SPLK-2003 test - Splunk SOAR Certified Automation Developer Updated: 2024
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Exam Code: SPLK-2003 Splunk SOAR Certified Automation Developer test January 2024 by Killexams.com team
|Splunk SOAR Certified Automation Developer
Splunk Automation test
Other Splunk examsSPLK-1003 Splunk Enterprise Certified Admin
SPLK-1001 Splunk Core Certified User
SPLK-2002 Splunk Enterprise Certified Architect
SPLK-3001 Splunk Enterprise Security Certified Admin
SPLK-1002 Splunk Core Certified Power User
SPLK-3003 Splunk Core Certified Consultant
SPLK-2001 Splunk Certified Developer
SPLK-1005 Splunk Cloud Certified Admin
SPLK-2003 Splunk SOAR Certified Automation Developer
SPLK-4001 Splunk O11y Cloud Certified Metrics User
SPLK-3002 Splunk IT Service Intelligence Certified Admin
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Configuring Phantom search to use an external Splunk server provides which of the following benefits?
A. The ability to run more complex reports on Phantom activities.
B. The ability to ingest Splunk notable events into Phantom.
C. The ability to automate Splunk searches within Phantom.
D. The ability to display results as Splunk dashboards within Phantom.
Within the 12A2 design methodology, which of the following most accurately describes the last step?
A. List of the apps used by the playbook.
B. List of the actions of the playbook design.
C. List of the outputs of the playbook design.
D. List of the data needed to run the playbook.
Which of the following are the steps required to complete a full backup of a Splunk Phantom deployment' Assume the
commands are executed from /opt/phantom/bin and that no other backups have been made.
A. On the command line enter: rode sudo python ibackup.pyc --setup, then audo phenv python ibackup.pyc --backup.
B. On the command line enter: sudo phenv python ibackup.pyc --backup âbackup-type full, then sudo phenv python
C. Within the UI: Select from the main menu Administration > System Health > Backup.
D. Within the UI: Select from the main menu Administration > Product Settings > Backup.
An active playbook can be configured to operate on all containers that share which attribute?
Which of the following applies to filter blocks?
A. Can select which blocks have access to container data.
B. Can select assets by tenant, approver, or app.
C. Can be used to select data for use by other blocks.
D. Can select containers by seventy or status.
A user has written a playbook that calls three other playbooks, one after the other. The user notices that the second
playbook starts executing before the first one completes.
What is the cause of this behavior?
A. Incorrect Join configuration on the second playbook.
B. The first playbook is performing poorly.
C. The steep option for the second playbook is not set to a long enough interval.
D. Synchronous execution has not been configured.
A customer wants to design a modular and reusable set of playbooks that all communicate with each other.
Which of the following is a best practice for data sharing across playbooks?
A. Use the py-postgresq1 module to directly save the data in the Postgres database.
B. Cal the child playbooks getter function.
C. Create artifacts using one playbook and collect those artifacts in another playbook.
D. Use the Handle method to pass data directly between playbooks.
Which of the following are examples of things commonly done with the Phantom REST APP
A. Use Django queries; use curl to create a container and add artifacts to it; remove temporary lists.
B. Use Django queries; use Docker to create a container and add artifacts to it; remove temporary lists.
C. Use Django queries; use curl to create a container and add artifacts to it; add action blocks.
D. Use SQL queries; use curl to create a container and add artifacts to it; remove temporary lists.
Which of the following are the default ports that must be configured on Splunk to allow connections from Phantom?
A. SplunkWeb (8088), SplunkD (8089), HTTP Collector (8000)
B. SplunkWeb (8089), SplunkD (8088), HTTP Collector (8000)
C. SplunkWeb (8421), SplunkD (8061), HTTP Collector (8798)
D. SplunkWeb (8000), SplunkD (8089), HTTP Collector (8088)
Without customizing container status within Phantom, what are the three types of status for a container?
A. New, In Progress, Closed
B. Low, Medium, High
C. Mew, Open, Resolved
D. Low, Medium, Critical
Splunk user account(s) with which roles must be created to configure Phantom with an external Splunk Enterprise
A. superuser, administrator
B. phantomcreate. phantomedit
C. phantomsearch, phantomdelete
Phantom supports multiple user authentication methods such as LDAP and SAML2.
What other user authentication method is supported?
During a second test of a playbook, a user receives an error that states: 'an empty parameters list was passed to
phantom.act()." What does this indicate?
A. The container has artifacts not parameters.
B. The playbook is using an incorrect container.
C. The playbook debugger's scope is set to new.
D. The playbook debugger's scope is set to all.
What does a user need to do to have a container with an event from Splunk use context-aware actions designed for
A. Include the notable event's event_id field and set the artifacts label to aplunk notable event id.
B. Rename the event_id field from the notable event to splunkNotableEventld.
C. Include the event_id field in the search results and add a CEF definition to Phantom for event_id, datatype splunk
notable event id.
D. Add a custom field to the container named event_id and set the custom field's data type to splunk notable event id.
After enabling multi-tenancy, which of the Mowing is the first configuration step?
A. Select the associated tenant artifacts.
B. Change the tenant permissions.
C. Set default tenant base address.
D. Configure the default tenant.
When configuring a Splunk asset for Phantom to connect to a SplunkC loud instance, the user discovers that they need
to be able to run two different on_poll searches.
How is this possible
A. Enter the two queries in the asset as comma separated values.
B. Configure the second query in the Phantom app for Splunk.
C. Install a second Splunk app and configure the query in the second app.
D. Configure a second Splunk asset with the second query.
On a multi-tenant Phantom server, what is the default tenant's ID?
What are indicators?
A. Action result items that determine the flow of execution in a playbook.
B. Action results that may appear in multiple containers.
C. Artifact values that can appear in multiple containers.
D. Artifact values with special security significance.
Which app allows a user to send Splunk Enterprise Security notable events to Phantom?
A. Any of the integrated Splunk/Phantom Apps
B. Splunk App for Phantom Reporting.
C. Splunk App for Phantom.
D. Phantom App for Splunk.
Some of the playbooks on the Phantom server should only be executed by members of the admin role.
How can this rule be applied?
A. Add a filter block to al restricted playbooks that Titters for runRole - "Admin''.
B. Add a tag with restricted access to the restricted playbooks.
C. Make sure the Execute Playbook capability is removed from al roles except admin.
D. Place restricted playbooks in a second source repository that has restricted access.
Recore 3D printer board developer [Elias Bakken] has posted about the automatic test procedure he developed using a stack-up of four (at least) pieces of vintage HP test equipment. In addition, his test jig and test philosophy is quite interesting.
Besides making a bed-of-nails test jig, he also designed a relay multiplexing board to that selects one of the 23 different voltages for measurement. We like his selection of mechanically latching relays in this application â€” not only does it save power, but it doesnâ€™t subject the test board to any magnetic fields (except when switching state).
In [Elias]â€™s setup, the unit under test (UUT) actually orchestrates the testing process itself. This isnâ€™t as crazy as it might sound. The processor is highly integrated in one package plus external DRAM. If the CPUs boot up at all, and pass simple self-test routines, thereâ€™s no reason not to utilize the on-board processor as the main test control computer. This might be a questionable decision if your processor was really small with constrained resources and connectivity. But in the case of Recore, the processor is a four-core ARM A53 SoC running Debian Linux â€” an arrangement that itself could well serve as an automated test computer in other projects.
In the video down below, [Elias] walks us through the basic tests, and then focuses on the heart of the Recore board tests: calibrating the input signal conditioning circuits. Instead of using very expensive precision resistors, [Elias] selected more economical 1% resistors to use in the preamp circuitry. The tradeoff here is the need to calibrate each channel, perhaps at multiple temperature points. This is a situation where using a test jig, automated test scripts, and and stack of programmable test equipment really shines.
[Elias] is still pondering some issues he found trying to calibrate thermocouples, so his adventure is not quite over yet. If you are wondering what Recore is, check out this article from back in June. Have you ever used the microprocessor on a circuit board to test itself, either standalone or in conjunction with an external jig? Let us know in the comments below.
The following is a description of two methods that have proven effective in implementing an Automated Testing Solution:
"Functional Decomposition" Method:
Navigation (e.g. "Access Payment Screen from Main Menu")
In order to accomplish this, it is necessary to separate Data from Function. This allows an automated test script to be written for a Business Function, using data-files to provide the both the input and the expected-results verification. A hierarchical architecture is employed, using a structured or modular design.
The highest level is the Driver script, which is the engine of the test. The Driver begins a chain of calls to the lower level components of the test. Drivers may perform one or more test case scenarios by calling one or more Main scripts. The Main scripts contain the test case logic, calling the Business Function scripts necessary to do the application testing. All utility scripts and functions are called as needed by Drivers, Main, and Business Function scripts.
Driver Scripts: Perform initialization (if required), then call the Main Scripts in the desired order.
(Note that Functions can be called from any of the above script types.)
* Scripts may be developed while application development is still in progress. If functionality changes, only the specific "Business Function" script needs to be updated.
* Since scripts are written to perform and test individual Business Functions, they can easily be combined in a "higher level" test script in order to accommodate complex test scenarios.
* Data input/output and expected results is stored as easily maintainable text records. The user's expected results are used for verification, which is a requirement for System Testing.
* Functions return "TRUE" or "FALSE" values to the calling script, rather than aborting, allowing for more effective error handling, and increasing the robustness of the test scripts. This, along with a well-designed "recovery" routine, enables "unattended" execution of test scripts.
* Multiple data-files are required for each Test Case. There may be any number of data-inputs and verifications required, depending on how many different screens are accessed. This requires data-files to be kept in separate directories by Test Case.
* Tester must not only maintain the Detail Test Plan with specific data, but must also re-enter this data in the various data-files.
* If a simple "text editor" such as Notepad is used to create and maintain the data-files, careful attention must be paid to the format required by the scripts/functions that process the files, or processing-errors will occur.
The concept of Continuous Integration (CI) is a powerful tool in software development, and itâ€™s not every day we get a look at how someone integrated automated hardware testing into their system. [Michael Orenstein] brought to our attention theÂ Hardware CI Arena, a framework for doing exactly that across a variety of host OSes and microcontroller architectures.
Hereâ€™s the reason it exists: while in theory every OS and piece of hardware implements things like USB communications and device discovery in the same way, in practice that is not always the case. For individual projects, the edge cases (or even occasional bugs) are not much of a problem. But when one is developing a software product that aims to work seamlessly across different hardware options, such things get in the way. To provide a reliable experience, one must find and address edge cases.
The Hardware CI Arena (GitHub repository) was created to allow automated testing to be done across a variety of common OS and hardware configurations. It does this by allowing software-controlled interactions to a bank of actual, physical hardware options. Itâ€™s purpose-built for a specific need, but the level of detail and frank discussion of the issues involved is an interesting look at what it took to get this kind of thing up and running.
The value of automatic hardware testing with custom rigs is familiar ground to anyone who develops hardware, but tying that idea into a testing and CI framework for a software product expands the idea in a useful way. When it comes to identifying problems, earlier is always better.
In this two part series, we explore the two sides of testing: automated and manual. In this article, we examine why automated testing should be done. To read the other side of the argument, go here.Â
In todayâ€™s business environment, stakeholders rely on their enterprise applications to work quickly and efficiently, with absolutely no downtime. Anything short of that could result in a slew of business performance issues and ultimately lost revenue. Take the recent incident in which CDN provider Fastly failed to detect a software bug which resulted in massive global outages for government agencies, news outlets and other vital institutions.Â
Effective and thorough testing is mission-critical for software development across categories including business software, consumer applications and IoT solutions. But as continuous deployment demands ramp up and companies face an ongoing tech talent shortage, inefficient software testing has become a serious pain point for enterprise developers, and theyâ€™ve needed to rely on new technologies to Strengthen the process.
The Benefits of Test Automation
As with many other disciplines, the key to quickly implementing continuous software development and deployment is robust automation. Converting manual tests to automated tests not only reduces the amount of time it takes to test, but it can also reduce the chance of human error and allows minimal defects to escape into production. Just by converting manual testing to automated testing, companies can reduce three to four days of manual testing time to one, eight-hour overnight session. Therefore, testing does not even have to be completed during peak usage hours.
Automation solutions also allow organizations to test more per cycle in less time by running tests across distributed functional testing infrastructures and in parallel with cross-browser and cross-device mobile testing. Furthermore, if a team lacks mobile devices to test on, it can leverage solutions to enable devices and emulators to be controlled through an enterprise-wide mobile lab manager.
Challenges in Test Automation
Despite all the benefits of automated software testing, many companies are still facing challenges that prevent them from reaping the full benefits of automation. One of those key challenges is managing the complexities of todayâ€™s software testing environment, with an increasing pace of releases and proliferation of platforms on which applications need to run (native Android, native iOS, mobile browsers, desktop browsers, etc.). With so many conflicting specifications and platform-specific features, there are many more requirements for automated testing â€“ meaning there are just as many potential pitfalls.
Software releases and application upgrades are also happening at a much quicker pace in recent years. The faster rollout of software releases, while necessary, can break test automation scripts due to fragile, properties-based object identification, or even worse, bitmap-based identification. Due to the varying properties across platforms, tests must be properly replicated and administered on each platform â€“ which can take immense time and effort.
Therefore, robust, and effective test automation also requires an elevated skill set, especially in todayâ€™s complex, multi-ecosystem application environment. Record-and-playback testing, a tool which records a testerâ€™s interactions and executes them many times over, is no longer sufficient.
With all of these challenges to navigate, including how difficult it can be to find the right talent, how can companies increase release frequency without sacrificing quality and security?
Ensuring Robust Automation with Artificial Intelligence
To meet the high demands of software testing, automation must be coupled with Artificial Intelligence (AI). Truly robust automation must be resilient, and not rely on product code completion to be created. It must be well-integrated into an organizationâ€™s product pipelines, adequately data-driven and in full alignment with the business logic.
Organizations can allow quality assurance teams to begin testing earlier â€“ even in the mock-up phase â€“ through the use of AI-enabled capabilities for the creation of single script that will automatically execute on multiple platforms, devices and browsers. With AI alone, companies can experience major increases in test design speed as well as significant decreases in maintenance costs.
Furthermore, with the proliferation of low-code/no-code solutions, AI-infused test automation is even more critical for ensuring product quality. Solutions that infuse AI object recognition can enable test automation to be created from mockups, facilitating test automation in the pipeline even before product code has been generated or configured. These systems can provide immediate feedback once products are initially released into their first environments, providing for more resilient, successful software releases.
To remain competitive, all businesses need to be as productive and efficient as possible, and the key to that lies in properly tested, functioning, performant enterprise applications. Cumbersome, manual testing is no longer sufficient, and enterprises that continue to rely on it will be caught flat-footed and getting outperformed and out-innovated. Investing in automation and AI-powered development tools will give enterprises the edge they need to stay ahead of the competition.
Founder and CEO of QA Mentor, Inc., an independent software-testing company headquartered in New York.
As the founder and CEO of an automated software testing company, Iâ€™ve been following the trends in this space closely, and it's exciting to see that the automation testing market is estimated to more than double in five yearsâ€”to $52.7 billion by 2027 from $24.7 billion in 2022.
As ResearchAndMarkets put it, more and more people work remotely and rely on mobile devices as well as cloud-based solutions. This has created an immediate need for all these devices to function without a hitch. As a result, weâ€™re witnessing a spike in the demand for performance testing that helps deliver faster software deployment to meet the requirements of the growing customer base across the globe.
Thatâ€™s just on the user side of things. From the vendor perspective, companies operate on a battlefield where competitors constantly push boundaries. Thereâ€™s a need for lightning-fast time-to-market. But there's also the need for cost-efficiency, to fight off the bug invasion, to manage the customer satisfaction uprising, to scale and to future-proof long-term profits.
While automated testing can help with these challenges, there is also a learning curve to getting started. Let's look at a few important steps to take to properly leverage automated testing for software deployment.
1. Choose testing tools and frameworks that make sense for your team. There are many popular options to choose from, but you should evaluate and select the ones that best align with your software development environment, requirements and skills set.
2. Define a comprehensive automation test strategy. Establish a clear testing strategy that outlines the scope, objectives and coverage of automated tests. Define test cases for various functional scenarios, performance testing, security testing and any other relevant areas specific to your software. Identify and prioritize best candidates for automation.
3. Implement continuous integration (CI). Integrate automated testing with a CI system. This will enable you to run tests automatically whenever new code is committed or a deployment is triggered, ensuring immediate feedback on code changes.
4. Build a robust test suite. Develop a suite of automated tests that covers critical functionalities and edge cases. Include unit tests, integration tests, API level tests and end-to-end tests to achieve comprehensive test coverage. Prioritize the most critical and frequently used features for automated testing.
5. Leverage test automation frameworks. Utilize test automation frameworks to streamline test creation, execution and maintenance. Frameworks provide reusable components, test data management, reporting capabilities and easy integration with continuous integration/continuous deployment pipelines.
6. Implement parallel testing. Execute automated tests in parallel across multiple machines, browsers, mobile devices or test environments to expedite the testing process. This reduces the overall execution time and enables faster feedback.
7. Use cloud-based testing services. Leverage cloud-based testing services to test your software across various browsers, devices and platforms simultaneously. This enhances compatibility testing and accelerates the deployment process.
8. Implement continuous deployment (CD). Integrate automated tests into your CD pipeline to ensure that only properly tested and Verified code is deployed to production. This minimizes the risk of introducing bugs or issues in the live environment.
9. Monitor and analyze test results. Set up monitoring and reporting mechanisms to track test results, identify failures and investigate issues. Use test analytics to gain insights into test performance, trends and areas for improvement.
10. Continuously optimize and maintain tests. Regularly review and update automated test cases to reflect changes in the software and keep up with evolving requirements. Maintain a balance between test coverage and execution time by identifying and removing redundant or obsolete tests.
Keep a few important things in mind.
When getting started with automated testing, youâ€™ll probably need to pivot in terms of organizational adoption and culture because implementing automated testing usually requires a shift in mindset. Because of this, youâ€™d be wise to obtain buy-in from all relevant stakeholders and strengthen the collaboration between development and testing teams. This can be a challenge in organizations that live by the mantra, â€śIf it isnâ€™t broken, why fix it?â€ť
Setting up automated testing may require an investment in tools, infrastructure and resources, including upfront costs associated with acquiring licenses, hardware and training team members. In terms of test design and maintenance, youâ€™ll also have to invest in thorough planning that considers various scenarios and ensures comprehensive coverage. Some teams forget to update and maintain test scripts, ending up with a lack of coverage, compatibility issues and slow reporting. Needless to say, all this erodes trust and morale in testing.
You'll also need to recognize that you will be dealing with multiple configurations, platforms and dependencies that can compromise environment stability. It can also be time-consuming to generate and manage test data that covers various scenarios and edge cases, so calculate this in your planning.
Likewise, youâ€™ll need effective scheduling, parallel execution and result analysis because automated testing requires that you coordinate test runs across multiple environments or distributed systems. While automated testing reduces manual effort, it still requires human involvement for test design, maintenance, analysis and decision making. Ensuring that team members have the necessary skills and expertise to effectively implement and manage automated testing is vital.
Finally, donâ€™t forget to set aside ample time to interpret test results and identify the root cause of failures, which requires careful scrutiny.
Lean into automated testing and shorten your release cycle.
In the realm of customer relations, organizations face numerous challenges. Companies are locked in intense competition with rivals that can outpace them unless they modernize operations. They must bring products to market quickly, maintain cost-effectiveness, handle defects and satisfy customer demands while protecting their long-term profitability.
Automated testing is key to faster software deployment, better quality assurance and turbocharged feedback loops. By implementing it properly and being aware of potential pitfalls, it can help ensure efficiency, precision and profitability for businesses in the digital space.
Are you ready to be part of a transformational journey that will reshape the digital landscape of Africa? A leading Pan-African telecommunications company is launching a groundbreaking Software Engineering Centre of Excellence (COE); dedicated to building future-focused digital products designed to empower and revolutionize the continent. This is your opportunity to join an innovative team and grow your career at the forefront of technological advancement, as we accelerate towards a brighter, more connected future for Africa.
Creates a testing strategy and participates in the creation of automated testing scenarios. Prepares and coordinates test plans and testing scenarios. Conducts necessary tests according to the testing strategy, registration of defects, control of elimination.
Co-Founder & CTO ofÂ Cymulate. Previously, Avihai was the Head of the Cyber Research Team at Avnet Cyber & Information Security
The market for automated security testing, or breach and attack simulation (BAS), is on fire, with analysts predicting an almost 35% compound annual growth rate and over $900 million market size in 2025. The growing appetite for this kind of solution has spawned different flavors of automated testing. Common to all of them is the capability to launch attacks that challenge an organization's IT system and its security controls, with the objective of identifying and closing security gaps. But what do they mean when they say "attacks"?
A great place to start is the MITRE ATT&CK framework. It advanced the security industry by describing the tactics and techniques used by threat actors across the cyber kill chain. This knowledge base fast became a de-facto common language for vendors, security practitioners and analysts to describe attacks and the techniques they employ. It also enables organizations to describe how their security architectures stack up to different techniques, and this is where automated security testing and validation comes in. By launching attacks, these solutions are supposed to provide visibility, uncovering the strengths and weaknesses of a security architecture before a threat actor does. We can group the attacks used by automated testing platforms into three groups.
1. Real Attacks
Real attacks are a combination of tools, payloads and techniques that were originally devised by a threat actor to achieve a specific objective â€” for example, to steal intellectual property or hold data for ransom. In the context of automated security testing, they are represented by the IOCs of a specific attack/malware. A real attack will answer the important question, "Are we protected against a specific threat?"
While this is a valuable question, similar to a pen test, its answer is limited in scope, and it is related to the specific attack that was launched. It does little to shed light on the overall operational effectiveness of a security architecture. This is because the techniques used by the attack/malware are far more important than the specific attack. A security architecture that can withstand this specific attack does not ensure that it can defend against a different threat that employs the same techniques in a slightly different combination, sequence or set of dependencies. Nonetheless, organizations like to have a quick answer to the question, "Are we also susceptible to an attack that hit the headlines last night?"
2. Atomic Techniques
Atomic techniques are implementations of individual threat actor techniques, and the most commonly used knowledge base and reference for this is the MITRE ATT&CK framework. Launching this type of attack is supposed to be useful to validate detection or prevention of specific techniques â€” for example, techniques used to escalate privileges. The decoupling of the technique being tested to other techniques allows security teams to focus on a specific concern. And assuming many different implementations of this technique are realized in the automated testing platform, it should provide visibility and even measure an organization's security effectiveness against it being successful. For example, can a threat actor that has somehow landed in my network gain rights to access privileged resources through the use of privilege escalation techniques?
And yet the disadvantage of atomic techniques, compared to real attacks, is a total lack of context. In many cases, this significantly reduces their effectiveness. For example, by combining evasion with credential access techniques, we would probably face a very different result compared to atomic executions of the same techniques. When simulating attacks, it is far more important to simulate adversarial behaviors and then reference them to the ATT&CK framework, as opposed to focusing on specific techniques.
3. Contextual Attacks
Like many things in life, the answer lies somewhere in between â€” in this case, between real attacks and atomic attacks. By implementing combinations of techniques that are contextually compatible, we achieve lifelike attacks that focus on a specific link in the cyber kill chain. For example, password cracking that uses Kerberoasting is a behavior that would combine evasion together with credential access techniques, realized in the following flow and mapped to the following ATT&CK techniques. By launching a myriad of these combinations, achievable only through automation, we get visibility on the overall operational effectiveness of a security architecture.
A Word On Simulation
When Gartner named the market breach and attack simulation (BAS), it triggered yet another marketerâ€™s war of words, as if the cyber industry needed more of these. In this case, attack simulations versus real attacks. Every proactive test is a simulation of a real event. The idea is to make them as real as possible without disrupting the business, thus enabling testing in production. After all, you want to test your real defenses, not a simulation of them.
For example, a ransomware attack simulation will attempt to encrypt files created for the purpose of the simulation. This is as real as you can get without facing the catastrophic results of an real attack. Continuous security validation, as I prefer to call it, should be able to answer both the question, "Could our organization fall victim to a specific threat or real attack that hit the headlines?" and the more valuable question, "How operationally effective are our defenses?" By leveraging threat intelligence-led, real attacks and contextual attacks or attack combinations can security validation provide the visibility for security teams to prioritize their efforts and optimize their security architecture.
By Rahul Vala, Softnautics
Todayâ€™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.
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
1. 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.
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.
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.
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.
3. 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.
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.
Pytest does not require the test scripts to have their assertions. It works flawlessly with Python inbuilt assertions.
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.
Read our success stories related to Quality Engineering services to know more about our expertise in the domain.
About the Author
Rahul is working as a Principal Engineer at Softnautics and has a total of 10 yearsâ€™ experience in Test Automation of different types of systems like embedded firmware, mobile and enterprise web applications. He has developed several complex Test Automation frameworks involving complex products and multiple components like boards, mobile devices, GPIO Controls, R Pi, cloud APIs, etc. He is passionate about pytest automation and loves to debug and find root cause of complex issues. In his free time, he loves to walk and play cricket and volleyball.
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In this article, we will be looking at the 25 best online math degree programs heading into 2024. If you want to skip our detailed analysis, you can go directly to the 5 Best Online Math Degree Programs Heading Into 2024.
Data Science as a Viable Career for Math Graduates
Mathematics is a versatile and fundamental discipline that applies to various fields; therefore, many occupational opportunities are available after getting a degree in mathematics. According to the US Bureau of Statistics, data science is estimated to be the fastest-growing occupational prospect for workers with a math degree. The projected growth rate for workers with a mathematics degree in the data science industry is 35% between 2022 and 2032. The increasing prevalence and integration of data science solutions across various industries is anticipated to drive swift and substantial growth.
According to a report by Precedence Research, the global data science platform market was valued at $129.72 billion in 2023. The market is expected to grow at a compound annual growth rate (CAGR) of 16.2% from 2024 to 2032 and reach $501.03 billion by the end of the forecasted period. Data handling plays a significant role in business expansion. Enterprises are employing data-driven strategies to make operational decisions. Data science has become a vital operational requirement, especially for firms going through a digital transformation. The increasing role of data science platforms in business will fuel market growth in the coming years.
The advancement of other technologies, including machine learning, artificial intelligence, and cloud computing, is anticipated to propel the growth of the data science platform market during the forecasted period.
In 2022, North America was the most dominant region in the global data science platform market. The region accounted for 36% of the global market revenue in 2022. Europe had the second-highest share of market revenue during the same year. The Asia Pacific region is expected to be the fastest-growing region for data science platforms during the forecasted period.
Major Players in the Data Science Space
Alteryx Inc (NYSE:AYX) is one of the most prominent names in the data science platforms industry. Alteryx Analytics Automation Platform enables data scientists to accelerate development of machine learning models and focus on insights with analytics automation. On December 18, Alteryx Inc (NYSE:AYX) announced that it had agreed to be acquired by Clearlake Capital Group, LP and Insight Partners. The transaction is valued at $4.4 billion, including debt. The acquisition is estimated to close in the first half of 2024, leading to Alteryx Inc (NYSE:AYX) becoming a privately held company.
Splunk (NASDAQ:SPLK) is another noteworthy name in the data science industry. The data-to-everything platform by Splunk (NASDAQ:SPLK) enables users to turn data into business outcomes. On September 21, Splunk (NASDAQ:SPLK) announced that it had entered a definitive agreement to be acquired by Cisco (NASDAQ:CSCO). The acquisition aims to transition organizations from threat detection and response to threat prediction and prevention. The transaction is set to close by the end of the third quarter of 2024.
Domo, Inc. (NASDAQ:DOMO) is another major player in the data science space. The Data Experience Platform by Domo, Inc. (NASDAQ:DOMO) provides end-to-end tools for data science, including visualizing results to optimize data science pipelines. On November 30, the company reported earnings for the fiscal third quarter of 2023. The company's revenue for the quarter grew by 0.82% and amounted to $79.68 billion, ahead of market consensus by over $651,000. Here are some comments from the company's earnings call:Â Â
Pursuing a degree in math opens a variety of occupational prospects other than data science. The ease and flexibility of online education have made it a popular choice for students looking to balance personal and professional commitments. We have made a list of the best online math degree programs heading into 2024.
25 Best Online Math Degree Programs Heading Into 2024
To make our list of the best online math degree programs heading into 2024, we have used a consensus methodology. We consulted four sources, including Intelligent.com, OnlineU, My Degree Guide, and Best Colleges. We extracted programs that appeared in at least 2/4 of our sources. We made our selections by examining the average rankings for each program across our sources. To calculate the average ranks, we summed the individual ranks for each program across the sources it appeared in and divided it by the number of sources it appeared in. The list has been arranged in descending order of the calculated average ranks.
25 Best Online Math Degree Programs Heading Into 2024
25. University of ArizonaÂ
Number of Mention: 2
Average Ranking Across Sources: 26.5
The University of Arizona has a completely online Bachelor of Science in Mathematics program. 120 credits are required for the completion of the program. The University of Arizona is accredited by the Higher Learning Commission.
24. Chadron State College
Number of Mention: 2
Average Ranking Across Sources: 21.5
Chadron State College has one of the best online math degree programs heading into 2024. The university offers a completely online Bachelor of Science in Mathematics program. Chadron State College is accredited by the Higher Learning Commission.
23. University of Texas
Number of Mention: 2
Average Ranking Across Sources: 16
The University of Texas has an online Bachelor of Science in Math degree program. The program consists of 328 credits overall. The online course enables students to complete the courses at their own pace. The program is accredited by the Southern Association of Colleges and Schools.
22. Indiana University
Number of Mention: 2
Average Ranking Across Sources: 15.5
Indiana University offers an online Bachelor of Science in Mathematics degree. The courses are delivered through the Indiana University online platform. 120 credits are required for completion. The program is accredited by the Higher Learning Commission.
21. Indian River State College
Number of Mention: 2
Average Ranking Across Sources: 14.5
Indian River State College has one of the best online math degree programs heading into 2024. The university offers an Associate of Arts degree program in Math. 60 credit hours are required for the completion of the degree. The program is accredited by the Southern Association of Colleges and Schools Commission on Colleges.
20. National University
Number of Mention: 2
Average Ranking Across Sources: 13.5
National University has its headquarters in San Diego, California. The university offers a variety of online degree programs in math along with 4-week courses. The flexibility of online classes enables students to learn at their own pace.
19. Midway University
Number of Mention: 2
Average Ranking Across Sources: 11
Midway University offers a completely online undergraduate math degree program. The course has a focus on logic, problem-solving, and data analysis. The online degree program prepares students for successful professional careers.
18. Eastern New Mexico University
Number of Mention: 2
Average Ranking Across Sources: 10
Eastern New Mexico University has one of the best online graduate degree programs heading into 2024. The university offers a completely online Bachelor of Science in Mathematics degree as well as a Bachelor of Arts degree in math. Students can access the online recorded lectures via Canvas and Mediasite.
17. SUNY Brockport
Number of Mention: 2
Average Ranking Across Sources: 9.5
SUNY Brockport offers a variety of online math degree programs, including a Master of Science in Education: Adolescence Mathematics program. The program is designed for students aspiring to become teachers. The program requires 30 credits for completion and is accredited by the National Council for Accreditation of Teacher Education.
16. Texas A&M University
Number of Mention: 2
Average Ranking Across Sources: 9
Texas A&M University offers an online Master of Science in Mathematics program. 36 semester hours are required for the completion of the program. The program is accredited by the National Council for Accreditation of Teacher Education.
15. Central Methodist University
Number of Mention: 2
Average Ranking Across Sources: 9
The Central Methodist University offers a completely online MS in Mathematics degree program. 32 credit hours are required for graduation. The flexibility of online education enables students to get a higher education while managing other personal and professional commitments.
14. American Public University
Number of Mention: 2
Average Ranking Across Sources: 2.5
American Public University offers one of the best online graduate degree programs heading into 2024, including a Bachelor of Science in Mathematics program. The program requires 120 credit hours for completion. The program is accredited by the Middle States Commission on Higher Education.
13. Mercy University
Number of Mention: 3
Average Ranking Across Sources: 20.7
Mercy University offers a BA in Mathematics degree program. The program requires 120 credit hours for completion. The program is accredited by the Middle States Commission on Higher Education.
12. University of North Dakota
Number of Mention: 3
Average Ranking Across Sources: 13.7
University of North Dakota offers completely online Bachelor's in Mathematics and Master's in Mathematics. The online programs enable students to finish the courses at their own pace. The University of North Dakota is accredited by the Higher Learning Commission.
11. University of Illinois Springfield
Number of Mention: 3
Average Ranking Across Sources: 11.3
University of Illinois Springfield has one of the best online math degree programs heading into 2024. The university offers an online Bachelor of Arts in Mathematical Sciences program. The program is designed for transfer students who are anticipated to have approximately 60 credits from another institution.
10. Louisiana State University
Number of Mention: 3
Average Ranking Across Sources: 8.7
Louisiana State University has a completely online Bachelor of Science in Mathematics program. The program requires 120 credits for completion. The program provides a convenient way for students to learn on their schedule.
9. Thomas Edison State University
Number of Mention: 3
Average Ranking Across Sources: 8.7
Thomas Edison State University offers an online Bachelor of Arts in Mathematics program. The program requires the completion of 120 credit hours for graduation. The program is accredited by the Middle States Commission on Higher Education. The university also offers an Associate Degree in Natural Sciences and Mathematics program.
8. Maryville University
Number of Mention: 3
Average Ranking Across Sources: 8.3
Maryville University offers an online Bachelor of Science in Mathematics program. The program requires the completion of 120 credit hours. It is accredited by the Middle States Commission on Higher Education.
7. Indiana University East
Number of Mention: 3
Average Ranking Across Sources: 5.7
Indiana University East offers an online Bachelor of Science in Mathematics program. 120 credits are required for the completion of the program. Indiana University East also offers an online Undergraduate Certificate in Pure Mathematics program.
6. Bellevue University
Number of Mention: 3
Average Ranking Across Sources: 5
Bellevue University offers an online Bachelor of Science in Mathematics program. The program requires the completion of 120 credit hours. The program is accredited by the Higher Learning Commission.
Click to continue studying and see the 5 Best Online Math Degree Programs Heading Into 2024.
Disclosure: None. 25 Best Online Math Degree Programs Heading Into 2024 is originally published on Insider Monkey.
As businesses in all industries continue to grapple with inflation, economic volatility, geopolitical concerns and lingering supply chain issues, leaders are working diligently to increase revenue, deliver on customer experience expectations, and provide greater operational efficiency.Â
Software development is a core revenue driver for all businesses today due to the strong correlation between a successful Agile development team and great customer experiences. Consumers have very little patience for subpar experiences, which has led companies to be intensely focused on ensuring high-quality applications are being delivered. Unfortunately, software development life cycle (SDLC) bottlenecks due to quality engineering (QE) efforts can significantly delay time to market, opening the door for competition. At the same time, organizations are looking at ways to significantly reduce their IT operating costs. Fortunately, achieving the operational efficiency goals for the business does not have to come at the expense of quality and customer experience.
Automated testing processes enable teams to quickly and easily increase their productivity and decrease the risk for human errors within the SDLC. Test automation technology has been mature for the past decade. For the first time, with the advancements achieved with AI, QE teams are able to maintain the same pace as their software development counterparts and provide quick feedback, informing them if they will diminish the customer experience with the release of their code.
Application teams usually have two primary goals during a release cycle: (1) to not break the customer experience and (2) to make it better with the newly released code. There is greater focus on ensuring that the customer experience is not negatively impacted compared to the effort to ensure new features work. And thatâ€™s where test automation can not only help lower the TCO, but also do a much better job in ensuring the current customer experience is not broken compared to non-automated approaches.
There are six primary areas where successful companies are improving the total cost of ownership of software testing:
Shifting from manual to automated testing
By increasing the level of test automation in the software development life cycle, especially in regression testing, quality engineers can focus their efforts on defining the complex test scenarios for the new features being developed. This can be accomplished effortlessly with the latest iterations of AI tools. Zero-maintenance automated tests can be generated based on real user data, which means any impact to customer experience in the current code base will be identified prior to release in a fraction of the time compared to before.
Democratizing test automation through low-code/no-code solutionsÂ
The biggest barriers preventing a QE team from automating tests are the steep learning curve, the lack of time to undergo training, and the high cost of test automation engineers. Thatâ€™s where low-code/no-code automated testing solutions help QE teams create automated tests without requiring them to go through deep technical enablement. They can stay focused on leveraging their SME knowledge to build the best test coverage possible to avoid negative customer impacts, while decreasing the TCO by spending less time running slow, manual tests.
Identifying defects earlier in the testing cycle
When developers must fix a bug from code written several days earlier, it brings their productivity down. They have to fix old code instead of writing new code, and spend much time and effort to understand the previous codeâ€™s context before effectively fixing it. Having automated tests run as part of the Continuous Integration (CI) process ends context switching for developers. They receive immediate feedback on whether their new code is going to break customer experience (i.e. app regression). They can then immediately address issues before starting to work on the next story from the backlog, which directly translates to time and effort savings.
Consolidating point solutions within a comprehensive software quality platform
At the heart of any cost optimization effort is technology or tool rationalization. Reducing the number of tools and vendors in any IT ecosystem is proven to deliver savings while increasing team productivity. Having a common, all-inclusive platform to create, maintain, run, manage and analyze tests enables cross-team collaboration and reusing testing assets that would otherwise need to be re-created if each team was using their own point solutions. That directly drives down the software testing TCO, while promoting testing coverage across teams that minimize the impact on customer experiences.
Shifting testing environments to the cloud
When it comes to ensuring the best customer experience, companies look for running tests against the broadest variety of browsers and mobile devices, reflecting how users interact with the companyâ€™s applications. Building and maintaining the infrastructure to host those browsers and mobile devices is expensive and inefficient. Companies that choose a common, all-inclusive testing platform typically realize savings of 66% in software testing TCO, while delivering a much better customer experience with the broadest testing combination of browsers and mobile devices.
Applying AI across the lifecycle to accelerate time-to-value
The hype around AI is obfuscating the real use cases that can augment QE teams productivity through capabilities that (1) accelerate progress, (2) generate insights and (3) drive optimizations across the software testing lifecycle. One such AI-powered use case to lower software testing TCO is through automatically generating zero-maintenance regression tests. This enables QE teams to focus on new feature testing while still ensuring no impact on customer experiences on the next release.
Successfully managing software testing TCO in the current business landscape involves a strategic approach that balances cost efficiency without compromising quality and, subsequently, customer experience. By shifting towards automated testing, leveraging low-code/no-code solutions, identifying defects promptly, consolidating tools, migrating testing environments to the cloud, and harnessing the power of AI, companies can strategically streamline their software testing processes. This approach ultimately delivers exceptional customer experience while effectively managing the TCO of software testing amidst economic challenges and rapidly evolving market demands.
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