There are several reasons why software programs fail, and some basic best practices can be employed to minimize the likelihood of that happening. They include the following:
Implementing load balancing. As the number of website users increase and they log on to add their personal data, a crash can impact other features, like access to the bank they hope to draw from when they check out. Think “Black Friday” and what happened when websites were not equipped to handle shopper traffic. On an e-commerce website when the number of users increases sharply to take advantage of an online offer that could potentially cause a crash, that can impact other features, like access to the payment page when they check out. Avoid a single point of failure by load balancing system traffic across multiple server locations.
Applying program scaling. This is the ability of a program’s application nodes to automatically adjust and ramp up to handle increased traffic via machine learning, as it analyzes the metrics on a real time basis. Scheduled scaling can be employed during forecasted peak hours or for special sale events, such as Amazon Prime Day. At off-peak hours, those nodes then can be scaled down. Dynamic scaling involves software changes based on metrics including CPU utilization and memory. Predictive scaling entails understanding current and forecasted future needs, utilizing machine learning modules and system monitoring.
Using continuous load and stress testing to ensure reliability of the code. Build a software program with a high degree of availability in mind, accessible every day of the year with a miniscule period of downtime. Even one hour offline a year can be costly. Employ chaos engineering during the development and beta testing stage, introducing worst-case scenarios when it comes to the load on a system. Then write a program to overcome those issues without resorting to downtime.
Developing a backup plan and program for redundancy. It’s crucial to be able to replicate and recover data in the event of a crash. Instill this type of business ethic within the corporate structure.
Monitoring a system’s performance using metrics and observation. Note any variance from the norm and take immediate action where needed. A word of caution: the most common reason for software failure is the introduction of a change to the operating system in production.
One Step at a Time
The first step in developing a software program is choosing the right type of architecture. Using the wrong type can lead to costly downtime and can discourage end users from returning for a second visit if other sites or apps offer the same products and services.
The second step is to incorporate key features including the ability to scale as demand on the program peaks (perhaps a popular retail site having a sale), redundancy that allows a backup component to takeover in case of a failure, and the need for continuous system testing.
The final step is to establish standards of high availability and high expectations where downtime is not an option. Following these steps creates a template to design better system applications that are reliable in all but the rarest of circumstances.
You can read more about Best Practices for Avoiding Software Failures here.
Nexlogica has the expert resources to support all your technology initiatives. We are always happy to hear from you.
A test case is a written document that gives detailed step-by-step instructions on how to perform a given test for a software feature.
A test case typically features:
the conditions necessary for the start of the test
one or more inputs, if any, for the test
the action that is to be performed
the results of the test, which may consist of outputs or changes in the conditions of the “world”
A test case resides at the tactical level. It tells the tester what they need to do and in what order, details the outcomes they should expect.
A test scenario is a more high-level description of a given concern that needs to be tested. Rather than being a step-by-step guide, test scenarios describe testing needs in very broad strokes.
Test scenarios typically live at the strategic level, which means they care about the why rather than the how. They’ll typically express the business motivation and rationale behind testing a given feature.
Test scenarios give origin to test cases.
You’ll typically have way more test cases than test scenarios as a logical consequence. The creation of test cases is typically more labor-intensive due to the granularity of details involved. But the creation of test scenarios can oftentimes be harder since it has a degree of ambiguity and uncertainty involved and needs to connect to the business in a way that makes sense ROI-wise.
Test Case
Test Scenario
Tactical level
Strategic level
Cares about the how
Cares about the why
There are many per test scenario.
One test scenario gives origin to many test cases.
It’s executed by a tester, QA professional, or developer.
It can’t be executed but serves as higher-level guidance for decision-making.
It can be automated with the help of a code-based or codeless test automation tool
It can’t be automated because it’s not a set of steps
It can be labor-intensive to elaborate on but is objective and unambiguous
Can have some degree of ambiguity
You can read more about Test Cases and Scenarios here.
Nexlogica has the expert resources to support all your technology initiatives. We are always happy to hear from you.
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.
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.
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.
Nexlogica has the expert resources to support all your technology initiatives. We are always happy to hear from you.
