Monthly Archives: January 2021

How Does Tessy Tool Automate the Integration Testing of Automotive Software?

Integration testing is the next level of test performed after unit testing. As per the V-Model, integration testing is executed against software architecture design. In this process, several software modules (units) are integrated as one module and tested together. And hence, the name integration testing. The interdependence of different software functions, the interfaces between them and time dependencies are some of the specifications that are verified during the process of integration testing.

Generally speaking, integration testing of automotive software is no different than testing of any regular software. The difference, however, lies in the tools that are deployed for integration testing of automotive software. In this blog, we will pick one such industry-recognized integration test tool that is well-known for its capability in automating the integration test process.

The tool that we intend to discuss in the blog is Tessy tool. Now, most of us identify Tessy tool as the quintessential unit testing tool mostly deployed for ISO 26262 compliant unit testing. However, there is a different side to Tessy as well. And we are here to explore that side and examine how Tessy tool helps the automotive engineers automate and speed up automotive software integration testing.

Integration Testing, the Tessy Way!

Tessy tool comes equipped with the capability to perform two types of testing- Unit Testing and Component Testing. In the Tessy environment, integration test is called as component testing as it treats a group of units to be tested, as one component. Tessy tool is designed to automate various processes of integration testing starting from requirements import to a comprehensive report generation.

Essentially, the component test feature of Tessy supports the engineers in creating scenarios where they can check the internal values of the component variables as well as the interfaces between different modules.

For example, a Battery evaluation module of an EV requires battery voltage data from a separate module to evaluate the remaining power in the battery. The engineers can create certain test scenarios to check whether the relevant functions are correctly called, and accurate data is transferred. Let’s learn about the steps in integration testing to understand the process better.

• Requirement Import from Requirement Management Tools: A component under test must provide the expected output for the test, to be marked successful. It is a way to ensure that the software component is exhibiting the functionalities as expected in the requirement document. And for Tessy to understand these requirements, it must first import them from the requirement management tools such as DOORS or Polarion.

  
  Tessy tool provides a one-click import to make the process easier and less time-consuming. The tool lets you check the requirement and edit it before it is committed to a test case. After the test cases are prepared, these requirements are linked to the respective test cases in order to ensure traceability.

• Creating Test Scenarios: We mentioned earlier in the blog that for verifying the interfaces between different modules, scenarios with test inputs are created. Tessy provides a scenario editor (SCE) function for this purpose. A scenario can be seen as a group of test objects and test cases that applies for a component under test. Instead of having individual test cases for each function, scenario provides a more consolidated and efficient way to perform integration testing. By creating one scenario for one component, multiple tests can be performed viz, checking global variables, calling component functions, setting input variables and so on. A scenario fulfills two main requirements:
  1. A scenario simulates the external application for testing the component. Both normal and abnormal behavior are simulated to check the error handling capabilities of the component.
  2. It also records the reaction of the component under the simulation.
• Test Interface Creation: The primary goal of integration testing using Tessy is to verify the interfaces between different modules. Hence, it is imperative that all these interfaces under test are enlisted in the tool. In this step, the interfaces of all non-static functions are summarized. As clear from the image, the functions are categorized under external and component functions and few other variables.
  

  
  External functions list the interfaces belonging to the underlying software that will be called by the component under test. Component function lists those interfaces that are callable from outside of the component. A sequence of all such function calls is what integration tests are mostly about.

• Work Task Configuration: Applications running under an RTOS (Real time operating system) usually have a timeframe for completing a function call. Hence, it becomes important to check whether a resulting call is executed after the stimulating call within the stipulated time frame. And for this sort of verification, a special handler function called the heartbeat function is called. This heartbeat function is called Work Task in Tessy testing environment. Simply stated, a work task tests the timely behavior of a component.

  
  In the work task configuration tab, the test engineers can drop the component functions and configure them as work tasks. All they need to do is to specify the start time and cycle time. Rest of the job is taken care of by Tessy tool.

• Test Case Design: Test case design is nothing but creating scenarios for testing. As multiple scenarios are required for stimulating a component and checking its behavior, they are created as and when required. Post the creation of these scenarios, they need to be configured too. Let’s understand what the required configurations are:
  1. Addition of Function calls: External and component functions are added to the time step.
  2. Test data configuration: Input value can be set, or output value can be checked for any time step of the scenario.
  3. Return value of functions: Parameter value is set for the functions added to the scenario. When Tessy calls a function, it always does so with the parameter value specified here. If there is no parameter value, an error will be displayed.
  4. Calling sequence of functions: Call sequence can be checked by specifying certain information for the expected function call within the scenario. This is a proven mechanism to check whether the behavior of a component is as expected or not.

  Once the scenarios are designed, they are executed. All one must do is to select the scenario and execute them with one click. After the execution is completed, a detailed report can be retrieved from the tool.

Value-Adds of Tessy Tool for Integration Testing of Automotive Software

• ISO 26262 Compliant Integration Testing: Tessy is one of the most sought-after ISO 26262 qualified unit and integration testing tool. It has a built-in template where you select the targeted ASIL value and Tessy takes care of the test methods as mentioned in the ISO 26262 standard document.
• Automation of Test Execution: Tessy is designed to be a test automation tool. Once, you import the requirements and create the scenarios, Tessy handles the execution of the test – traversing the code and providing complete coverage.
• Comprehensive Test Report: Test reports created by Tessy tool are quite comprehensive and detailed showing the linked requirements, pass and failed test cases, etc. There is also an option to edit the test report setting on the basis your preferred information. These test reports provide all the information (evidence) required for ISO 26262 certification or qualification.
• Requirement Traceability and Accuracy of Test: With Tessy, it is easy to achieve requirement traceability as it commits the requirements to respective test cases. It makes change management easier. Since it is an ISO 26262 qualified tool, the test results have been found to be very accurate and reliable.

Final Thoughts

Tessy has proven to be a very helpful tool in integration test automation for automotive software development. Being a ISO 26262 qualified tool makes it even more desirable for the engineers. Its reliability can also be gauged by the fact that many OEMs, Tier-1 suppliers and product engineering companies are deploying Tessy for integration testing, in addition to unit testing.

SOME/IP: Gateway to Future Vehicle Networking System

Service oriented communication is emerging as the new mode of communication between the automotive ECUs. The underlying communication protocol here is Ethernet.

In order to achieve dynamic communication, Signal-oriented architecture used in CAN or LIN was not sufficient. Modern applications such as ADAS, V2X and automated driving etc. required new software features to be added dynamically to the ECUs.

Service oriented communication provided a way to do so by defining the entire system as service interfaces. And SOME/IP is the middleware that makes it all possible. Too much information to process? Our video will make it simpler.

If the video made you curious, read our detailed SOME/IP blog to understand the intricacies of this protocol. We also have a webinar on SOME/IP where our expert explains the concept in great depth.

Artificial Intelligence for Automotive Applications – A Close Look at the Revolutionary Trends

Today, the automotive industry is at the cusp of a phenomenal transformation. Ambitious automakers have started incorporating advanced technologies in their products and operations to ensure that they stay a step ahead of competitors in the market.

The modern automobile is fortified with IoT technologies and applications:

• Sensors that gather valuable data about vehicle condition and driver behavior
• Complex machine learning (ML) algorithms that convert the collected data to insightful reports, and
• Usage of this data to segment customers and provide individualized offers

These are some of the most prevalent use cases of artificial intelligence for automotive applications today.

The relationship between automotive OEMs and niche software technology solution providers have only strengthened due to these advancements.

As a trusted technology partner for global automotive OEMs and Tier 1 Suppliers, Embitel has been developing transformative solutions for connected cars of the future. Our IoT team consisting of experts in artificial intelligence (AI), cloud solutions and embedded software have been combining business understanding with powerful tools and processes to deliver insights for future decision making.

In this article, we take a look at some of the distinct AI/ML trends in the automotive industry and the associated ideas/products we have conceived at our IoT Innovation Lab in Bengaluru.

AI Use Cases in the Automotive Industry

Implementation of artificial intelligence and data science has benefited not only the car manufacturers, but also parts/software suppliers, vehicle rental companies and other businesses related to the automotive domain.

The visionaries in the connected car and autonomous driving arena take advantage of data science and AI for developing disruptive technologies in the industry.

Listed below are three far-reaching automotive trends, powered by machine learning and data science.

1. Predictive Maintenance

   Predictive Maintenance is perhaps one of the finest examples of how data science can be harnessed for adding value to automotive businesses.

   • Manufacturing Analytics – Analytics has proven to be an extremely powerful tool in the manufacturing value chain. In order to realize the full potential of data science, it is important to analyze and collect data from various functions across the manufacturing life cycle.

     This indicates that an end-to-end analytics strategy that covers workforce analytics, asset/inventory management and operational planning is crucial for getting the complete picture for generating insights.

     The use of AI in vehicle manufacturing helps automakers reduce manufacturing costs and provides a safer and more efficient factory floor.

     Technologies such as Computer Vision enables easy identification of product anomalies. ML algorithms can be utilized for prototyping products and simulation.

     AI also helps in predicting malfunctions in automotive parts. This helps the manufacturing systems to work at prime performance levels, and save time and money in the long run.

     

      

     Apart from the manufacturing module, the use of data science can be extended across the entire business to benefit the following divisions:

     • Product research and development
     • Procurement
     • Supply chain
     • Distribution
     • Sales and customer service
     • Aftermarket
     • Marketing
   • Vehicle Maintenance Recommendations – Machine learning algorithms can be employed to provide evolving recommendations to drivers about vehicle maintenance. Based on the past occurrence of an event/issue, it is possible to predict when the next such issue may happen.

     So, for instance, the data collected by a vehicle’s sensors may indicate gradual overheating, friction or noise. These issues may also lead to the breakdown of a specific vehicle part in the future.

     • The machine learning algorithm records these events regularly and analyses the frequency of occurrence of these issues.
     • It also accurately predicts when the breakdown of the vehicle or part is expected, based on the findings.
     • The driver can, hence, take precautionary measures by getting the vehicle inspected and maintenance activities scheduled to avoid such a breakdown. This is a classic example of predictive maintenance in automobiles.

      

     

      

     Fleet management companies also utilize predictive maintenance to avoid unprecedented repairs and protect the ROI on each vehicle.

     Automotive OEMs are increasingly integrating predictive maintenance in their vehicles to improve customers’ compliance with vehicle maintenance schedules, enhance customer satisfaction and boost brand reputation.

     Over the years, our engineering teams have been working with global OEMs to develop software that effectively predicts the maintenance requirements of vehicle parts.

2. Driver Behavior Analytics

   AI and Deep Learning based automotive applications can offer a plethora of valuable in-car analytics. Cameras and IR sensors can detect the driver’s behavior accurately and provide warning signals to avoid accidents. Some of the key focus areas of driver behavior analytics include detection of:

   • Rash driving – IoT sensors can collect data on driver speeding, sharp turns, sudden braking, etc. This information can be analyzed continuously to form an impression of the driver’s behavior on the road.

     Project Genie, an intuitive mobile app developed by the engineers at Embitel, can evaluate the driver’s road performance and provide feedback at the completion of each journey. This helps the driver understand the issues with his driving and take corrective actions to stay safe.

   • Driver distraction – Machine learning based automotive applications can assist drivers by detecting driver distraction and providing early warning signs.

     For instance, a driver may also be engaged in several other activities while driving. This includes attending calls on a mobile phone, texting, reaching out to the back seat, talking to passengers, smoking, reaching for the infotainment system to play music, etc.

     These activities are generally classified as driver distraction activities and they usually take the driver’s attention off the roads.

     

      

     DriveSafe, a real-time driver distraction detection app incubated at the Embitel Innovation Lab, can analyze driver actions and classify these activities as “focused” or “distracted”. The driver is then notified of distracted driving through audio and SMS alerts, so that he/she can bring back his/her focus on the road.

   • Driver drowsiness – Machine learning based automotive apps enable the detection of eye openness and head position of a driver. The app subsequently sends a notification to alert the driver, if he/she is found to be drowsy.

Driver behavior analytics in the insurance industry:

The insurance industry can leverage driver behavior analytics to determine car insurance premiums for customers.

Insightful risk profiles are created for each driver based on his/her performance on the road, issues in personal life, health complications, and a myriad of other such factors that could affect their driving. This information is the basis for determining the premium.

The process of filing insurance claims at the time of an accident can also be streamlined through the use of AI.

3. Analyzing Road Conditions

   AI-powered automotive applications can detect road conditions in real-time so that drivers can be updated of construction work, accidents, speed limits and road closures before they start the journey.

   The AI/ML engineers at Embitel have conceived an IoT-based smartphone app to analyze road conditions and provide appropriate navigation assistance to drivers based on these conditions. This app determines the most optimum path for the journey based on potholes, humps, and road closures. The driver is also warned of the approaching hump/pothole, around 100 meters before he/she reaches it.

   In cities where there is frequent traffic congestion and road construction work, this information is very valuable for commuters.

   

    

   If a person is driving in a new city, he/she would have to completely depend on a web mapping service for best route suggestion. He/she would not know about the untarred roads that may be part of the optimized path suggested by the app. The smartphone app developed by our engineers can combine road condition info and traffic details to provide the best path suggestion.

Conclusion

The automotive market is seeing increased competition, cost pressure and volatility. Even small interventions can help auto OEMs make great strides in increasing their market share. Since data science is emerging as a game-changer in the automotive industry, the opportunities offered by it are plenty.

The perfect time to adopt artificial intelligence for automotive applications is now. If you have an idea for an exciting AI/ML based application, reach out to us. We can help you in transforming your vision to reality!

How Fleet Management is Being Redefined by Smartphones Apps and Telematics Devices

Businesses today are increasingly adopting technology to improve the availability and efficiency of assets and personnel. Fleet management is one such sector where digitization has brought about a significant transformation.

For a long time, companies have been using fleet management software that continuously monitor fleet operations. These systems also provide tangible inputs for improving business process efficiency.

However, the fleet management systems that are based on telematics and mobile data have redefined the status quo in the industry.

In this blog, we explore how telematics and smartphones are the future of fleet management. But first, let us understand the basics.

What is the Purpose of Fleet Management?

Fleet management is the process of continuously monitoring a fleet and utilizing asset information to reduce costs, improve efficiency and ensure compliance.

Fleet management involves tasks such as recording vehicle specifications and servicing schedules, maintaining history of drivers and vehicles, and collecting compliance reports. Apart from commercial vehicles, private vehicles that are used for work (grey fleet) are also usually included in fleet management.

It is evident that managing a group of commercial vehicles can be a very challenging task.

So, companies these days employ fleet management software for coordinating these activities. A basic version of a fleet tracking software would mitigate the following primary challenges of a fleet manager:

• Accurately keeping track of the driver and vehicle history
• Scheduling vehicle servicing
• Maintaining vehicle parts history
• Acquiring vehicles for the fleet
• Tracking fuel bills and repair invoices
• Ensuring health and safety of the drivers
• Monitoring compliance reports and regulatory documents (tax, license, insurance, etc.)

When a company incorporates a system that efficiently manages the fleet, it is able to keep operating costs under control and improve customer experience.

A fleet management solution generally has comprehensive reporting modules and can send emails and alerts. Integration of this software with third-party tools are also common.

How Telematics and IoT-Based Mobile Apps Enhance a Fleet Management System

Telematics is the use of telecommunications and informatics on moving vehicles.

Fleet management companies are increasingly integrating telematics in their fleet tracking software. They are also harnessing the power of mobile data, wireless communication and IoT-enabled vehicle control solutions for fleet management.

And the interesting part is that all these technologies have now begun to converge to provide a massive amount of vehicle and driver information to the fleet manager.

Incorporating an IoT-based telematics platform in a fleet management system gives you valuable data that is one step ahead.

For instance, a telematics device can provide details about the nearest vehicle to service a request after analyzing whether the vehicle has the parts to get the job done.

Another example is the system’s ability to monitor a vehicle’s engine hours and then report how the vehicle maintenance cycle is affected by the engine hours.

Businesses are utilizing the hardware of mobile phones (and the built-in GPS) to perform a bunch of tasks that were previously done by installed GPS systems in vehicles. Such specially designed mobile apps for fleet management, when used in parallel with telematics devices, can provide immense benefits to fleet management companies.

Benefits of Mobile Apps in Fleet Management

App installed in drivers’ smartphone and the telematics device installed in the vehicle together make it easy for fleet management companies to detect whether the driver and the vehicle are together.

How fleet managers benefit from these apps:

• Fleet management mobile apps help fleet managers in all scheduling and dispatching activities. This ensures better coordination with the field personnel.
• Fleet managers also receive a lot of critical fleet-related data from these apps such as position of vehicles, engine hours, speed, idle time of engines, driver speeding or harsh turns, on-site time taken, etc. This information can be exported to Pdf, Excel or CSV files. These apps can also be integrated with third-party software for additional business insights.
• These apps provide information on unauthorized usage of vehicles or fuel cards.
• Fleet management apps also streamline the communication between enterprises and their delivery partners, who may be other small/medium businesses. The availability of partners and inventories are assessed in real-time to ease the burden of managing the extended workforce.
• Fleet managers receive timely alerts when there are accidents or other such unprecedented events. They can then make the necessary arrangements for nearby vehicles to respond to the casualty and to schedule alternate delivery vehicles to take up the job.
• A mobile app-based fleet management solution ensures all historical data is consolidated in one place. This helps in avoiding human errors that may creep in during manual data consolidation processes.
• Such apps help in assessing the condition of the vehicles and scheduling maintenance checkup on time. This improves the longevity of the fleet and reduces downtime.
• Fleet management apps help in optimizing fuel as they provide the shortest routes and speed for each vehicle. Hence, one of the main concerns of a fleet manager is mitigated.

How drivers and customers benefit from these apps:

• Usage of smartphone apps has inculcated a sense of discipline in drivers.
• Fleet management mobile apps help in ensuring the safety of drivers, by tracking rash driving.
• These apps can save drivers a lot of hassle, as they are routed to servicing locations in the most efficient manner.
• Drivers can also access customer history on the smartphone apps to deliver improved customer experience each time.
• Drivers just need to carry their mobile phones during their working hours. Legal paperwork, cargo-related documents and all other such critical documentation will be safely stored in the app.

Conclusion

We now know that the installation of a fleet management system (integrated with telematics and an IoT-enabled smartphone app) helps in streamlining the management of a fleet like none other.

While there may be an initial investment cost to go digital, it will definitely pay off well in the future, as it makes the entire process more efficient and profitable.