Suraj Jadhav

The automotive industry is undergoing a rapid technological transformation, shifting from traditional mechanics to electrification and autonomous driving. Cars have evolved into complex, sustainable systems that are secure, digital, connected, and entertaining for passengers. The industry’s move towards a software-defined vehicle requires automotive software developers to adapt their methods. 

Quality management is crucial in this digital infrastructure, demanding alignment with growing demands while maintaining consumer standards. Manual testing is impractical due to the complexity of software development, making it expensive and time-consuming. The slow waterfall method is no longer suitable for the frequent software updates and daily production releases required today. 

Testing advanced applications in the automotive industry presents unique challenges, requiring tests on physical vehicles or complex simulations. Quality assurance challenges arise from the design and implementation of end-to-end tests, the need to test a higher number of combinations, and challenges in functional testing on vehicles in test centers to ensure acceptable KPIs. 

 Consider the intricacy involved in testing software tasked with analyzing input from numerous Electronic Control Units (ECUs). This software is designed to execute remote commands triggered through a mobile application held by the end user. Additionally, it is responsible for continuously monitoring the health and movement of the vehicle while in operation on the road, promptly generating diagnostic alerts when necessary. 


Driving the Future of Cars: How Quality Intelligence (or AI) Impacts the Automotive Industry

Role of Quality Intelligence in the automotive industry.

 Challenges with Infotainment Testing 

Automotive equipment manufacturers continue to face the challenge of providing in-vehicle infotainment (IVI) systems that delight their customers.  Customer demands for intuitive, powerful, and high-quality IVI systems increase, compelling manufacturers to add more features and content. The pressure to reduce testing cycle time from 4-6 weeks to 1 week for new product features adds complexity. Another major challenge in Infotainment testing lies in achieving comprehensive test coverage to guarantee system performance under diverse conditions and various combinations as specified. Manually testing all these scenarios for every change deployment is nearly impossible, necessitating the need for a robust automation solution. 

Our unique solution to address the current challenges 

The philosophy of DevOps, with continuous integration and continuous delivery (CI/CD), is the need of the hour, with regular/automatic liquid software Over the Air (OTA) updates, leveraging 5G networks and advances in cloud computing. 

At QualityKiosk Technologies, we see quality not as isolated pockets but as an integral component of product development across the entire quality lifecycle. Our solution encompasses diverse testing methodologies to ensure the robustness of automotive software, covering product simulation, production, and live field performance of live vehicles.  

QualityKiosk’s AI-driven testing approach 

Our 360-degree lean testing covers desktop, sub-system, system benches, and vehicle testing, ensuring a thorough evaluation. We strategically employ various testing contexts—Model-in-the-Loop, Software-in-the-Loop, Hardware-in-the-Loop, and real-world testing—customized for Tier 2, Tier 1, and OEMs. 

Automation is at the core of our DevOps methodology, driving efficiency in processes, enhancing management, and promoting transparent workflows. This integrated approach replaces outdated practices in software development and deployment, significantly reducing time to market. 

Our performance testing evaluates software endurance and its capacity to handle data under extreme workloads. We undertake live vehicle testing in real-world conditions to assess functionality, safety, and resilience in practical settings. Our Observability solutions play a critical role in collecting data on customer behavior, correlating telemetry data, detecting anomalies, and validating software quality and performance.  

Use of AI in Automobile Testing 

Our integration of AI in automobile testing is transformative in the automotive industry, driven by machine learning and automation. This shift fosters the production of electric cars and aligns with a broader movement towards eco-friendly practices. The current trends in the automotive sector highlight several key benefits: 

  1. Enhanced safety measures  
  2. Reduced vehicle issues through predictive maintenance 
  3. Elevated driving experience for users 
  4. Autonomous driving capabilities 

As AI gains traction, testing AI/ML models with big data sets becomes imperative. Automotive data encompasses consumer behavior, preferences, driving patterns, locations, and more. Without rigorous quality testing, big data may fall short in providing valuable insights for decision-making. Implementing Big Data testing, thus, becomes crucial for precise data processing and assessment. 

Our Big Data and analytics testing is designed to ensure 100% validation of all data. It examines and validates the functionality and performance of big data applications. Our end-to-end testing approach addresses big data testing requirements, including metrics, tools, and test data.  

We use a broad range of AI led testing tools including Katalon, Tricentis, Dynatrace and many others to build an AI-augmented quality management platform. The result is complete data validation, a reduction in overall quality costs, accelerated time-to-market and predictive customer experience. 

In conclusion, our holistic AI-augmented QA approach for cloud-connected cars offers numerous benefits. These include reduced ownership costs facilitated by open-source tools, comprehensive test coverage, and a 40% faster time to market. Our modular, scalable frameworks minimize maintenance, while pre-built templates and codeless automation streamline the testing process.  

To explore innovative and efficient testing methodologies in the automotive industry, reach out to us at [email protected].

About the Author

Suraj Jadhav is a BU Head – Auto & Consumer Practice at QualityKiosk Technologies. He is an expert on RPA and Test Automation solutions and CI/CD best practices, and has worked with global Automotive, Healthcare, Telecom, and eCommerce clients. His primary focus areas include ERP, Enterprise Datawarehouse, Cloud Adoption & Migration, Analytics & Business Intelligence, Mobile & Web Apps, AI & ML, RPA, Chatbots, and other evolving technologies.  

Suraj has a Post Graduate Diploma in e-Business from the Welingkar Institute of Management Development and Research (WeSchool), Mumbai.