In this rapidly evolving technology, embedded systems have become the backbone of the modern world. From the subtle intelligence of smart home devices to the critical operations within healthcare and automotive industries, embedded systems are the quiet architects of our technological landscape. The seamless and error-free operation of these intricate systems is ensured by the meticulous application of Quality Assurance (QA). QA emerges as a paramount force in the development of embedded systems. In this article, we dissect the significance of QA in embedded systems, where precision and reliability are not just desired but mandatory. Join us as we navigate through various aspects of QA, exploring how QA shapes the robust functionality of embedded systems.

Embedded systems are specialized computing systems that are designed to perform dedicated functions or tasks within a larger system. Unlike general-purpose computers, embedded systems are tightly integrated into the devices they operate, making them essential components in various industries. They are the brains behind smart home devices, medical equipment, automotive systems, industrial machinery, and more. These systems ensure seamless and efficient operation without drawing much attention to themselves.

Significance of Quality Assurance in Embedded Systems

In embedded systems, QA involves a systematic process of ensuring that the developed systems meet specified requirements and operate flawlessly in their intended environments. The importance of QA for embedded systems can be emphasized by the following factors:

Reliability: Embedded systems often perform critical functions. Whether it’s a pacemaker regulating a patient’s heartbeat or the control system of an autonomous vehicle, reliability is non-negotiable. QA ensures that these systems operate with a high level of dependability and consistency. Some of the key test types in reliability testing

• Feature Testing
• Regression Testing
• Load Testing

Safety: Many embedded systems are deployed in environments where safety is paramount, such as in medical devices or automotive control systems. QA processes are designed to identify and reduce potential risks and hazards, ensuring that these systems comply with the safety standards. To achieve a safe state in an embedded system, the Hazard Analysis and Risk Assessment (HARA) method is applied to embedded systems when it comes to automotive and the healthcare sector, an additional layer of consideration is crucial in medical devices and systems, compliance with data security and patient privacy standards is of utmost importance. The Health Insurance Portability and Accountability Act (HIPAA) method is applied to ensure that healthcare information is handled securely and confidentially

Compliance: Embedded systems must stick to industry specific regulations and standards. QA processes help verify that the developed systems comply with these regulations, whether they relate to healthcare, automotive safety, smart consumer electronics, or any other sector. Embedded systems undergo various compliance tests depending on the product nature, including regulatory, industry standards, and security compliance tests

Performance: The performance of embedded systems is critical, especially when dealing with real-time applications. QA includes performance testing to ensure that these systems meet response time requirements and can handle the expected workload. Following are the types of performance testing

• Load testing
• Stress testing
• Scalability testing
• Throughput testing

Evolution of QA in Embedded Systems

The technological landscape is dynamic, and embedded systems continue to evolve rapidly. Consequently, QA practices must also adapt to keep pace with these changes. Some key aspects of the evolution of QA in embedded systems include

Increased complexity: As embedded systems become more complex, with advanced features and connectivity options, QA processes need to address the growing complexity. This involves comprehensive testing methodologies and the incorporation of innovative testing tools

Agile development practices: The adoption of agile methodologies in software development has influenced QA practices in embedded systems. This flexibility allows for more iterative and collaborative development, enabling faster adaptation to change requirements and reducing time-to-market

Security concerns: With the increasing connectivity of embedded systems, security has become a paramount concern. QA processes now include rigorous security testing to identify and address vulnerabilities, protecting embedded systems from potential cyber threats

Integration testing: Given the interconnected nature of modern embedded systems, integration testing has gained significance. QA teams focus on testing how different components and subsystems interact to ensure seamless operation

Automated Testing in Embedded Systems
As embedded systems fall in complexity, traditional testing methods fall short of providing the speed and accuracy required for efficient development. This is where test automation steps in. Automated testing in embedded systems streamlines the verification process, significantly reducing time-to-market and enhancing overall efficiency. Also, incorporating machine learning algorithms to enhance and modify testing procedures over time, machine learning testing is an important aspect of automated testing. This helps to identify possible problems before they become more serious and increases efficiency

Testing approaches for Embedded systems

Testing Approaches for Embedded Systems
The foundation of quality control for embedded systems is device and embedded testing. This entails an in-depth assessment of embedded devices to make sure they meet safety and compliance requirements and operate as intended. Embedded systems demand various testing approaches to cover diverse functionalities and applications.

• Functional testing is used to make sure embedded systems accurately carry out their assigned tasks. With this method, every function is carefully inspected to ensure that it complies with the requirements of the system
• Performance testing examines the behavior of an embedded system in different scenarios. This is essential for applications like industrial machinery or automotive control systems where responsiveness in real-time is critical
• Safety and compliance testing is essential, especially in industries with strict regulations. Compliance with standards like ISO 26262 in automotive or MISRA-C in software development is non-negotiable to guarantee safety and reliability

Leveraging machine learning in testing (ML testing)

Machine Learning (ML) is becoming more and more popular as a means of optimizing and automating testing procedures for embedded systems. AIML algorithms are used in test automation. Test time and effort are greatly reduced with ML-driven test automation. It can create and run test cases, find trends in test data, and even forecast possible problems by using past data. ML algorithms are capable of identifying anomalies and departures from typical system behavior. This is particularly helpful in locating minor problems that conventional testing might ignore.

As technology advances, so does the landscape of embedded systems. The future of Quality Assurance in embedded systems holds exciting prospects, with a continued emphasis on automation, machine learning, and agile testing methodologies.

In conclusion, the role of QA in the development of embedded systems is indispensable. It not only guarantees the reliability and safety of these systems but also evolves alongside technological advancements to address new challenges and opportunities in the ever-changing landscape of embedded technology.

Softnautics, a MosChip Company provides Quality Engineering Services for embedded software, device, product, and end-to-end solution testing. This helps businesses create high-quality embedded solutions that enable them to compete successfully in the market. Our comprehensive QE services include embedded and product testing, machine learning applications and platforms testing, dataset and feature validation, model validation, performance benchmarking, DevOps, test automation, and compliance testing.

Read our success stories related to Quality Engineering services to know more about our expertise in this domain.

Contact us at business@softnautics.com for any queries related to your solution design and testing or for consultancy.
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Machine learning (ML) models have become increasingly popular in many kinds of industries due to their ability to make accurate and data-driven predictions. However, developing an ML model is not a one-time process. It requires continuous improvement to ensure reliable and accurate predictions. This is where ML testing plays a critical role as we are seeing massive growth in the global artificial intelligence and machine learning market. The worldwide AIML market was valued at approximately $19.20 billion in 2022 and is anticipated to expand from $26.03 billion in 2023 to an estimated $225.91 billion by the year 2030 with a Compound Annual Growth Rate (CAGR) of 36.2% stated by Fortune Business Insights. In this article, we will explore the importance of ML testing, the benefits it provides, the various types of tests that can be conducted, and the tools and frameworks available to streamline the testing process.

What is Machine Learning (ML) testing, and why it is important?

The process of evaluating and assessing the performance of Machine Learning (ML) models, which is responsible for accuracy and reliability, is known as Machine learning (ML) testing. ML models are algorithms designed to make independent decisions based on patterns in data. Testing ML models is essential to ensure that they function as intended and produce dependable results when deployed in real-world applications. Testing of ML models involves various types of assessments and evaluations to verify the quality and effectiveness of these models. These assessments aim to identify and mitigate issues, errors, or biases in the models, ensuring that they meet their intended objectives.

Machine learning systems operate in a data-driven programming domain where their behaviour depends on the data used for training and testing. This unique characteristic underscores the importance of ML testing. ML models are expected to make independent decisions, and for these decisions to be valid, rigorous testing is essential. Good ML testing strategies aim to reveal any potential issues related to design, model selection, and programming to ensure reliable functioning.

How to Test ML Models?

Testing machine learning (ML) models is a critical step in the machine learning solution development and deployment of robust and dependable ML model. To understand the process of ML testing, let’s break down the key components of both offline and online testing.

Offline Testing

Offline testing is an essential phase that occurs during the machine learning model development and training of an ML model. It ensures that the model is performing as expected before it is deployed into a real-world environment. Here’s a step-by-step breakdown of the offline testing process.

The process of testing machine learning models involves several critical stages. It commences with requirement gathering, where the scope and objectives of the testing procedure are defined, ensuring a clear understanding of the ML system’s specific needs. Test data preparation follows, where test inputs are prepared. These inputs can either be samples extracted from the original training dataset or synthetic data generated to simulate real-world scenarios.

AIML systems are designed to answer questions without pre-existing answers. Test oracles are methods used to determine if any deviations in the ML system’s behaviour are problematic. Common techniques like model evaluation and cross-referencing are employed in this step to compare model predictions with expected outcomes. Subsequently, test execution takes place on a subset of data, with a vigilant eye on test oracle violations. Any identified issues are reported and subjected to resolution, often validated using regression tests. Finally, after successfully navigating these offline testing cycles, if no bug is identified the offline testing process ends. The ML model is then ready for deployment.

Online Testing

Online testing occurs once the ML system is deployed and exposed to new data and user behaviour in real-time. It aims to ensure that the model continues to perform accurately and effectively in a dynamic environment. Here are the key components of online testing.

• Runtime monitoring
• User response monitoring
• A/B testing
• Multi-Armed Bandit

Testing tools and frameworks

Several tools and frameworks are available to simplify and automate ML model testing. These tools provide a range of functionalities to support different aspects of testing

ML testing tools and frameworks

• Deepchecks
  It is an open-source library designed to evaluate and validate deep learning models. It offers tools for debugging, and monitoring data quality, ensuring robust and reliable deep learning solutions.
• Drifter-ML
  Drifter-ML is a ML model testing tool specifically written for the scikit-learn library focused on data drift detection and management in machine learning models. It empowers you to monitor and address shifts in data distribution over time, essential for maintaining model performance.
• Kolena.io
  Kolena.io is a python-based framework for ML testing. It focuses on data validation that ensure the integrity and consistency of data. It allows to set and enforce data quality expectations, ensuring reliable input for machine learning models.
• Robust Intelligence
  Robust Intelligence is a suite of tools and libraries for model validation and auditing in machine learning. It provides capabilities to assess bias and ensure model reliability, contributing to the development of ethical and robust AI solutions.

ML model testing is a crucial step in the development process to ensure the reliability, accuracy, and fairness of predictions. By conducting various types of tests, developers can optimize ML models, detect, and prevent errors and biases, and improve their robustness and generalization capabilities – enabling the models to perform well on new, unseen data beyond their training set. With the availability of testing tools and frameworks, the testing process can be streamlined and automated, improving efficiency and effectiveness. Implementing robust testing practices is essential for the successful deployment and operation of ML models, contributing to better decision-making and improved outcomes in diverse industries.

Softnautics, a MosChip Company provides Quality Engineering Services for embedded software, device, product, and end-to-end solution testing. This helps businesses create high-quality solutions that enable them to compete successfully in the market. Our comprehensive QE services include machine learning applications and platforms testing, dataset and feature validation, model validation and performance benchmarking, embedded and product testing, DevOps, test automation, and compliance testing.

Read our success stories related to Quality Engineering services to know more about our expertise in this domain.

Contact us at business@softnautics.com for any queries related to your solution design and testing or for consultancy.

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Nowadays, Containers have become a leading CICD deployment technique. By employing appropriate connections with Source Code Management systems (SCM) like GIT, Jenkins is able to start a build procedure each time a developer contributes his code. This method makes all environments accessible to new Docker container images that are generated. Using these images, it allows for quicker application development, sharing, and deployment by groups. The Global CICD tool market is expected to see significant compound annual growth rate (CAGR) of 57.38% from 2023 to 2029, according to the report published by market intelligence data research.

Docker containers help developers in creating and conducting tests for their code in any circumstance to find flaws early in the life cycle of an application. It speeds up the process, reduces build time, and enable engineers to perform tests concurrently. Also, it can be integrated with tools like Jenkins and SCM platforms, e.g., GitHub. Developers upload their code to GitHub, test it using Jenkins, and then create an image utilizing that code. To address inconsistencies between various environment types, this image can be added to the Docker registry.

QA automation faces an issue when configuring Jenkins to execute automated testing within Docker containers and retrieve the results. The best approach for automating the testing procedure in CI/CD will be explored in this article ahead.

Continuous Integration (CI)

Each commit a developer makes to a code repository is automatically verified using a process called continuous integration. In most cases, validating the code entails constructing and testing it. Keep in mind that tests must be completed quickly. That is due to the developer’s need for prompt feedback on his changes. As a result, CI typically includes fake unit and/or integration tests.

Continuous Delivery (CD)

Continuous Delivery is a routine method for automatically releasing validated build artefacts.
After the code is built, integrated, tested, and passed, it is now appropriate to make build artefacts available. Naturally, a release must undergo testing before being deemed stable. As a result, we want to do release acceptance tests. Human and automated acceptance testing is available.
There are numerous considerations to remember:

• Depending on the application, a MySQL database may be required
• A method for determining whether automated tests are executed within a Success/Fail test cycle is required. Further stats may be needed
• There must be a complete shutdown and removal of all containers built during a test run

Why to use containers?

Containers allow one to easily deploy code on multiple servers without purchasing additional hardware. Instead of purchasing two servers, you can deploy the code on a single server from a container. This reduces costs and makes scaling easier. Therefore, if a code only needs 1GB RAM but the server has 32GB, you waste the remaining 31GB of hardware. The idea of hardware virtualization was developed to prevent wastage.

However, there is still another waste: the code might not always use all RAM or CPU. Only 30% of the system’s resources will be used more than 90% of the time. Containerization was introduced to help with this as well. Containerization leverages underutilised system resources and shares hardware resources.

Jenkins automates the CI/CD process. The question arises as to where to do the continuous delivery? So, this problem of where to do delivery is solved by “container”, “server” or virtual machine.

Understanding the Container lifecycle

The base image is present in the container registry. An appropriate base image selection is the first step in docker life cycle. The required base image is selected from the container registry. Docker-file is written by a user to create a new image for the application using the base image. Add the required packages and application dependencies layered on the image. An account or own container registry is created by user for storing built images.

A new fresh image is created using docker-file and pushed to the container registry. With newly built image, run a container to test the application. Once the task is complete, stop and delete the container.

If any modifications are made in application inside the running container and the process demands all the modifications, then another image of the currently operating container is to be created. A new image is created with a commit command. This image is pushed to the container registry for further processing.

Container lifecycle

How the process works?

The initial step is to run a Docker container that will execute automated tests and deliver test results and an overall code completeness report.

Use the “dockerize” function to wait for an application to get started, if your tests rely on another service, such as a database, to be accessible.

Integrating dependencies

An application can be made up of many containers that run various services (e.g., application, related database). Manually starting and managing containers can be time-consuming, so docker developed a useful tool to help speed up the process: Docker Compose.

Performing the automated tests

It’s time to start a Jenkins project! Use the “Freestyle project” type. Add a build step labelled “Execute shell” after that.

For accurately referencing containers, it is essential to mention the project name to differentiate test containers from others on the same host.

We can monitor the log file results from container hosting tests to determine when tests have truly finished running.
Diagram

Automated testing with container in continuous delivery

The following are the benefits of using containers for automation and QA

• Containerization improves SDLC effectiveness and speed, which benefits businesses
• Because deployments are carried out in containers, it operates faster than Jenkins or any other tool. This makes it possible to deploy the same containers to various locations at once, which is incredibly quick as it leverages the Linux kernel
• Due to the isolation provided by each container, maintaining tests and environments is simpler. The other containers won’t be impacted by a mistake in one of the containers
• There is no requirement to pre-allocate RAM to containers because containers provide a predictable and repeatable testing environment. The host operating system or the device that hosts the Docker image runs the containers
• The ability to keep the majority of application dependencies and configuration information inside a container reduces environmental variables. Continuous integration ensures application consistency during testing and production as it is done in parallel with testing
• Running new applications or automation suites in new containers is simple
• If a problem arises, testers can send photographs instead of bug reports to developers (in this case, an image of the programme, possibly even at the time a test failed). To address the issue, multiple developers can work simultaneously on debugging it. This is made possible by creating ‘n’ number of containers using the same image, allowing efficient collaboration among the development team. The duplication process can be easily performed
• Early reports of glitches and problematic alterations
• Automating repetitive manual test case execution
• QA engineers can conduct more exploratory testing

Conclusion:

The adoption of containerized testing and the integration of Jenkins into the CI/CD process has revolutionized QA automation testing. By leveraging containers, developers can create and execute tests in any environment, leading to early flaw detection and faster application development.

Softnautics provides Quality Engineering Services for embedded software, device, product, and solution testing. This helps businesses create high-quality solutions that enable them to compete successfully in the market. Our comprehensive QE services include compliance, machine learning applications and platforms testing, embedded and product testing, DevOps and test automation. Additionally, we enable our solutions to adhere to numerous industry standards, including FuSa ISO 26262, MISRA C, AUTOSAR, and others.

Read our success stories related to Quality Engineering services to know more about our expertise in the domain.

Contact us at business@softnautics.com for any queries related to your solution or for consultancy.

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In the world of extreme technology advancements, each product, system or platform is completely different from another. Hence, the testing needs too vary drastically.
To test a web application, the system needs are limited to just a web browser client and Selenium for test automation. Whereas an IoT product needs end device, cloud, web app, API as well as Mobile application (Android & iOS) to be tested thoroughly end to end.
Similarly, there can be a completely different type of product or system. So, one size fits all is never an option when it comes to testing. To test different types of systems, one needs to be extremely versatile as well as flexible. Same goes for test automation. Each system needs a completely different type of automation framework to accommodate the complexity, scalability and different components of the product under test. If there is a single test framework which is versatile and flexible enough to achieve all of the above, it is Pytest.

Pytest provides numerous advantages that can assist companies in optimizing their software testing procedures and enhancing the overall quality of their products. One of the key benefits of Pytest is its seamless integration with continuous integration and delivery (CI/CD) pipelines, which allows for automated testing of code modifications in real time. This results in improved efficiency and faster bug resolution time, ensuring that the product meets the desired level of quality and reliability. Pytest offers comprehensive reporting and analysis functionalities, which can help developers and testers promptly identify and resolve issues.

Pytest offers a huge number of Features in form of Functions, Markers, Hooks, Objects, Configurations and a lot more to make the framework development extremely flexible and give freedom to the test framework architect to implement the desired structure, flow and outcome which best fits product requirement.

But to make use of the above, and what to use when, is a major challenge. This blog will explain the features of different categories which are used to achieve complex automation.

Hooks:
Hooks are a key part of Pytest’s plugin system and are used by plugins and by Pytest itself to extend the functionality of Pytest. Hooks allow plugins to register custom code to be run at specific points during the execution of Pytest, such as before and after tests are run, or when exceptions are raised. They provide a flexible way to customize the behavior of Pytest and to extend its functionality. The categories of hooks go by the stage at which they are used.
Some widely used hooks in each category summarized below:

BootStrapping: At the very beginning and end of test run.

• pytest_load_initial_conftests – Load initial plugins and modules that are needed to configure and setup the test run ahead of the command-line argument parsing.

Initialization: After boot strapping to initialize the resources needed for test run.

• pytest_addoption – add additional command line options to the test runner.
• pytest_configure – Perform additional setup/configuration after command line options are parsed.
• pytest_sessionstart – Perform setup steps after all configurations are completed and before the test session starts.
• pytest_sessionfinish – Teardown steps or generate reports after all tests are run.

Collection: During test collection process, used to create custom test suites and collect test items.

• • pytest_collectstart – Perform steps/action before the collection starts.
  • pytest_ignore_collect – Ignore the collection for specified path.
  • pytest_generate_tests – Generate multiple parameterized calls for a test based on parameter.
  • pytest_collection_modifyitems – Modify the collected test items list as needed. Filter, Re-order according to markers, or other criteria.

Runtest: Control and customize individual test run

• pytest_runtest_setup/teardown – Setup/teardown for a test run.
• pytest_runtest_call – Modify the arguments or customize the test when a test is called.
• pytest_runtest_logreport – Access the test result and modify/format before it is logged.

Reporting: Report status of test run and customize reporting of test results.

• pytest_report_header – Add additional information to the test report.
• pytest_terminal_summary – Modify/Add details to terminal summary of the test results.

Debugging/Interaction: Interact with the test run that is in progress and debug issues.

• pytest_keyboard_interrupt – Perform some action on keyboard interrupt.
• pytest_exception_interact – Called when an exception is raised which can be interactively be handled.
• pytest_enter/leave_pdb – Action to perform when python debugger enters/leaves interactive mode.

Functions:
As the name suggests, these are independent pytest functions to perform a specific operation/task.
Pytest functions are directly called like a regular python function call. i.e. pytest.()
There are a number of Pytest functions available to perform different operations.
Below listed are widely used Pytest functions and their uses.

approx: assert that two numbers are equal to each other with some tolerance.
Example:
assert 2.2 == pytest.approx(2.3)
# fails, because 2.2 is not similar to 2.3
assert 2.2 == pytest.approx(2.3, 0.1)
#pass with 0.1 tolerance

skip: skip an executing test with given message reason. Used to skip on encountering a certain condition.
Example:
import pytest
if condition_is_encountered:
pytest.skip(“Skip integration test”)

fail: Explicitly fail an executing test with given message. Usually used to explicitly fail test while handling an exception.
Example:
import pytest
a = [1, 2, 3]
try:
invalid_index = a[3]
except Exception as e:
pytest.fail(f”Failing test due to exception: {e}”)

xfail: Explicitly fail a test. Used for known bugs. Alternatively preferable to use pytest.mark.xfail.
Example:
pytest.xfail(“This is an existing bug”)

skip: Skip the test with a given message.
Example:
pytest.skip(“Required environment variables were not set, integration tests
will be skipped”)

raises: Validate the expected exception is raised by a particular block of code under the context manager.

Example:
with pytest.raises(ZeroDivisionError):
1 / 0

Importorskip: Import a module or skip the test if module import fails.
Example:
pytest.importorskip(‘graphviz’)

Marks:
Marks can be used to apply meta data to test functions (but not fixtures), which can then be accessed by fixtures or plugins. They are very commonly used for test parameterization, test filtering, skipping and adding other metadata. Marks are used as decorators.

@pytest.mark.parametrize: Parametrization of arguments for a test function. The collection will generate multiple instances of the same test function as the number of parameters.
Example:
@pytest.mark.parametrize(“test_input,expected”, [(“3+5”, 8), (“2+4”, 6), (“6*9”, 42)])
def test_eval(test_input, expected):
assert eval(test_input) == expected

@pytest.mark.usefixtures: A very useful marker to define which fixture or fixtures to use for the underlying test function. The fixture names can be specified as a comma separated list of strings.
Example:
@pytest.mark.usefixtures(‘fixture_one_name’, ‘fixture_two_name’)

@pytest.mark.custom_markers: These are markers that are created dynamically which user can give name as per the requirement. These custom markers are mainly used to test filtering and categorizing different sets/types of tests.
Example:
@pytest.mark.timeout(10, “slow”, method=”thread”)
@pytest.mark.slow
def test_function():
…

Conclusion:
Pytest is way above and over the above listed concepts. Compiled the most useful concepts that are difficult to find all at one place. These concepts are useful for a framework developer to plan the architecture of the test automation framework and make the most of them to build an efficient, flexible, robust and scalable test automation framework.

Softnautics offers top-notch Quality Engineering Services for software and embedded devices, enabling businesses to develop high-quality solutions that are well-suited for the competitive marketplace. Testing of embedded software and devices, DevOps and test automation, as well as machine learning application and platform testing, are all part of our Quality Engineering services. Our team of experts have experience working on automation frameworks and tools like Jenkins, Python, Robot, Selenium, JIRA, TestRail, Jmeter, Git and more, as well as ensure compliance with industrial standards such as FuSa – ISO 26262, MISRA C, and AUTOSAR.

To streamline the testing process, we have developed STAF, our in-house test automation framework that facilitates end-to-end product/solution testing with greater efficiency and faster time-to-market. Softnautics comprehensive Quality Engineering services have a proven track record of success, as evident in our numerous success stories across different domains.

For any queries related to your solution design or require consultation, please contact us at business@softnautics.com.

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Today’s modern businesses require faster software feature releases to produce high-quality products and to get to market quickly without sacrificing software quality. To ensure successful deployments, the accelerated release of new features or bug fixes in existing features requires rigorous end-to-end software testing. While manual testing can be used for small applications or software, large and complex applications require dedicated resources and technologies like python testing frameworks, automation testing tools, and so on to ensure optimal test coverage in less time and faster quality releases. PyTest is a testing framework that allows individuals to write test code in Python. It enables you to create simple and scalable test cases for databases, APIs, and user interfaces. PyTest is primarily used for writing API tests. It aids in the development of tests ranging from simple unit tests to complex functional tests. According to a report published by future market insights group, the global automation testing market is expected to grow at a CAGR of 14.3% registering a market value of US$ 93.6 billion by the end of 2032.

Why choose Pytest?

Selection of the right testing framework can be difficult and relies on parameters like feasibility, complexity, scalability, and features provided by a framework. PyTest is the go-to test framework for a test automation engineer with a good understanding of Python fundamentals. With the PyTest framework, you can create high-coverage unit tests, complex functional tests, and acceptance tests. Apart from being an extremely versatile framework for test automation, PyTest also has a plethora of test execution features such as parameterizing, markers, tags, parallel execution, and dependency.

• There is no boilerplate while using Pytest as a test framework 
• Pytest can run tests written in unittest, doctest, and nose
• Pytest supports plugins for behaviour driven testing
• There are more than 150 plugins available to support different types of test automation
•  

The diagram below shows a typical structure of a Pytest framework.

Pytest root framework

As shown above in the structure, the business logic of the framework core components is completely independent of Pytest components. Pytest makes use of the core framework just like instantiating the objects and calling its functions in the test script. Test script file name should either start with `test_` or end with `_test`. The test function name should also be in the same format. Reporting in Pytest can be taken care of by Pytest-html reporting.

Important Pytest features
Pytest fixtures
The most prominently used feature of Pytest is Fixtures. Fixtures, as the name suggests are decorator functions that are used in pytest to generate a specific condition that needs to be arranged for the test to be run successfully. The condition can be any precondition like creating objects of the classes required, bringing an application to a specific state, bringing up the mockers in case of unit tests, initializing the dependencies, etc. Fixtures also take care of the teardown or reverting of the conditions that were generated after the test execution is completed. In general, fixtures take care of the setup and teardown conditions for a test.

Fixture scope
The setup and teardown do not have to be just for the test function. Scope of the setup may differ from a test function to as large as the whole test session. This means the setup-teardown is executed only once per defined scope. To achieve the same, we can define the scope along with the fixture decorator i.e., session, module, class, function..

Fixture usage
Pytest provides the flexibility to use a fixture implicitly or call it explicitly, with autouse parameter. To call the fixture function by default, the autouse parameter value needs to be set to True, else to False.

Conftest.py
All the fixtures that are to be used in the test framework are usually defined in conftest.py. It is the entry point for any Pytest execution. Fixtures need not be autouse=True. All defined fixtures can be accessed by all the test files. conftest.py needs to be placed in the root directory of the Pytest framework.

Pytest hooks
Pytest provides numerous hooks that will be called in to perform a specific setup. Hooks are generator functions that yield exactly once. Users can also write wrappers in conftest for the Pytest hooks.

Markers
Pytest provides markers to group a set of tests based on feature, scope, test category, etc. The test execution can be auto-filtered based on the markers. i.e., acceptance, regression suit, login tests, etc. Markers also act as an enabler for parameterizing a test. The test will be executed for all the parameters that are passed as the argument. Note, Pytest considers a test for one parameter as a completely independent test. Many things can be achieved with markers like marking a test to skip, skipping on certain conditions, depending on a specific test, etc.

Assertion
Pytest does not require the test scripts to have their assertions. It works flawlessly with Python inbuilt assertions.

Pytest.ini
All default configuration data can be put in pytest.ini and the same can be read by the conftest without any specific implementation.
PyTest supports a huge number of plugins with which, almost any level of a complex system can be automated. A major benefit of Pytest is that any kind of implementation of the structure is done using raw Python code without any boilerplate code. It means implementing anything in Pytest is as flexible and clean as implementing anything in Python itself.
Amidst shorter development cycles, test automation provides several benefits that are critical for producing high-quality applications. It reduces the possibility of unavoidable human errors taking place during manual testing methods. Automated testing improves software quality and reduces the likelihood of defects jeopardizing delivery timelines.
At Softnautics, we provide Quality Engineering Services for both embedded and software products to help businesses create high-quality solutions that will enable them to compete in the market. Our complete QE services include embedded software and product testing, DevOps and automated testing, ML platform testing, and compliance with industry standards such as FuSa – ISO 26262, MISRA C, AUTOSAR, etc. Our internal test automation platform, STAF, supports businesses in testing end-to-end solutions with increased testing efficiency and accelerated time to market.

Test automation is the practice of automating test execution using frameworks and tools to carry out tests more quickly and reduce the need for human testers. In this method of software testing, reusable test scripts are created to test the functioning of the application, cutting down on overall regression time and facilitating quicker software releases. Utilizing test automation shortens the testing life cycle’s regression time and improves quality of releases. According to a report published by future market insights group, the global automation testing market is expected to grow at a CAGR of 14.3% registering a market value of US$ 93.6 billion by the end of 2032. Automated test scripts can be written in several different programming languages, such as Python, C#, Ruby, Java, etc. Among them, Python is by far the most popular language used among automation engineers for automation testing. It provides various useful tools and libraries used for automation testing. Python also extensively supports many different types of test automation frameworks. Apart from the default Python testing framework, unit test (or PyUnit), various python frameworks are available that can be more suitable for the project. The most appropriate test framework for the project can be selected based on the project requirement, size, and the automation framework practiced, for example, TDD (Test Driven Development), BDD (Behaviour Driven Development), ATDD (Acceptance Test Driven Development), KDD (Keyword Driven Development), etc.

Types of Python testing frameworks

Test Automation Framework

The introduction of the (Continuous Integration/Continuous Deployment) CI/CD process has strengthened the release mechanism, helping products to market faster than ever before and allowing application development teams to deliver code changes more frequently and reliably. Regression testing is the process of ensuring that no new mistakes have been introduced in the software after the adjustments have been made by testing the modified sections of the code as well as the parts that may be affected by the modifications. The Software Testing Market size is projected to reach $40 billion in 2020 with a 7% growth rate by 2027. Regression testing accounted for more than 8.5 percent of market share and is expected to rise at an annual pace of over 8% through 2027 as per the reports stated by the Global Market Insights group.

The Importance of Regression Testing

Regression testing is a must for large-sized software development teams following an agile model. When many developers are making multiple commits frequently, regression testing is required to identify any unexpected outcome in overall functionality caused by each commit, CI/CD setup identifies that and notifies the developers as soon as the failure occurs and makes sure the faulty commit doesn’t get shipped into the deployment.

There are different CI/CD tools available, but Jenkins is widely accepted because of being open source, hosts multiple productivity improvement plugins, has active community support, and can set up and scale easily. Source Code Management (SCM) platforms like GitLab and GitHub are also providing a good list of CI/CD features and are highly preferred when the preference is to use a single platform to manage code collaboration along with CI/CD.

Different level of challenges needs to be overcome when CI/CD setup is handling multiple software products with different teams, is using multiple SCMs like GitLab, GitHub, and Perforce, is required to use a cluster of 30+ high configuration computing hosts consisting of various operating systems and handling regression job count as high as 1000+. With the increasing complexity, it becomes important to have an effective notification mechanism, robust monitoring, balanced load distribution of clusters, and scalability and maintenance support along with priory management. In such scenarios, the role of the QA team would be helpful which can focus on CI/CD optimization and plays a significant part in shortening the time to market and achieving the committed release timeline.

Let us see the challenges involved in regression testing and how to overcome them in the blog ahead.

Effective notification mechanism

CI/CD tool like Jenkins provides plugin support to notify a group of people or a specific set of team members who are responsible to cause unexpected failures in the regression testing. Email notifications generated out of plugins are very helpful to bring attention to the underlying situation which needs to be fixed ASAP. But when there are plenty of such email notifications flooding the mailbox, it becomes inefficient to investigate each of them and has a high chance of being missed out. To handle such scenarios, a Failure Summary Report (FSR) highlighting new failures becomes helpful. FSR can further have an executive summary section along with detailed summary sections. Based on the project requirement, one can integrate JIRA, Jenkins links, SCM commit links, and time stamps to make it more useful for developers as the report will have all required references in a single document. FSR can be generated once or multiple times a day based on project requirements.

Optimum use of computing resources

When CI/CD pipelines are set up to use a cluster of multiple hosts with high computing resources, it is expected to have a minimum turnaround time of a regression run cycle with maximum throughput. To achieve this, regression runs need to be distributed correctly across the cluster. Workload management and scheduler tools like IBM LSF, and PBS can be used to run the jobs concurrently based on available computing resources at a given point in time. In Jenkins, one can add multiple slave nodes to distribute jobs across the cluster to minimize the waiting time in the Jenkins queue, but this needs to be done carefully based on available computing power after understanding the resource configuration of slave hosting servers, if not done carefully can result into node crash and loss of data.

Resource monitoring

To support the growing requirement of CI/CD, while scaling one can easily be missed to consider the disk space limitations or cluster resource limitations. If not handled properly, it results in CI/CD node crashes, slow executions, and loss of data. If such an incident happens when a team is approaching an import deliverable, it becomes difficult to meet the committed release timeline. Robust monitoring and notification mechanism should be in place to avoid such scenarios. One can-built monitoring application which continuously monitors the resources of each computing host, network disk space, and local disk space and raises a red flag when the set thresholds are crossed.

Scalability and maintenance

When regression job count grows to many 1000+, it becomes challenging to maintain them. A single change if manually needs to be done in many jobs becomes time-consuming and error-prone. To overcome this challenge, one should opt for a modular and scalable approach while designing test procedure run scripts. Instead of writing steps in CI/CD, one can opt to use SCM to maintain test run scripts. One can also use Jenkins APIs to update the jobs from the backend to save manual efforts.

Priority management

When regression testing of multiple software products is being handled in a single CI/CD setup, priority management becomes important. Pre-merge jobs should get prioritized over post-merge jobs, this can be achieved by running pre-merge jobs on a dedicated host by providing separate Jenkins slave and LSF queue. Post-merge Jenkins jobs of different products should be configured to use easy-to-update placeholders for Jenkins slave tags and LSF queues such that priorities can be easily altered based on which product is approaching the release.

Integration with third-party tools

When multiple SCMs like GitLab/GitHub and issue tracking tools like JIRA are used, tacking commits, MRs, PRs, and issue updates help the team to be in sync. Jenkins integration with GitLab/GitHub helps in reflecting pre-merge run results into SCM. By integrating an issue tracker like JIRA with Jenkins, one can create, and update issues based on run results. With SCM tools and JIRA integration, issues can be auto-updated on a new commit and PR merges.

Not only must regression test plans be updated to reflect new changes in the application code, but they must also be iteratively improved to become more effective, thorough, and efficient. A test plan should be viewed as an ever-evolving document. Regression testing is critical for ensuring high quality, especially as the breadth of the regression develops later in the development process. That’s why prioritization and automation of test cases are critical in Agile initiatives.

At Softnautics, we offer Quality Engineering Services for both software and embedded devices to assist companies in developing high-quality products and solutions that will help them succeed in the marketplace. Embedded and product testing, DevOps and test automation, Machine Leaning Application/Platform testing and compliance testing are all part of our comprehensive QE services. STAF, our in-house test automation framework, helps businesses test end-to-end products with enhanced testing productivity and a faster time to market. We also make it possible for solutions to meet a variety of industry standards, like FuSa ISO 26262, MISRA C, AUTOSAR, and others.

Read our success stories related to Quality Engineering services to know more about our expertise in the domain.

Contact us at business@softnautics.com for any queries related to your solution or for consultancy.

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It has been observed that the frequency of traffic accidents has increased significantly over the last two decades, resulting in many fatalities. As per the WHO (World Health Organization) road safety report across the globe, about 1.2 million people lose their life on the roads each year, with another 20 to 50 million suffering quasi-injuries. One of the primary elements that have a direct impact on road user safety is the reliability of automobile devices and systems.

Autonomous vehicles are gaining immense popularity with the advancement in self-driving. Wireless connectivity and other substantial technologies are facilitating ADAS (Advanced Driver Assistant Systems), which consists of applications like adaptive cruise control, automated parking, navigation system, night vision & automatic emergency braking, etc, which play a critical role in the development of fully autonomous vehicles.

Safety Of The Intended Functionality SOTIF (ISO/PAS 21448) was created to solve the new safety challenges that software developers are encountering for autonomous (and semi-autonomous) vehicles. SOTIF (ISO 21448) refers to safety-critical functionality that necessitates sufficient situational awareness. By implementing these procedures, you can accomplish safety in situations where you might otherwise fail. SOTIF (ISO 21448) was designed to be ISO 26262: Part 14 at first. Since assuring safety in the absence of a system breakdown is so difficult, SOTIF (ISO 21448) has become its standard. Because AI and Machine Learning are the vital components of autonomous vehicles. The use of SOTIF (ISO 21448) will be critical in guaranteeing that AI can make appropriate judgments and avoid dangers.

Functional Safety – ISO 26262

FuSa (ISO 26262) automotive functional safety standard establishes a safety life cycle for automotive electronics, requiring designs to pass through an overall safety process to comply with the standard. As within the case of IEC (International Electrotechnical Commission), 61508 measures the reliability of safety functions and uses maximum probability while ISO 26262 is predicated on the violation of safety goals and provides requirements to realize a suitable level of risk. ISO 26262 validates a product’s compliance from conception to decommissioning to develop safety-compliant systems.

ISO 26262 employs the idea of Automotive Safety Integrity Levels (ASILs), a refinement of Safety Integrity Levels, to reach the objective of formulating and executing reliable automotive systems and solutions. ASILs are assigned to components and subsystems that have the potential to cause system failure and malfunction, resulting in hazards. The best allocation of safety levels to the system framework is a complicated issue that must ensure that the highest safety criteria are met while the development cost of the automobile system is kept to a minimum. Let us see what each part of this standard reflects.

Automotive Functional Safety Guidelines

Part 1 – Vocabulary: It relates to the definitions, terms, and abbreviations used in the standard to maintain unity and avoid misunderstanding.

Part 2 – Management of Functional Safety: It offers information on general safety management as well as project-specific information on management activities at various stages of the safety lifecycle.

Part 3 – Concept Phase: Analysis and assessment of risk are being evaluated in the early product development phase.

Part 4 – Product Development at the System Level: It covers system-level development issues comprising system architecture design, item integration & testing.

Part 5 – Product Development at the Hardware Level: It covers basic hardware level design and evaluation of hardware metrics.

Part 6 – Product Development at the Software Level: It comprises software safety, design, integration & testing of embedded software.

Part 7 – Production and Operation: This section explains how to create and maintain a production process for safety-related parts and products that will be installed in vehicles.

Part 8 – Support Processes: This section covers all stages of a product’s safety lifecycle, like proceeding to verification, undertaking tool qualification, documentation etc.

Part 9 – Automotive Safety Integrity Level (ASIL): It covers the requirement for ASIL analysis, defines ASIL decomposition state and analysis of dependent failures.

Part 10 – Guideline on ISO 26262: It covers an overview of ISO 26262 and other guidelines on how to apply the standard.

ISO 26262 classifies ASILs into four categories: A, B, C, and D. The lowest degree of automobile hazard is ASIL A, while the maximum degree is ASIL D. Since the dangers connected with their failure is the highest, systems like airbags, anti-lock brakes, and power steering require an ASIL-D rating, the highest level of rigor applied to safety assurance. Components like rear lights, on the other hand, are merely required to have an ASIL-A rating. ASIL-B would be used for headlights and brake lights, while ASIL-C would be used for cruise control.

Types of ASIL classification

Automotive Safety Integrity Levels are determined by two factors such as analysis of hazard and assessment of risk. Engineers measure three distinct factors for each electronic component in a vehicle, and those are based on the following factors.

• Intensity (the severity of the driver’s and passengers’ injuries)
• Amount of exposure (how frequently the vehicle is subjected to the hazard)
• Possibility of control (how much the driver can do to avoid an accident.)

MISRA C

The Motor Industry Software Reliability Association (MISRA) publishes standards for the development of safety and security-related electronic systems, embedded control systems, software-intensive applications, and independent software.

MISRA C contains components that protect automobile software from errors and failures. With over 140 rules for MISRA–C and more than 220 rules for MISRA–C++, the suggestions tackle code safety, portability, and reliability issues that affect embedded systems. For MISRA C compliance, developers must follow a set of mandatory rules. The goal of MISRA C is to provide the best performance in terms of software operation for software programs used in automobiles, as these programs can have a significant impact on the vehicle’s overall design safety. Developers utilize MISRA C as one of the tools for developing safe software for automobiles.

AUTOSAR

AUTOSAR (Automotive Open System Architecture) this standard’s goal is to provide a set of specifications that describe fundamental software modules, specify programmatic links, and implement common methods for further development using a standardized format.

AUTOSAR’s sole purpose is to provide a uniform standard across manufacturers, software suppliers, and tool developers while maintaining competition so that the result of the business is not harmed.

While reusability of software components lowers development costs and guarantees stability, it also increases the danger of spreading the same software flaw or vulnerability to other products that use the same code. To solve this significant issue, AUTOSAR advocates safety and security features in software architecture.

The design approach of AUTOSAR includes

• Product and system definition including software, hardware, and complete system.
• Allocating AUTOSAR to each ECU (Electronic Control Unit)
• Configuration of OS, drivers, and application for each ECU (Electronic Control Unit)
• Comprehensive testing to validate each component, at unit level and system level.

The necessity to assure functional safety at every level of product development and commissioning has grown even more crucial in today’s world when automotive designs have got increasingly complicated with many ECUs, sensors, and actuators. Therefore, today’s automakers are more concerned about adhering to the highest automobile safety requirements, such as the ISO 26262 standard and ASIL Levels.

At Softnautics, we help automotive businesses to manufacture devices/chipsets complying with automotive safety standards and design Machine Learning based intelligent solutions such as automatic parallel parking, traffic sign recognition, object/lane detection, in-vehicle infotainment systems, etc. involving FPGAs, CPUs, and Microcontrollers. Our team of experts has experience working with autonomous driving platforms, middleware, and compliances like adaptive AUTOSAR, FuSa (ISO 26262), and MISRA C. We support our clients in the entire journey of intelligent automotive solution design.

Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.

Contact us at business@softnautics.com for any queries related to your solution or for consultancy.

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SOM is an entire CPU architecture built in a small package, of size like a credit card. It is a board-level circuit that integrates a system function and provides core components of an embedded processing system – processor cores, communication interfaces, and memory blocks on a single module. Designing any product based on the SOM is a much faster process than designing the entire system from the ground up.

There are multiple System on Module manufacturers available in the market worldwide with an equal amount of open-source automated testing frameworks. If you plan to use System-on-Module (SOM) in your product, the first thing required is to identify the test automation framework from the ones available out in the market and check for a suitable module for your requirement.

Image/Video intensive industries face difficulty in designing and developing customized hardware solutions for explicit application, with reduced time and cost. It is linked with quick evolving processors with increasing complexity, requiring product companies to constantly introduce upgraded variants in a short span. System on Module (SOM) ensures reduced development and design risk for any application. SOM is a re-usable module embracing maximum hardware/processor complexity, leaving behind reduced work on the carrier/mainboard, thus accelerating Time-to-Market.

System-on-Module is a small PCB board having CPU, RAM, Flash, Power Supply, and various IOs (GPIOS, UART, USB, I2C, SPI, etc.). In new-age electronics, SOM is becoming a quite common part of the design, specifically in industrial, medical electronics. It reduces the design complexity and the time-to-market which is critical for a product’s success. These System-on-Modules runs an OS and are mainly used in applications where Ethernet, file systems, high-resolution display, USB, Internet, etc. are required and the application needs high computing with less development effort. If you are building a product with less than 20-25K volume, it is viable to use a ready SOM for the product development.

Test Automation frameworks for SOM

A testing automation framework is a set of guidelines used for developing test cases. A framework is an amalgamation of tools and practices designed to support quality assurance experts test more efficiently. The guidelines involve coding standards, methodologies to handle test data, object repositories, processes to store test results, or information on accessing external resources. Testing frameworks are an essential part of any successful product release that goes under testing automation. Using a framework for automated testing will enhance a team’s testing efficiency, accuracy, and will reduce time and risks.

There are different types of Automated Testing Frameworks, each having its architecture and merits/demerits. Selecting the right framework is very crucial for your SOM application testing.

Below mentioned are few frameworks used commonly:

• Linear Automation Framework
• Modular Based Testing Framework
• Library Architecture Testing Framework
• Data-Driven Framework
• Keyword-Driven Framework
• Hybrid Testing Framework

From above, the Modular and Hybrid testing frameworks are best suitable for SOM and their development kit verification. The ultimate goal of testing is to ensure that software works as per the specifications and in line with user expectations. The entire process involves quite a few testing types which are preferred or prioritized over others depending on the nature of the application and organization. Let us see some of the basic testing involved in the end-to-end testing process.

Unit testing: Full software stack is made of many small components, so instead of directly testing the full software stack one should cover individual module level testing first. Here unit testing makes sure to have module/method level input/output testing coverage. Unit testing offers a base for complex integrated software and provides fine quality application code, speeding up continuous integration and development process. Often unit tests are executed through test automation by developers.

Smoke testing: Smoke testing is to verify whether the deployed software build is stable or not. To go ahead with further testing depends on smoke test results. It is also referred to as build verification testing which checks whether functionality meets its objective. There is still some development work required if SOM does not clear the smoke.

Sanity testing: The changes or proposed functionality that are working as expected is defined by sanity testing. Suppose we fix some issue in the boot flow of the embedded product, then it should go to the validation team for sanity testing. Once this test is passed it should not impact other basic functionality. Sanity testing is unscripted and specifically targets the area that has undergone a code change.

Regression testing: Every time the program is revised/modified, it should be retested to assure that the modifications didn’t unintentionally “break” some unrelated behavior. This is called regression testing; these tests are usually automated through a test script. Each time the program/design is tested, it should give a smooth result.

Functional testing: Functional testing specifies what the system does. It is also known as black-box testing because the test cases for functional tests are developed without reference to the actual code, i.e., without looking “inside the box.”

Any embedded system has inputs, outputs, and implements some drivers between them. Black-box testing is about which inputs should be acceptable and how they should relate to the outputs. The tester is unaware of the internal structure of the module or source code. Black-box tests include stress testing, boundary value testing, and performance testing.

Over the past years, Softnautics has developed complex software around various processor families from Lattice, Xilinx, Intel, Qualcomm, TI, etc., and has successfully tested the boards for applications like vision processing, AI/ML, multimedia, industrial IoT, and more. Softnautics has market proven process for developing a verification and validation automation suite with zero compromises on feature and/or performance coverage as well as executing test automation with in-house STAF and open-source frameworks. We also provide testing support for product/solution future releases, release management, and product sustenance/maintenance.

Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.

Contact us at business@softnautics.com for any queries related to your solution or for consultancy.

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