by Teknita Team | Sep 28, 2022 | Uncategorized
For software companies, it’s important to know whether the product developed matches the expectations. Building on that, API testing is all about checking whether the applications meet functionality, performance, reliability, and security expectations. API automation tests are critical components of successful testing. Automation is a crucial component for your development team to improve its efficiency.
API testing is software testing that involves testing application programming interfaces (APIs) to determine if they are working as expected. It can be conducted manually or automatically, and it is often used in conjunction with other types of testing, such as functional and regression testing.
API testing is an essential part of the software development process because it helps to ensure that APIs are working as expected and that they can handle the load that will be placed on them when the software is released. Additionally, it can help to identify potential security vulnerabilities in the API before the software is released.
API Testing Tools:
Testim
Testim is actually a full blown test automation platform, a subset of which includes API testing. The intriguing thing about Testim is that it uses artificial intelligence to help with test suite execution, maintenance, and even creation. Part of this, of course, involves testing your API.
Features
Obviously, the killer feature here is a framework that learns with each test suite execution and maintenance activity. But, on top of that, you can create API actions and validations for standard testing activities. But you can also isolate your UI testing by extracting data from your API and efficiently exercising the user interface. It’s a powerful way to test the UI and API in parallel.
Pricing
Testim has a freemium model, making it easy to get started for free. From there, you can customize the pricing to your team’s needs.
Postman
Postman came to the market initially as a Google Chrome plugin. Its main objective was to test API services. Currently, Postman has expanded its services for both Windows and Mac. So, whether you’re looking for exploratory or manual testing, it’s a great choice.
Features
With Postman, you can monitor the API, create automated tests, perform debugging, and run requests. Postman has the following characteristics:
– Its interface allows users to extract web API data.
– Postman enables writing Boolean tests and isn’t based on the command line.
– It comprises built-in tools, collections, and workspaces.
– It supports various formats, including RAML and Swagger.
Pricing
Postman is a free-to-use API testing tool. However, for additional features, it costs $12 for each user per month.
SoapUI
SoapUI enables the testing of web services REST and SOAP APIs. It’s a headless tool for functional testing that offers both a free package and a Fixed package.
Features
The free package grants users access to the full source code, and the Fixed package lets you take API testing a bit further. So, let’s take a look at the features of the two packages separately.
SoapUI Free Package
If you’re just beginning with automation or have a limited budget, the SoapUI free package is the way to go. Its primary features include the following:
– The ability to reuse security scans and load tests for functional test cases, thanks to reusable scripts
– Point-and-click and drag-and-drop functionality for simple and quick creation of tests
SoapUI Fixed Package
– REST, GraphQL, SOAP, JMS, and JDBC testing
– Support for asynchronous testing
– Data-driven testing
– Synthetic data generation
– Integrated API security testing
– As a result, more and more enterprises are opting for the Fixed package.
Pricing
The free package doesn’t include any costs. However, if users wish to avail themselves of the benefits of the “Fixed” version, the cost starts at $759 a year.
Apigee
Apigee is a cross-cloud API testing tool. Users can access its features using different editors, such as Swagger. It also enables measuring and testing of API performance.
Features
Apigee is a multistep tool powered by JavaScript with the following features:
– Identifies issues related to performance by tracking error rates, API traffic, and response times
– Enables creation of API proxies with the help of OpenAPI Specification and their deployment in the cloud
– Is compatible with APIs containing enormous data
Pricing
Apigee offers a free trial to users so that they can check its compatibility with specific requirements. The packages include Evaluation, Standard, Enterprise, and Enterprise Plus.
You can contact the sales team for each package’s price.
Assertible
Assertible’s main focus is reliability. It’s popular among recent developers and offers plenty of useful features.
Features
Assertible supports the automation of API tests at every step, from continuous integration to the delivery pipeline. It also has the following characteristics:
– Supports running of API tests after deployment
– Offers integration with tools such as Slack, GitHub, and Zapier
– Supports HTTP responses and their validation with turnkey assertions
Pricing
The standard version costs $25/month, the startup plan costs $50/month, and the business plan costs $100/month. There’s also a free personal plan if you want to try out Assertible yourself.
Karate DSL
Creating scenarios for API-based BDD tests has never been easier. Karate DSL helps users accomplish this without needing to write down step definitions. It also has already created those definitions, so users can get on with API testing as quickly as possible.
Features
Version 0.9.6 of Karate DSL includes async capability-based support for WebSocket. It offers easy-to-write tests for those who aren’t into core programming, as well as support for multithreaded parallel execution and configuration staging/switching. In addition, because the tool is built on top of Cucumber-JVM, you can execute test cases like any project developed using Java. Only, instead of Java, you have to write scripts in a language that makes it easier to work with JSON or XML.
Pricing
Karate DSL has open-source pricing. This means that it will provide the software free of charge. However, the solutions will have costs according to the requirements.
Rest Assured
Rest Assured is an API tool that facilitates easy testing of REST services. It’s an open-source tool and a Java domain-specific language designed to make REST testing simpler. Moreover, the latest version has fixed OSGi support-related issues. It also offers added support when it comes to using Apache Johnzon.
Features
Beginning with version 4.2.0, Rest Assured requires Java 8 or higher. Rest Assured has the following characteristics:
– Built-in functionalities ensure that users don’t need to carry out coding from scratch.
– Users don’t require extensive knowledge of HTTP.
– A single framework can have a combination of REST tests and UI.
– Seamless integration is possible with the Serenity automation framework.
– Provides several authentication mechanisms
Pricing
Rest Assured has open-source pricing. This implies that the software is free, but not the solutions.
JMeter
Created to perform load testing, JMeter is now popular for functional API testing. Moreover, JMeter 5.4 came out in December 2020 with additional bug fixes and core enhancements. The user experience is also better than the previous versions.
Features
JMeter is compatible with static and dynamic resources for testing performance. The integration between JMeter and Jenkins allows users to include API tests within CI pipelines. In addition, JMeter works with CSV files and enables teams to create unique parameter values for tests.
Pricing
JMeter has open-source pricing. The services come with a price tag, but the software is free to use.
API Fortress
API Fortress facilitates the building, execution, and automation of performance and functional testing. Subsequently, it’s the most powerful monitoring tool for SOAP and REST. The tool is also known for its ease of use.
Features
API Fortress helps developers eliminate redundancy and remove silos from a company by increasing transparency. Its UI is perfect for novices who have mediocre technical know-how. In addition, it offers simple, one-click test integration and is compatible with physical hardware and the cloud.
Pricing
The price of API Fortress ranges from $1,500 to $5,000 per year. How much an enterprise has to pay also depends on specific project needs.
Hoppscotch
Hoppscotch started as Postwoman, an open-source API testing tool that offers an alternative for the popular Postman API testing tool. Liyas Thomas has created the project, who initially announced the project on Hackernoon. More than one year later, the project has changed its name and accumulated over 26.000 stars on GitHub.
Features
Hoppscotch brands itself as a lightweight API testing tool with a minimalistic UI. The tool itself offers a complete set of functionality to make testing easier. Here are some features:
– Open a full-duplex communication channel over a single TCP connection
– Stream server sent events over an HTTP connection
– Send and receive data with a SocketIO server
– Subscribe and publish topics of an MQTT Broker
– Send GraphQL queries
– Just like Postman, you can access a history of previous requests, create collections for storing – API requests, or configure a proxy to access blocked APIs.
Pricing
Hoppscotch is free to use but accepts donations via PayPal or Patreon.
You can read more about API Testing Tools here.
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by Teknita Team | Sep 27, 2022 | Uncategorized
The shift left testing essentially means “test often, and start as early as possible.” The “shift left” testing movement is about pushing testing toward the early stages of software development. By testing early and often, a project can reduce the number of bugs and increase the quality of the code. The goal is to not find any critical bugs during the deployment phase that require code patching.
Shift-left testing advocates that testing should be placed at the start of development, instead of at its end. In true software agile development, there shouldn’t be phases, but instead continuous activities occurring in short, iterative cycles. Shift left testing in agile is all about small code increments. The agile methodology includes testing as an integral part of the shorter development cycle. Therefore, shift left testing fits nicely into the agile idea. The testing engineer has to perform testing after each code increment—often referred to as a two-week sprint.
Some organizations like to push shift left testing even further toward the coding phase. A good approach to adopt is test-driven development. Test-driven development requires you to first write the tests for the piece of code you want to develop. Therefore, you can immediately verify the validity of your code.
Another way of pushing testing further left includes the use of static analysis tools. A static analysis tool helps to identify problems with parameter types or incorrect usage of interfaces.
Furthermore, testing experts believe that behavior-driven development (BDD) can accelerate the shift left movement. BDD defines a common design language that can be understood by all stakeholders, such as product owners, testing engineers, and developers. Therefore, it enables all involved stakeholders to simultaneously work on the same product feature, accelerating the team’s agility.
Benefits of Shift Left Testing
So, what are the benefits of shift left testing?
- Find bugs early on in the software development life cycle
- Reduce the cost of solving bugs by detecting them early on
- Gain a higher-quality product as the code contains fewer patches and code fixes
- Have fewer chances that the product overshoots the estimated timeline
- Provide higher customer satisfaction as the code is stable and delivered within the budget
- Maintain higher-quality codebase
Another great benefit of shift left testing is the ability to use test automation tools. As we want to test early and often, test automation helps you accomplish this goal. We don’t want to overload our testing team with manually testing every new feature the development team introduces.
You can read more about Shift Left Testing here.
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by Teknita Team | Sep 26, 2022 | Uncategorized
Kubernetes is rapidly becoming the most important infrastructure platform in the modern IT environment. Known as K8s, it is an open-source system for automating deployment, scaling, and management of containerized applications.
How Kubernetes works
- When developers create a multi-container application, they plan out how all the parts fit and work together, how many of each component should run, and roughly what should happen when challenges (e.g., lots of users logging in at once) are encountered.
- They store their containerized application components in a container registry (local or remote) and capture this thinking in one or several text files comprising aconfiguration. To start the application, they “apply” the configuration to Kubernetes.
- Kubernetes job is to evaluate and implement this configuration and maintain it until told otherwise. It:
- Analyzes the configuration, aligning its requirements with those of all the other application configurations running on the system
- Finds resources appropriate for running the new containers (e.g., some containers might need resources like GPUs that aren’t present on every host)
- Grabs container images from the registry, starts up the new containers, and helps them connect to one another and to system resources (e.g., persistent storage), so the application works as a whole
- Then Kubernetes monitors everything, and when real events diverge from desired states, Kubernetes tries to fix things and adapt. For example, if a container crashes, Kubernetes restarts it. If an underlying server fails, Kubernetes finds resources elsewhere to run the containers that node was hosting. If traffic to an application suddenly spikes, Kubernetes can scale out containers to handle the additional load, in conformance to rules and limits stated in the configuration.
Here are five metrics to manage your Kubernetes environments.
Kubernetes Cluster Metrics
Monitoring the health of a Kubernetes cluster can help you understand the components that impact the health of your cluster. For example, you can learn how many resources the cluster uses as a whole and how many applications run on each node within the cluster. You can also learn whether your nodes are working well and at what capacity.
Here are several useful metrics to monitor:
- Node resource utilization—metrics such as network bandwidth, memory and CPU utilization, and disk utilization. You can use these metrics to find out if you should decrease or increase the number and size of cluster nodes.
- The number of nodes—this metric can help you learn what resources are being billed by the cloud provider and discover how the cluster is used.
- Running pods—by tracking the number of running pods, you can understand if the available nodes are sufficient to handle current workloads if a node fails.
Kubernetes Pod Metrics
The process of monitoring a Kubernetes pod can be divided into three components:
- Kubernetes metrics—these allow you to monitor how an individual pod is being handled and deployed by the orchestrator. You can monitor information such as the number of instances in a pod at a given moment compared to the expected number of instances (a lower number may indicate the cluster has run out of resources). You can also see in-progress deployment (the number of instances being switched to a newer version), check the health of your pods, and view network data.
- Pod container metrics—these are mostly available via cAdvisor and exposed through Heapster, which queries each node about the containers that are running. Important metrics include network, CPU, and memory usage, which can be compared with the maximum usage permitted.
- Application-specific metrics—these are developed by the actual application itself and relate to specific business rules. A database application, for example, will likely expose metrics on the state of an index, as well as relational statistics, while an eCommerce application might expose the data on the number of customers online and the revenue generated in a given timeframe. The application directly exposes these types of metrics, and you can link the app to a monitoring tool to track them more closely.
State Metrics
kube-state-metrics is a Kubernetes service that provides data on the state of cluster objects, including pods, nodes, namespaces, and DaemonSets. It serves metrics through the standard Kubernetes metrics API.
Here are several aspects you can monitor using state metrics:
- Persistent Volumes (PVs) – a PV is a storage resource specified on the cluster and made available as persistent storage for any pod that requests it. PVs are bound to a certain pod during their lifecycle. When the PV is no longer needed by the pod, it is reclaimed. Monitoring PVs can help you learn when reclamation processes fail, which signifies that something is not working properly with your persistent storage.
- Disk pressure—occurs when a node uses too much disk space or when a node uses disk space too quickly. Disk pressure is defined according to a configurable threshold. Monitoring this metric can help you learn if the application truly requires additional disk space or if it prematurely fills up the disk in an unanticipated manner.
- Crash loop—can happen when a pod starts, crashes, and then gets stuck in a loop of continuously trying to restart without success. When a crash loop occurs, the application cannot run. It may be caused by an application crashing within the pod, a pod misconfiguration, or a deployment issue. Since there are many possibilities, debugging a crash loop can be a tricky effort. However, you do need to learn of the crash immediately in order to quickly mitigate or implement emergency measures that can keep the application available.
- Jobs—components designed to temporarily run pods. A job can run pods for a limited amount of time. Once the pods complete their functions, the job can shut them down. Sometimes, though, jobs do not complete their function successfully. This may happen due to a node being rebooted or crashing. It may also be the result of resource exhaustion. Monitoring job failures can help you learn when your application is not accessible.
Container Metrics
You should monitor container metrics to ensure containers are properly utilizing resources. These metrics can help you understand if you are reaching a predefined resource limit and detect pods that are stuck in a CrashLoopBackoff.
Here are several container metrics that you should monitor:
- Container CPU usage—learn how much CPU resources your containers are using in relation to the pod limits you have defined.
- Container memory utilization—discover how much memory your containers are utilizing in relation to the pod limits you have defined.
- Network usage—detect sent and received data packets as well as how much bandwidth is being used.
Application Metrics
These metrics can help you measure the availability and performance of the applications running in pods. The business scope of the application determines the type of metrics provided. Here are several important metrics:
- Application availability—can help you measure the uptime and response times of the application. This metric can help you assess optimal user experience and performance.
- Application health and performance—can help you learn about performance issues, latency, responsiveness, and other user experience issues. This metric can surface errors that should be fixed within the application layer.
You can read more about Kubernetes Monitoring here.
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by Teknita Team | Sep 23, 2022 | Uncategorized
1. Count the fails
It’s not the age of the system, necessarily, that is the biggest problem. Where it fails to do your bidding is the real issue.
“The first step in modernizing your IT system is to identify the specific failings of your current legacy system,” says Mo Hafez, senior solutions engineer at Expereo, an internet, cloud connectivity and SD-WAN provider. “Whether your specific problems or concerns are security, infrastructure, or a combination of those problems, identifying them early will ensure that your modernization efforts will be as efficient as possible.”
“The hardest, most challenging part involves setting your and your team’s expectations. A transformation requires taking little bites, one area at a time,” says Philip Morehead, director of product at Nexient, an agile software development provider.
2. Compare apples to barrels
Once you’ve identified where the failures are in aging systems, compute the costs in fixes, patches, upgrades, and add-ons to bring the system up to modern requirements. Now add any additional costs likely to be incurred in the near future to keep this system going. Compare the total to other available options, including a new or newer system.
“While this isn’t a one-size-fits-all approach, the last 2.5 years have proven just how quickly priorities can change,” says Brian Haines, chief strategy officer for FM:Systems, an integrated workspace management system software provider. “Rather than investing in point solutions that may serve the specific needs of the organization today, a workplace tech solution that offers the ability to add or even remove certain functions later to the same system means organizations can more efficiently respond to ever-changing business, employee, workplace, visitor and even asset needs going forward.”
3. Accelerate the automation
Make smart automation plans a part of your overall implementation strategy for modernizing your legacy systems.
“When it comes to automation, it’s all about building value to drive value. To modernize aging systems, there has to be a proactive approach to automation and understanding the ripple effects that come with it — then training for them across,” says Karlo Bustos, vice-president of Professional Services at Board Americas, a decision-making platform provider.
4. Do a madness check
You’re not saddled with legacy systems because you have a fetish for old and cranky tech. It’s much more likely that you inherited that bag of treachery, became a victim of way too many budget cuts, or got sucked into a black-hole mandate. Other types of madness may also be to blame.
“A significant challenge for IT experts is that some organizations have been previously unable to replace legacy systems due to regulatory or organizational mandates,” says Rod Simmons, vice president of product strategy at Omada, a provider of Identity Governance and Administration (IGA) software. “Many organizations also succumb to the ‘sunk cost’ fallacy. They’ve invested so much time, money and energy into legacy systems that are barely working. Not to mention they are spending so much time trying to make what they have work, that it feels impossible to consider how things could be better.”
5. Get new keys
When you modernize legacy tech you can accidently create a few more gaps in its security. One such security flaw can spring from reusing old security keys. Either the keys themselves are already compromised, or you forget to destroy them when you get or make new keys and the old ones get compromised later.
Current encryption keys may be enough for now, given their enormous size and the inherent difficulty in cracking them. However, harvesting attackers are very patient and can be sitting on your system waiting on quantum computing to come online. If that’s a concern for your company, you may want to investigate the quantum keys that are already available.
While you’re tinkering around to make the system better, fit it with new security keys of some type, pay attention to whom you give assess to these new keys, and destroy the old keys.
6. Be fickle about partners
The reality is that you’ll need more partners and sometimes different partners as dictated by the needs of your business over time. There is no discernable advantage to being overly loyal or sentimental about any given partner, no matter how familiar or how cozy the relationship in the past.
Also look for ways to replace or augment partners with automation, AI, or simplified functionality.
7. Decouple data
Legacy applications and platforms are of the data silos. This is a potentially fatal flaw for any effort to modernize or optimize now — and going forward. So, look hard at freeing up that data and breaking down silos everywhere you can.
“De-couple data stores that are used by many monolithic applications and consolidate behind enterprise accessible services such as APIs,” advises Mark Schlesinger, senior technical fellow at Broadridge Financial Solutions.
Break all the black boxes, too.
“Mainframes are often called ‘black boxes’ of info for a reason: They’re webs of personalized code that have been managed by countless developer hands that have either exited their posts or retired altogether,” says Tim Jones, managing director of application modernization at Advanced, an international provider of application modernization services.
You may need a partner that is an expert in this type of black box cracking to help you get this done.
8. Double down on containers
Containers can make modernization easier, but they can also be used to double your deployments quickly and efficiently.
“Use containers in lower-level public cloud environments to build products that will be deployed to production on the private cloud, as well as for products that will be deployed in production to the public cloud when time to market is critical and/or when future portability is expected to be necessary,” says Mark Schlesinger, senior technical fellow at Broadridge Financial Solutions.
9. Reach for new tools even to fix old tech
Most modernization projects these days are too big to do fast and yet must be completed quickly. Your set of familiar tools may not be enough to get you across the finish line in time. Don’t hesitate to reach for new tools to make the work quicker.
“By utilizing modern IT models, new approaches to IT like DevOps or site reliability engineering [SRE], and particularly new advancements in technology like AIOps, more IT teams are leveraging AI-driven intelligence and automation to make quick and accurate decisions, allowing them to deliver resiliency despite immense pressures,” says Dinesh Nirmal, general manager for IBM Automation.
You can read more about Modernizing Aging IT Systems here.
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by Teknita Team | Sep 22, 2022 | Uncategorized
There are six areas of innovation that are already impacting, or are soon likely to impact, how business is managed and accomplished in our increasingly digital world:
– Cybersecurity.
As organizations deal with the risks and vulnerabilities posed by digital transformation, cybersecurity is required to continue to advance to keep pace with continually evolving and increasingly sophisticated cybercrime methods. Artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) can help detect malware and ransomware. For instance, advanced algorithms are aiding in tighter security, especially when combined with automation tools.
– Wireless 5G technology.
5G is a prerequisite for our continued digital transformation, with global 5G smartphone subscriptions anticipated to reach 1B in 2022, and over 3B by 2026. 5G allows for 10-20 times higher data speeds, significantly greater device connectivity, and a greatly improved user experience with little to no lag times. 5G is also essential for supporting the continued advancement of augmented and virtual reality (AR/VR), which are destined to be game changers for how we work, live, and play.
– Artificial intelligence.
Already a component of the aforementioned cybersecurity, AI’s footprint is much broader and with equally deep impact in other business areas, with the market projected to reach over $126B by 2025. At a high level, AI-based business tools allow organizations to forecast more precisely, in turn helping to improve operations, project sales and revenue more accurately, and recognize market trends much faster.
– Natural language processing.
Centered on interactions between computers and the human language, NLP is one of the most interesting and widely used AI technologies (think Siri and Alexa virtual assistants as well-known examples). However, NLP is still formative, and as advancements continue, companies will create machines with the ability to engage with humans in a way that will disrupt multiple aspects of our business and personal lives in ways many might currently deem unimaginable.
– Metaverse.
The virtual world where people can work, play, and interact with one another through immersive online experiences will continue to see its popularity and sophistication grow. AR/VR gaming and digital marketplaces that include livestream shopping, virtual art galleries, and digital real estate (the latter alone surpassing over $500M in 2021 and expected to more than double to over $1B in 2022) will continue to expand and integrate with social networks over the next several years. Again, AI and ML technologies will assist with powering the metaverse forward.
– Web3.
Something of a rebrand of blockchain technology, Web3 offers benefits to businesses and individuals, including complete ownership of data, the ability to allow users access to their data across multiple apps, and full data encryption allowing for enhanced security and greater transparency. Decentralized systems enabled by Web3 are also benefiting creators and artists who can leverage non-fungible tokens (NFTs) to market their products for fair earnings, shared ownership, and autonomy. As the “creator” economy grows along with Web3, more people will be enabled to build and create.
As digital transformation continues to disrupt how we do business, organizations must be constantly aware of new technologies in order to learn what to adapt and when, as it makes the most sense for their organizational roadmap and vision.
Of these technologies, advanced cybersecurity solutions are already a “must have” to ensure full customer data protection. Other technologies, such as 5G and AI/ML, are also becoming more widely available and may soon join the category of innovations that organizations must integrate in order to thrive from a competitive and customer satisfaction perspective.
You can read more about Digital Transformation here.
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