice Labs has built a scalable and standardized testing framework capable of supporting high-performance fiber technologies and evolving hardware platforms.
Key Outcomes
- More than 15,000 automated tests managed within Xray.
- Over 95 percent of OLT tests are fully automated.
- Centralized traceability across requirements, tests, and defects.
- Standardized reporting across engineering teams.
- Optimized nightly execution using intelligent test selection.
- Structured data foundation supporting AI-driven test prioritization.
“Xray has elevated our testing strategy by centralizing everything in Jira, improving traceability, accelerating feedback loops, and enabling confident, data-driven release decisions.”
Celso Covelo, System Test Manager at Altice Labs
Automation and manual tests now operate within the same framework, ensuring consistency in coverage, reporting, and release governance. Collaboration between QA and development has improved through shared visibility within Jira and Xray.
