Verification & Validation Expertise

Verification & Validation Expertise

Test infrastructure, automated software-to-vehicle level testing, and SIL/MIL/HIL simulation & testing to ensure the safety, reliability, and performance of vehicles.

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Accelerating the journey to safe and reliable automobiles.

V&V approaches are evolving to address the challenges presented by software & services, personalization, autonomous driving, and connected & electric vehicles. This includes software/system requirements & integration testing, system health and performance profiling ensuring cybersecurity and functional safety of complex systems.
V&V is embracing AI and machine learning to automate the test lifecycle – intelligent test case generation, execution, and analysis, and advanced HIL systems and virtual environments to conduct time and cost-efficient testing before physical prototypes are built. Shift-left methodologies call for integrating V&V within DevSecOps pipelines, enabling continuous testing and security assessments throughout the development lifecycle leading to earlier bug detection and faster resolution.

How Acsia Can Help?

Expertise in planning and executing software level to vehicle level tests across Digital Cockpit (IVI, IC, HUD), Telematics (TCU), EV (battery management, DC-DC chargers), AI (voice recognition, CARLA), ADAS, and Out-Car (mobile app, connectivity, cloud communications).

Labs, simulators, tools, and know-how to perform Functional/Non-functional, Connectivity, Security & Safety, Application, Performance & Stability, Black Box, 24×7 Light-Out, FOTA/OTA, Integration, System, Software, SIL/MIL/HIL, Vehicle & Field testing.

Highly scalable and configurable internally developed tool capable of managing automated test runs for various ECU types, log analysis, sanity & regression, and image & language. Automated bug detection & environment/test failure reports. Integrated with real-time HMI touch automation framework (including test case generation) and Grafana (observability).

Acsia’s robust Verification & Validation capability helps OEMs to navigate the increasing complexity of modern vehicle features like ADAS, AD, and connected car technologies. The intricate interplay between hardware and software necessitates deep expertise in establishing test infrastructure, automating software-to-vehicle level testing, and SIL/MIL/HIL simulation & testing to ensure the safety, reliability, and performance of vehicles.

Project Highlights

Defined and executed end-to-end software and system testing, covering integration, functional, performance, security, and in-vehicle tests with automation.

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Implemented automated pre-integration, CI/CT processes, developed test frameworks, performed KPI testing and trend analysis, and conducted integration testing for all IOC CRs.

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Conducted speech and feature testing, automated tests using ECU Test, performing Defect/problem management (quality of the tickets, analysis, identifying appropriate domain and tracking to closure)

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Performed integration, SW and system testing, validated buck-boost converter features and functional safety, verified battery charging, and performed automated HIL testing using vTestStudio, CANTATA and Polarion.

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Hear From Experts

Valliaappan Chidambaram
AVP, Verification & Validation
With Acsia since 2018
"The global shift toward intelligent, software-defined vehicles demands robust and scalable validation. At Acsia, we bring deep expertise in Verification & Validation—spanning Digital Cockpit, Telematics, EV, ADAS, and Cloud. Backed by ASPICE L3-compliant test suites, in-house automation frameworks, and 24x7 lab setups, we help OEMs and Tier-I suppliers accelerate product maturity and ensure uncompromised quality."
OEM Production Program Experience:
Tier-I Experience:
Why Acsia?
Scalable VaaS
Highly scalable Verification/Validation as a Service spanning SW component/system/vehicle-level testing and enabling OEMs to shift left.
Proven experience
Acsia has performed SIL/MIL/HIL simulations and testing for leading OEMs and Tier-I suppliers.
Ready and Re-usable
Benefit from readily available modular components and test infrastructure and reusable test framework and licenses.
Automation
AI/ML powered test automation framework capable of executing end-to-end test lifecycle from planning to reporting.
Stringent Compliance
In-depth comprehension of the global regulatory requirements, and auto industry-specific standards (ASPICE, ISO 26262, VDA, SAE).
Highly scalable Verification/Validation as a Service spanning SW component/system/vehicle-level testing and enabling OEMs to shift left.
Acsia has performed SIL/MIL/HIL simulations and testing for leading OEMs and Tier-I suppliers.
Benefit from readily available modular components and test infrastructure and reusable test framework and licenses.
AI/ML powered test automation framework capable of executing end-to-end test lifecycle from planning to reporting.
In-depth comprehension of the global regulatory requirements, and auto industry-specific standards (ASPICE, ISO 26262, VDA, SAE).
What’s In It For You

Rigorous V&V ensures vehicles meet stringent safety standards like ISO 26262 and ASPICE, minimizing risks and protecting lives.

Early detection and resolution of defects during V&V lead to significant cost savings compared to fixing issues later in the development cycle.

Automated testing and streamlined processes accelerate V&V, enabling OEMs to deliver innovative features to customers faster.

Through thorough V&V, OEMs can ensure their vehicles perform consistently and reliably, enhancing customer satisfaction and brand reputation.

Frequently Asked Questions

Verification and Validation (V&V) are critical processes in automotive software development that ensure the software meets specified requirements and functions correctly. It helps identify and rectify defects early in the development lifecycle, enhancing the safety, reliability, and performance of automotive systems.

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A Comprehensive Testing Portfolio covering software to vehicle-level testing across Digital Cockpit, Telematics, EVs, AI, ADAS, and cloud solutions, ASPICE L2 Compliant Test Suite, and In-House Test Automation Framework ensure end-to-end verification and validation, enhancing efficiency, reliability, and compliance in automotive software testing.

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Acsia utilizes advanced Hardware-in-the-Loop (HIL) systems and virtual environments to conduct comprehensive testing before physical prototypes are built. By adopting shift-left methodologies, verification & validation is integrated within DevSecOps pipelines, enabling continuous testing and security assessments throughout the development lifecycle. This proactive approach ensures early detection of bugs and vulnerabilities, contributing to the functional safety and cybersecurity of vehicles.​

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Yes, Acsia’s internally developed test automation framework, TITAN, is highly scalable and configurable, allowing it to be tailored to meet specific project needs. This flexibility ensures that the testing processes align with the unique requirements of each project, enhancing the effectiveness and efficiency of the verification & validation efforts.​

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  • Testing for Rear Seat Entertainment system for a German OEM through a Japanese Tier-1.
  • IOC Testing of a Digital Cockpit for a German OEM through a German Tier-1.
  • Speech Testing for a German OEM through a German Tier-1.
  • DC-DC Converter for a German OEM through a German Tier-1.
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AH2025/PS06 | AI/ML

Context

Continuous employee learning is essential for companies to stay competitive in a fast-changing business environment. Organizations adopt Learning Management Systems (LMS) to upskill employees, meet compliance requirements, and support career growth. However, existing LMS platforms often act as content repositories rather than personalized learning assistants.

 

Pain Point

  • Employees are overwhelmed by generic training content and struggle to find relevant courses.
  • Managers lack visibility into skill gaps and training effectiveness.
  • Companies spend heavily on training programs without clear insights into ROI or business impact.
  • Current LMS solutions provide limited personalization and recommendations, leading to low engagement.

 

Challenge

Develop an AI-powered LMS that goes beyond course hosting, by:

  • Mapping employee skills, roles, and career paths to relevant training modules.
  • Using learning analytics to predict skill gaps and recommend personalized learning journeys.
  • Providing managers with team-level insights on training progress and skill readiness.
  • Enabling employees to learn flexibly, with adaptive learning paths based on performance.

 

Goal

Create a smart, data-driven LMS that improves employee engagement, learning outcomes, and workforce readiness while giving leadership clear visibility into training impact.

 

Outputs

  • Personalized learning recommendations for each employee.
  • Skill gap dashboards for managers and HR.
  • Learning progress analytics with completion, performance, and adoption rates.
  • Training ROI insights linked to productivity and career growth.

 

Impact

  • Employees gain relevant, career-aligned skills faster.
  • Managers can strategically deploy talent based on verified skills.
  • Organizations see higher training ROI and improved workforce agility.
  • Creates a culture of continuous learning, driving retention and innovation.
AH2025/PS05 | AI/ML

Context

Continuous employee learning is essential for companies to stay competitive in a fast-changing business environment. Organizations adopt Learning Management Systems (LMS) to upskill employees, meet compliance requirements, and support career growth. However, existing LMS platforms often act as content repositories rather than personalized learning assistants.

Pain Point

  • Employees are overwhelmed by generic training content and struggle to find relevant courses.
  • Managers lack visibility into skill gaps and training effectiveness.
  • Companies spend heavily on training programs without clear insights into ROI or business impact.
  • Current LMS solutions provide limited personalization and recommendations, leading to low engagement.

Challenge

Develop an AI-powered LMS that goes beyond course hosting, by:

  • Mapping employee skills, roles, and career paths to relevant training modules.
  • Using learning analytics to predict skill gaps and recommend personalized learning journeys.
  • Providing managers with team-level insights on training progress and skill readiness.
  • Enabling employees to learn flexibly, with adaptive learning paths based on performance.

Goal

Create a smart, data-driven LMS that improves employee engagement, learning outcomes, and workforce readiness while giving leadership clear visibility into training impact.

Outputs

  • Personalized learning recommendations for each employee.
  • Skill gap dashboards for managers and HR.
  • Learning progress analytics with completion, performance, and adoption rates.
  • Training ROI insights linked to productivity and career growth.

Impact

  • Employees gain relevant, career-aligned skills faster.
  • Managers can strategically deploy talent based on verified skills.
  • Organizations see higher training ROI and improved workforce agility.
  • Creates a culture of continuous learning, driving retention and innovation.
AH2025/PS04 | AI/ML

Context

Software teams struggle to diagnose system failures from massive log files. Manual analysis is slow, error-prone, and requires expert knowledge. Root cause extraction from unstructured, noisy logs. Use creative algorithms, LLM prompting strategies, or hybrid heuristics.

Pain Point

  • Manual log analysis is slow, error-prone, and requires deep expertise in both the system and its environment.
  • Critical issues can be missed or misdiagnosed, leading to longer downtimes and higher costs.
  • Existing monitoring tools often raise alerts without actionable insights, leaving developers to do the heavy lifting.

Challenge

Build an AI-powered log analytics assistant that can:

  • Ingest and parse unstructured application logs at scale.
  • Automatically flag potential defects or anomalies.
  • Summarize possible root causes in natural language.
  • Provide actionable insights that developers can use immediately.

Goal

Deliver a working prototype that:

  • Operates on sample log data.
  • Produces insights that are accurate, usable, and easy to interpret.
  • Bridges the gap between raw log data and developer-friendly diagnostics.

Outputs

  • Automated defect detection (flagging anomalies in logs).
  • Root cause summaries in natural language.
  • Actionable recommendations (e.g., suspected component failure, probable misconfiguration).
  • Visualization/dashboard (if possible) for quick triage.

Impact

  • Reduced time to diagnose failures, lowering downtime and maintenance costs.
  • Increased developer productivity, freeing engineers to focus on fixes rather than sifting logs.
  • Improved reliability of complex software systems.
  • Scalable approach that can be extended across industries (finance, automotive, telecom, healthcare).
AH2025/PS03 | AI/ML

Context

Drivers and passengers spend significant time in vehicles where comfort, safety, and accessibility directly affect satisfaction and well-being. Yet today’s in-car systems remain largely static and manual, requiring users to adjust climate, seats, infotainment, and navigation themselves. With increasing connectivity, AI offers the potential to transform cars into adaptive, intelligent companions.

Pain Point

  • Current in-car experiences are one-size-fits-all, failing to account for individual preferences or needs.
  • Manual adjustments while driving can be distracting and unsafe.
  • Accessibility gaps (e.g., for elderly passengers or those with hearing/visual impairments) remain unaddressed.

Challenge

Build a Generative AI-powered cockpit agent that dynamically personalizes the in-car experience based on contextual data such as:

  • Driver profile (age, preferences, past behaviour).
  • Calendar & journey type (work commute, leisure trip, urgent travel).
  • Mood (estimated from inputs like speech, facial cues, or self-reporting).
  • Accessibility needs (visual/hearing impairments, elderly passengers).

Goal

Deliver real-time, adaptive personalization of:

  • Comfort settings: AC, seat adjustments, lighting.
  • Infotainment: music, podcasts, news.
  • Navigation guidance: route optimization based on urgency, preferences, and accessibility.

Outputs

  • Dynamic in-car assistant that responds to context in real-time.
  • Personalized environment settings for comfort and safety.
  • Adaptive infotainment & navigation suggestions tailored to mood, journey type, and accessibility.

Impact

  • Safer driving experience with fewer distractions.
  • Higher passenger satisfaction through comfort and entertainment personalization.
  • Improved accessibility and inclusivity for diverse user needs.
  • New value proposition for automakers: cars as intelligent, personalized environments, not just vehicles.
AH2025/PS02 | AI/ML

Context

Automotive software development is highly complex, involving multiple tools (Jira, GitHub, MS Teams, Confluence), distributed teams, and strict compliance standards (ISO 26262, ASPICE). Project managers must continuously monitor tasks, track resources, and identify risks. However, the sheer volume of data across tools makes real-time visibility and decision-making difficult.

Pain Point

  • Project managers waste time manually consolidating data from Jira, GitHub, and communication platforms.
  • Resource allocation bottlenecks (overloaded developers, idle testers) often go unnoticed.
  • Risks (delays, defects, dependency issues) are only discovered late, impacting delivery timelines.
  • Lack of predictive insights leads to reactive, rather than proactive, project management.

Challenge

Build an AI-powered project management assistant that can:

  • Auto-generate project dashboards by integrating Jira, GitHub, and MS Teams data.
  • Provide real-time resource allocation insights (who is overloaded, who is free).
  • Predict risks and delays using historical patterns and live progress signals.
  • Deliver natural language summaries for managers and stakeholders.

Goal

Enable project managers to see the full picture instantly, automate reporting, and take data-driven decisions on resources and risks without manual effort.

Outputs

  • Automated project dashboards (progress, backlog, velocity, open PRs/issues).
  • Resource allocation map showing workload distribution across the team.
  • Risk prediction engine (e.g., “Module X likely delayed by 2 weeks due to dependency on Y”).
  • AI-generated summaries (daily/weekly status reports in plain language).

Impact

  • Reduced management overhead → fewer hours wasted on reporting.
  • Improved predictability → early identification of risks and delays.
  • Optimal resource utilization → balanced workloads across teams.
  • Better stakeholder communication → clear, automated updates.
  • Scalable for enterprises → can be deployed across multiple automotive software teams.
AH2025/PS01 | AI/ML

Context

In modern organizations, assembling the right project team is critical to success. Managers must balance skills, experience, cost, availability, and domain expertise, but decisions are often made using intuition or partial information. This leads to suboptimal teams, missed deadlines, or budget overruns.

Pain Point

  • Team formation today is time-consuming and heavily manual, requiring managers to cross-check spreadsheets, HR databases, and project needs.
  • Costs and expertise trade-offs are rarely quantified, making it hard to justify team composition to leadership or clients.
  • Traditional staffing tools focus on availability but fail to optimize across multi-dimensional constraints (skills, budget, past project fit, timeline).

Challenge

Build a Generative AI assistant that takes as input:

  • Employee database (skills, past projects, availability, cost)
  • Customer project requirements (tech stack, timeline, budget, domain)

Goal

Enable managers to form the best-fit, economically feasible project teams in minutes, rather than days, while providing transparency into why each recommendation was made.

Outputs

  • Optimal team composition: Recommended employees, with justification.
  • Economic feasibility analysis: Skill coverage vs cost vs timeline.
  • Alternative team recommendations: Trade-off scenarios (e.g., lower cost, faster delivery, more experienced).

Impact

  • Faster project staffing → quicker project kick-offs.
  • Higher client satisfaction due to right skills on the right project.
  • Lower staffing costs through data-driven optimization.
  • A scalable framework that can be extended for hackathons, consulting firms, or large enterprise project staffing.
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