Sii Poland
SII UKRAINE
SII SWEDEN
Sii Poland
SII UKRAINE
SII SWEDEN
Sii tests and validates embedded software and hardware components through certified testing processes, embedded engineering expertise, and AI-assisted workflows across performance, compliance, safety, and quality requirements.
Having strong quality assurance expertise and experience, we support our clients in the right choice of testing methodologies and tools to meet project-specific requirements. The process consists of multiple activities:
Our experts will guide you through the quality assurance process via selecting a progress-tracking technique and preparing output documentation.
We offer embedded software testing on different levels:
Sii’s Quality Assurance Engineers ensure, monitor, and improve the overall software quality, along with its security, stability, and effectiveness via executing various types of tests, including:
As AI is part of our engineers’ everyday workflow and integrated into the SDLC, Sii QA engineers, embedded developers, architects, and DevOps specialists use AI assistants to make test scenario creation and execution more time- and resource-efficient. AI helps accelerate test script generation, support test execution, automate repetitive tasks, and inform engineering decisions, while Sii experts maintain full human oversight.
Instead of simply collecting test results, Sii transforms testing data into actionable engineering insights.
Using AI, Sii automatically identifies:
Our reporting combines:
Testing is meant not only for quality assurance, but also to verify the compliance of devices and software with various standards:
We provide a clear and detailed indication of any non-compliance found in:
Compliance remains a human responsibility – but AI can dramatically accelerate the preparation process. AI helps identify potential compliance gaps, missing traceability, documentation inconsistencies, and insufficient verification coverage long before formal certification activities begin.
Sii combines AI-supported compliance analysis with certified tools such as VectorCAST and experienced certification engineers, reducing preparation effort while improving confidence in certification readiness.
Sii continuously develops internal AI accelerators, validation platforms, and experimental sandboxes where new AI technologies are safely evaluated before being introduced into customer environments. This allows Sii experts to validate accuracy, security, explainability, and engineering value while minimizing AI adoption risks for clients. We are actively evaluating AI agents capable of supporting software verification, automated defect investigation, requirements analysis, regression impact assessment, and documentation generation under human supervision. This approach helps clients benefit from AI-supported embedded software testing without replacing formal engineering judgment, compliance control, or certification responsibility.
Sii’s embedded QA teams bring proven experience across automotive, medical, and industrial projects. We design test strategies that reflect real hardware–software constraints and certification needs, delivering 100% unit-test coverage, full traceability, and audit-ready reports with certified toolchains (e.g., VectorCAST).
From strategy to test execution and reporting, we deliver comprehensive testing support that ensures security, functionality, and overall quality. Our expertise spans white-box analysis, fuzz testing, stress testing, and other advanced methodologies – delivering embedded applications that comply with the highest industry standards and perform reliably in real-world conditions.
Our certified toolchain and structured methodology let us test even when target hardware is limited or arrives late – using software simulators, hardware emulators, and stubs. We cover the core validation scope from integration and regression to user acceptance testing, with full traceability, coverage analysis, and certification-ready reports (including VectorCAST). Outcome: reduced risk and cost, faster time-to-market, and compliant, stable software.
Read out FAQ
Embedded software testing differs from standard software testing primarily due to the close interaction between software and hardware. Embedded systems often feature tightly coupled hardware–software components, which can make testing more complex and constrained. Unlike typical software testing, embedded testing frequently requires specialized tools, including hardware simulators, emulators, and in-circuit debuggers, to validate system behavior under real-world conditions.
Challenges in embedded testing include limited access to hardware, strict timing constraints, high safety, security, and compliance requirements, and a need for specialized testing tools. Many embedded systems are designed for real-time performance, which adds complexity to the testing process.
Embedded testing covers defined levels – unit, component, integration, and system – and specific test types, including smoke, regression, sanity, and user acceptance testing. The right mix is set in the testing strategy to meet project-specific requirements and is executed with supporting assets such as software simulators, hardware emulators, stubs, and certified toolchains like VectorCAST.
Embedded security testing involves testing software components for vulnerabilities that could be exploited through bugs or integration flaws. It’s not limited to software alone – it also addresses hardware and software components interaction, secure boot processes, and resilience against external attacks.
Automated testing allows engineers to run repetitive test cases faster and more consistently. With embedded testing, automation also enables dynamic analysis tools to test software across different hardware scenarios using simulators or emulators. This helps fix issues earlier and supports stress testing under load.
To ensure compliance, we follow established best practices for embedded systems and leverage certified tools such as VectorCAST for software verification. Our testing aligns with relevant industry standards – covering functional correctness, performance requirements, and documentation obligations – to ensure the software meets regulatory and certification criteria.
Tools commonly include software simulators, hardware emulators, and stubs to substitute unavailable hardware, plus certified toolchains such as VectorCAST for unit and integration testing with full traceability. Teams also rely on automated testing frameworks and compiler toolchains, and they routinely script test harnesses and utilities in Python (and other scripting languages, e.g., Bash or PowerShell) to orchestrate runs, generate fixtures, parse logs, and produce certification-ready reports. This ensures both embedded application logic and hardware interactions are validated reliably.
Best practices for embedded software testing include starting with a clear embedded testing strategy, using automated testing tools, and ensuring full test coverage. It’s also important to isolate hardware dependencies using emulators and simulators, apply regression testing regularly, and maintain full traceability between test cases and software requirements. Testing is most effective when aligned with certification and compliance standards from the start.
An effective test case for embedded systems clearly defines the input data, expected outcome, execution conditions, and validation method. In embedded testing, test cases must often account for hardware–software interactions, timing constraints, memory limitations, and other system-specific considerations. Organizing test cases based on risk level, criticality, and coverage objectives ensures that the most important software components are validated early and thoroughly. This approach improves reliability, reduces defects, and supports compliance with industry standards.
AI is becoming important in embedded software testing because it helps engineering teams analyze more test data, identify defect patterns faster, and focus verification effort on the highest-risk areas. AI-assisted workflows can support requirements analysis, test scenario generation, anomaly detection, regression impact assessment, and missing coverage identification. At Sii, AI is used as part of the engineers’ everyday workflow to support testing decisions while keeping full human oversight.
AI improves embedded testing reporting by turning raw test results into actionable quality insights. It helps identify recurring failures, unstable software modules, regression risks, performance anomalies, certification gaps, root causes, and missing test coverage. Sii combines AI-generated quality insights with bug reporting, requirement traceability, test coverage analysis, progress tracking, and engineering recommendations to support faster validation, release planning, and certification preparation.
AI can support multiple stages of the embedded testing lifecycle, including test case generation, test maintenance, defect classification, log analysis, and result interpretation. It can also help automate repetitive engineering tasks and prioritize regression testing based on risk. Combined with CI/CD pipelines, simulators, emulators, and certified testing tools, AI-assisted testing helps teams accelerate verification without replacing engineering judgment.
AI can support safety-critical embedded testing by improving test generation, coverage analysis, anomaly detection, documentation review, and compliance gap identification. However, in regulated industries such as automotive, railway, aerospace, and medical devices, AI should support engineers rather than replace formal verification or certification processes. Sii uses AI under human supervision, combining AI-assisted analysis with certified tools such as VectorCAST and experienced certification engineers to support readiness for standards such as ISO 26262, IEC 62304, EN 50128, and DO-178C.
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