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Why do you need Test Automation in 2021!

You cannot talk about the latest practices like DevOps, Continuous Delivery and Continuous Testing without the term Automation. With the power of automation, Testing has taken a ‘moonshot’, transforming the way the world looks at Testing.

In a traditional software development environment, testing would get completed at the end of the development cycle.

Also, manual testing is time-consuming and prone to errors. Organizations are now translating their manual test cases to automated testing to achieve Quality-at-Speed, lower the risk factor and improve the test coverage.

Agile and DevOps have changed the way the code is developed, tested and deployed. Development and QA teams are under tremendous pressure to have a working version of the software within each sprint. The software is always in development and must be deployment-ready at all instances making testing mandatory at every stage of the delivery pipeline. And Continuous Testing and Test Automation are the keys to its success.

According to the World Quality Report, there is an increase in the adoption of Agile and DevOps. With the growing risk and complexity of software development, teams are on the hook to keep quality high. This has resulted in QA teams becoming orchestrators of quality. So What exactly is Test Automation?

Test Automation is a method in software testing that leverages automation tools to control the execution of tests. Test automation is also called automated testing or automated QA testing.

According to the MarketsAndMarkets report, “the global test automation market size is anticipated to rise from USD 12.6 Billion in 2019 to USD 28.8 Billion by 2024 at a CAGR (Compound Annual Growth Rate) of 18.0 per cent during the prediction period”.

Types of Automation Tests

Let us look at the types of tests that can be automated and how they help test every aspect of the system enabling users to achieve a frictionless experience.

Unit Tests: How well does your single unit of code work independently?

Integration and API Tests: How well does your system work when all units are integrated? Does it make or break the system? How well does your system work with external systems?

Regression Tests: How well does your system work when you add a new feature to your existing one? Has your software “regressed” after the new update?

Every time you add a new feature or functionality, you need to check if there is any impact on the existing features

Performance Tests: How well do your software handle load and stress? How well is the responsiveness and stability of your software? Smoke Tests: How well is your software’s stability? How well are your software’s functionalities working? Are there any showstoppers? Security Tests: How well is your software secure from any vulnerabilities?

Click here to read more : Automation testing

If this has picked your interest and you want to know more, then please click here and schedule a demo, or reach out to us at info@webomates.com. We have more exciting articles coming up every week.

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Tags: Continous Testing, Non functional testing, Test Automation

Managing QA Cost By Adopting Intelligent Test Automation

The current IT landscape demands faster time to market with a high level of customer satisfaction, but in a cost-efficient manner.

Needless to say, software testing becomes a critical activity to accelerate releases. Test automation has helped in achieving this goal to some extent, especially in terms of time management and quality assurance. However, it still needs to do better when it comes to cost-effectiveness and overall efficiency.

That is where intelligent test automation steps in. We have discussed the benefits of intelligent test automation in detail in our blog.

Intelligent test automation augments the automation testing process with the benefits of AI and ML algorithms making it more intuitive, efficient, and reliable. It introduces the capabilities of predictive analysis based on the past testing data and new information made available to it in terms of functional and technical specifications. Factors driving the cost in software testing

As per the world quality report 2021–2022 by Capgemini, the global pandemic has spurred digital transformation programs worldwide, leading to the rise in demand for high-quality and reliable applications/products. Consequently, the pressure on the quality assurance budget has increased.

In the latest study conducted by Gartner Inc. for technology-related budgets, worldwide IT spending is projected to total $4.2 trillion in 2021, an increase of 8.6% from 2020.

While the external factors give a larger picture, there are certain factors in software testing which drive the cost of quality assurance activity and have a direct effect on the IT budgets.

Flaws in test automation Test automation has proved beneficial for software testing, however, increasing demand and the need for speed with superior quality have exposed certain chinks in the armor. For instance, false failure reports slow down the testing process. We have covered this in detail in our blog “Bane of automation-false failures”.

Also, test cases and test data maintenance become a major headache with every test cycle. Updating test cases based on previous test results, or change in specifications becomes tough and is prone to errors. The same is the case with updating test data. Any change in specifications may lead to modification in test inputs too. Keeping pace with rapid changes and testing multiple test conditions with multiple data combinations is an enormous task.

All these issues add to the overall project cost since the efforts involved are high and there might be delays due to tracking & correcting false failures and test maintenance activity. Discovering bugs late in the testing cycle

Sometimes, the bug is discovered late in the test cycle. Reasons could be any, outdated or limited test data, unexpected error due to an untested path because of limited test cases. Discovering bugs late in the testing cycle has a cascading effect on the project quality and schedule, which puts the whole estimated QA cost in peril.

Shift left testing coupled with the power of AI addresses this issue to a major extent. Read our blog “Intelligent analytics with AI” for a better understanding of the product life cycle and bug discovery. Improper test planning

Impeccable planning is the key to the success of any project. However, the lack of collaboration and communication between business and technical teams may riddle the project with delays. And the cost goes high since time is money.

How intelligent test automation manages cost

As stated in the previous section, the organizations are earmarking bigger IT budgets, so naturally, the general expectation is to maximize the benefits. Investing in AI-augmented software development is another aspect that is gaining importance due to its efficiency, reliability, and better ROI.

The findings of another survey by Gartner Inc. for the increase in industry-wide funding for AI in 2022 are shared below. Organizations are heavily investing in AI and there is a good reason for it.

The stakes involved are pretty high.Read More about : Intelligent test automation If this has picked your interest and you want to know more, then please click here and schedule a demo, or reach out to us at info@webomates.com. We have more exciting articles coming up every week. Stay tuned and like/follow us at LinkedIn — Webomates LinkedIn Page Facebook — Webomates Facebook page For More Information visit us at : webomates.com Tags: Artificial Intelligence, Automation Testing, Intelligent Analytics, IntelligentAnalytics

Enhancing the Value of Black box Testing with AI Test Automation

Black box testing, also known as functional testingor behavioral testing, requires the testers to evaluate the functionality and usability of the software without delving deeper into the details of the code. Black box testing verifies and validates the functionality of the software from an end-user perspective, and also checks for any interface issues.

Black box testing has many advantages that work in favor of the testing teams, however, it comes with certain baggage too. The following table lists the pros and cons of black-box testing and the entailing section elaborates on how AI test automation enhances its value by working on its drawbacks and improvising it further to provide a holistic testing setup.

AI test automation — The facilitator for enhancing the value of Black box testing

In an increasingly agile environment and continuous testing, the drawbacks of Black-box testing may impede overall feature velocity. Test automation addresses this issue to a certain extent and has marked benefits in terms of accuracy, scalability, dependability, enhanced test coverage, time, and effort saving. AI automation further adds the dimension of machine learning capabilities to test automation by emulating human behavior of understanding, learning, and implementing, without any actual human involvement. It can spot anomalies, learn from patterns, analyze the data, and then if required, can upgrade the test scripts (self-healing).

Improved test coverage, accuracy, and reliability AI test automation applies machine learning to identify various logical paths and test conditions to generate the test cases. AI-assisted testing tools can also identify error-prone areas and lay extra emphasis on designing test cases for the same. This aids in having comprehensive test coverage.

AI test automation helps in validating the test cases for a variety of test inputs for all the identified test conditions in the module under test. AI testing tools constantly learn and update their knowledge base with every test cycle based on test result analysis. They apply that knowledge to improve software testing by detecting even minor changes and update the test scenarios accordingly. Webomates CQ applies AI and ML algorithms to its self-healing test automation framework to dynamically understand the changes made to the application and modifies the testing scope accordingly. Our AI codeless engine effortlessly modifies (heal) the test cases, scripts and re-executes them within the same test cycle. Read more about self-healing in our blog “Self-healing — Automate the automation”.

Efficient defect management AI test automation has efficient defect management with effective defect tracking capabilities and detailed reporting with analysis. Improved defect traceability and defect prediction contribute to optimizing the test strategies. Also, false positives in testing slow down the development speed. Webomates’ ingenious AI defect predictor can identify false positives with 99% accuracy. It not only differentiates true failures from false failures but also helps in creating a defect using the AI engine for True Failures.

Our AI engine also predicts possible defects based on the test result analysis and facilitates the generation of new test cases, thus ensuring a good test coverage.

Read more about defect management and intelligent analytics in our blogs “Defect triaging” and “Intelligent analytics with AI”. It will give you detailed insight and a deeper understanding of how Webomates CQ handles the defects and analyzes them to generate detailed reports for all the stakeholders, thus expediting the testing process by making it more efficient.

Superior test failure analysis Intelligent test automation and intelligent analytics help the testing and development teams to have a better insight into the impact of code changes and risks associated with those changes. Appropriate actions can be taken based on these insights and issues, if any, can be intercepted much earlier in test cycles.

Webomates’ patented AI Test Package Analyzer identifies all the test cases which are impacted due to a defect and traces them to impacted user stories/epics/requirements to identify the exact origin of the defect. This helps in understanding the root cause of the issue. For more details about test failure analysis, read our blog “Test failure analysis with AI”.

Read More about : Ai test automation

If this has picked your interest and you want to know more, then please click here and schedule a demo, or reach out to us at info@webomates.com. We have more exciting articles coming up every week.

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Top 5 Reasons Why You Need AI Testing in Financial Sector

The Perils of ignoring Quality Testing

quality-testing The Growing Need for a Digital Facelift across Fintech Trust is the new currency!

Adoption of AI and ML across the banking, financial services, and insurance (BFSI) or the Financial technology (Fintech) industry space is advancing at an unprecedented rate. And simplifying the customer journey is currently the prime focus of any financial or banking institution.

Banking is all about speed and security – hence there is the need to deliver rapid upgrades to existing products and services and launch new offerings to meet the ever increasing customer demands.

According to Globenewswire, the global mobile banking market was valued at $715.3 million in 2018 and is expected to reach $1,824.7 million by 2026, registering a CAGR of 12.2% from 2019-2026.

Potential vulnerabilities of Fintech Data Confidentiality and Security Breaches

Strong Regulatory compliance

Complicated Transactions Processing Lifecycle

Application availability to all potential users.

Compatibility with a range of devices, platforms, networks, and operating systems

The Equifax Catastrophe Did you know that Equifax curtailed their regression and as a result did not discover a security vulnerability? This resulted in a data breach that exposed the personal information of 147 million people! Security experts found an unknown hacking group trying to find websites with security flaws so as to find a system to exploit.

Banking security is always top-notch. This article examines how embracing Webomates’ Intelligent Automation Testing powered by AI and ML technologies helps to keep your system safe and secure.

The only industry with a higher security can be the Department of Defense… And we help them keep their systems secure too!

As per Gartner, here’s how the customer’s expect to transact: While on vacation – relaxing on a beach

From the convenience of their home

Banking on the go

And what are the new gen customers expecting from the banking firms? An exceptional personalized experience to have a smooth transaction processing

Secure and reliable web and mobile banking with no downtime

Instant application loading and seamless performance

With such increased pressure and operational complexity, the banking institutions recognized that a change was needed.

What needs to be changed? The traditional approaches to Quality Assurance that are no longer beneficial. Why? To deliver better software faster that’s safe and secure. How? Perform end-to-end application testing powered by AI ML tools.

With automation becoming increasingly embedded in improving efficiency, manual processes are either being augmented or replaced by systems equipped with artificial intelligence (AI) and machine learning (ML) capabilities.

Why Intelligent Test Automation for Financial Companies is Necessary Fintech software relies on rock-solid testing, Intelligent automation testing and QA processes to ensure high product quality. Club that with DevOps, Continuous Testing and Shift-Left testing and you can be sure of quality releases, everytime!

software relies Ensuring Conformance to PCI DSSCompliance Requirements

Fintech companies are highly regulated industries and have to meet compliance standards like PCI DSS (Payment Card Industry Data Security Standard) international security standards to secure their customers’ data and privacy.

Webomates CQ portal’s reporting & auditing capability makes sure the customer platform is compliant.

Optimizing the customer experience by Functional Testing and AI-driven exploratory testing

How well does your system work when you add a new feature to your existing application? Does it make or break the system?

It is of utmost importance to perform core functionality checks through Regression Testing for every release.

We Test as you Develop. Using its advanced AI, Webomates CQ creates up to 2,000 test cases in 4 weeks that are relevant, appropriate for the existing release and will be relevant for the new release and software version.

By combining Exploratory Testing with regression testing, it helps you scrutinize the potential risks. The basic aim of exploratory testing is to pinpoint how a feature works under various conditions and that could be against different devices, browsers or operating systems as well.

Create exceptional digital experience by Performance Testing

Come salary day, and that brings in a spike in the usual traffic on the app. Combine that with other events like the end of the financial year or a festive season, and all hell can break loose.

With the number of users each banking app has, Performance Testing is a must to ensure that the app functions properly under different load conditions and the customers are not affected by any performance failures.

Protecting data by thorough Security Testing

Failure or vulnerabilities anywhere along the payment process could lead to disaster. If a vulnerability exists in a financial software program, hackers could gain access and exploit sensitive user data.

Security testing ensures that the application does not have any such web vulnerability that can expose sensitive data to an intruder or an attacker.

Webomates helps you to shift left your testing and provides an effective means to perform testing in parallel to development processes, enabling faster defect identification and resolution.

Managing test data and ensuring test optimization

Data management is another big aspect of testing with the numerous kinds of testing on huge volumes of data across multiple environments. Performance, load, stress testing require humongous amounts of data.

Our comprehensive test automation solution facilitates the regular updating of test data with minimal human intervention. The platform is designed to reduce test cycle duration and mission critical defects by more than 50% by applying AI and ML to software testing. Read More about : Ai testing in financial sector

Requirements traceability is a testing artifact that keeps track of all the user requirements and the details of the test cases mapped to each of those requirements. It serves as a documented proof that all the requirements have been accounted for and validated to achieve their end purpose.

Tags: AI Testing, Automation testing in Banking, Automation testing in Fintech, Intelligent Test Automation

How Long does a Kitchen Remodeling take?

The kitchen is the most prominent part of the home that reflects your lifestyle. A Kitchen must be beautifully adorned and must have updated appliances to make your home look trendy. If you are planning the Kitchen Remodeling for your home then we are here with some expert tips to make the process smooth and easy for you. The most important question is when you plan the Kitchen remodeling that how long it would take?

There are many opinions and blogs are available online about how much time Kitchen Remodeling takes but there’s no fix and finite time for the remodeling process as it depends on the various factors like size and scope of the kitchen, the number of doors/windows, appliances, cabinets, the type of contractor, etc. If you are tackling a complete kitchen remodel project, then you should expect to spend at least three weeks to accomplish the tasks. On the other hand, the length of the kitchen remodel is determined by the size of the kitchen. A bigger kitchen remodel will take more time to complete.

You may find a lot of different estimations to remodel a kitchen, from 1 week to 3 or 4 months even as high as 6 months. But the answer isn’t as simple as several days, weeks, or months because it varies depending on the style being changed, as well as the size of the space as well as how much is going to be spent too.

Can you remodel your kitchen yourself?

Fortunately, you can do DIY kitchen remodeling if you have the skills and time spare. You can save money by doing it yourself because then there are no contracting fees involved. There are definite perks to be gained if you take the time to plan your project properly — including significant cost savings! Along with members of your family or friends who are also handy, doing the remodel yourself proves to be quite an enjoyable initiative. However, there might be components that require outside help, especially those related to plumbing and electrical functions.

For these components, we recommend that you hire a professional. Although there’s usually a fee involved for their services and insights into how things perform and should be upgraded, hiring them will ensure Kitchen Additions Perth offers better results efficiently so as not to waste resources or time spent on trial-and-error sessions due to lack of experience in such tasks. What are the important steps of Kitchen Remodeling? Following are the most important steps of a typical kitchen remodeling:

  1. Assess your needs and wants: At the very first step, get to know why you want to remodel your kitchen and get a clear idea of the things you want to have in your remodeled kitchen. Be prepared with your design inspiration and some sample ideas.

  2. Hire a Contractor or DIY: Decide whether you want to remodel your kitchen yourself or want to hire a Kitchen Remodeling Contractor. If you have enough time and skill then DIY kitchen remodeling is a very budget-friendly idea but still, there are some areas where you would need professional help. If you are hiring a contractor then know the tips to hire a remodeling contractor.

  3. Planning, Design, and Budget: Once you hire the contractor, plan everything in detail with your kitchen remodeling contractor. Express every desire and every small detail that you want to have in your final result to avoid confusion. Take the help of your contractor to prepare the budget and always keep an emergency budget ready to avoid delays in the process.

  4. Demolition of existing Kitchen: After having planned everything out in your head and with all your decisions in front of you, the old kitchen in your home must be demolished. This process can sometimes take less than a week, but this will vary depending on the size of the kitchen. It includes removing the old cabinets, countertops, lighting, appliances, and flooring. Be mindful to turn off the power if there is any possibility that it will pose a threat to you or anyone else that may enter into your home during this demolition period!

  5. Structural Changes and Mechanical Installation: If you’ve got in mind some major structural changes like removing a door or wall, then I would need at least 3–4 days to a week If it’s merely mechanical installation then it would take 2–5 days involving the plumbing, electrical installation, gas lines, drains, water lines.

  6. Drywall, paint, lighting, and flooring are the next crucial steps in the remodeling process.

Once the drywall is installed, it’s time for priming and painting on top of that. The standard amount of time to get both of these tasks done is around three days though it all depends on the type of paint that will be used.

Just because you paint before installing cabinets doesn’t mean that they can go up right away because once there’s something on the walls, you may miss any flaws with lights or outlets. Lighting can also sometimes take two days to install along with floors which often require more time depending upon what type was chosen for example laminate or ceramic tile flooring needs a longer amount of time since they are larger surfaces compared with hardwood.

  1. Cabinets and Countertops: It’s the exciting part of kitchen remodeling, especially if you can finally stop shopping for new cabinets!

This also indicates that we are near the end of the whole journey because if your cabinets are already measured and prepared which takes a few months, then the installation will be quick and easy within 3 to 5 days. On the other hand, countertop installation takes a much longer time. We need to understand that because they must be cut accurately to accommodate appliances or faucets, it requires great patience to finalize this task.

And finally, install the new appliances that would take approximately 1 week, and then clean the whole area to witness your dream kitchen. CMAC Construction is one of the best Kitchen Remodeling Contractors in the Bay Area. We are delivering the desired designs to our clients all across the Bay Area. If you are planning to remodel your Kitchen near Bay Area then contact us for a Free Estimate Now.

CMAC Construction is one of the best Marin County General Contractors that specializes in new builds, renovations and improvements.

They provide the best services for Home Remodeling, kitchen remodeling, bathroom remodeling, staircase installation, flooring installation and termite remediation. If you are looking for Marin County General Contractors then don’t look further, call CMAC Contruction and get a free estimate.

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The Future of Intelligent Automation

Intelligent automation (IA) is a technique to automate predefined repetitive testing tasks, using various test automation tools and testing scripts.

The Future of Intelligent Automation report by Gartner, reported that “Intelligent automation provides huge potential for greater productivity and efficiency in application testing, at a lower cost.”

Test automation is the key to continuous testing and has marked benefits in accuracy, scalability, dependability, enhanced test coverage, time, and effort saving. Intelligent automation contributes to the enormous potential for higher productivity, and efficiency in application testing at a lower cost. The Future of Intelligent Automation report by Gartner, also reported that by 2022, 40% of application development (AD) projects will use AI-enabled test set optimizers that build, maintain, run and optimize test assets.”

Next innovation in QA intelligent automation and productivity.

Why does it matter?

By working with application leaders to explore IA use cases, such as test optimization, defect prediction, model-based testing, test data generation, and test insights, the team increases application testing agility. Incorporating intelligent automation into application testing services for both new proposals and existing contracts optimizes application testing costs.

Applying intelligent automation provides an incredible improvement in quality and an increase in application testing speed. An effective IA testing considers all the changes due to bug fixes or introducing a new feature. Besides modifying and executing the affected test cases and scripts (often referred to as healing) there has to be a service level guarantee that all possible scenarios are covered. The cherry on the top will be if the time and cost are not affected much.

Webomates provides guaranteed regression testing of 24 hours for FULL services, 8 hours of Overnight services, and quick Smoke regression, which gets completed within 15 minutes to a maximum of 1 hour. Our test model ensures that all the relevant test cases are self-healed and retested to reflect any changes in the build release and provide a True Pass and True Failure report. Read More about : Test automation

Tags: Continous Testing, Non functional testing, Test Automation

Test Analytics with AI-Based Automation

What are software testing metrics?

As a project manager if you want to know how productive your team is. The answer “very productive” sounds good, but not enough to satisfy the higher management and business unit owners. They want to see the numbers to substantiate these claims. Similarly, when asked about project progress, the answer “as per the schedule” is not the right answer when it comes to presenting it to the people who matter, i.e. customers. They want to see some tangible progress on board.

“What about the health of the project?” — “Very few defects here and there” will not cut it. Where are the numbers? That is where software testing metrics step in.

Software testing metrics are quantifiable measures that help the business and technical stakeholders to determine a software project’s progress, quality, and people/process efficiency.

But, throwing in just some random numbers won’t help your cause unless they are in strong context with the project under development. It is important to consider the type of project, requirements, timelines, budget, and of course the quality process too before defining the metrics.

Webomates is the API automation platform that enables companies to load, monitor and automate web APIs. These APIs may be public or private and can be used to control connected devices, integrate SaaS solutions, and drive business processes.

Types of software metrics Software metrics can be broadly classified into 3 categories.

Project Metrics: These metrics help in evaluating project status, estimating variance in schedule/cost, assessing productivity, and quantifying defects to measure the project’s health. Product Metrics: These metrics are based on product specifics like size, complexity, performance, etc. These are measured at different stages throughout the software development to ensure that the product is being developed as per the requirements.

Process Metrics: These are used to measure the efficiency of the software testing process based on the net outcome in terms of defect-related numbers within the specified timeline and budget. These numbers help in optimizing the process further for improved results.

For every metric type usually, there is a base metric and derived/calculated metric. Base metrics are specified during test planning and derived metrics are calculated by applying specific formulas.

The need for software testing metrics

Software testing metrics answer the questions that aid the managers, customers, decision-makers, and technical teams in making informed decisions about their projects.

MetricsWhat they tell youROI on testing

Did we overshoot the budget?

Did we estimate the effort and skills correctly?

Time management

Was the time earmarked for testing too little or extra?

How time/efforts can be managed better to improve feature velocity

Will the product be delivered on time?

Defect management

How many defects were discovered?

Were they fixed or deferred?

Severity of defects

Test result analysis

Was the testing effective?

Was the test planning efficient?

Is the product adhering to specified quality standards? These are just a few examples, but when you sit down and start listing down the metrics, more questions will arise, that will get your think tank working to start deriving metrics pertinent to your software. Some metrics are relevant to the internal development and process teams, while some act as project health indicators for the management, and some act as flags for the customers. Importance of software testing metrics (How they make QA process more effective)

Software metrics are the definite numbers that help the business/technical stakeholders to understand the efficacy of their process, monitor critical operations, evaluate the outcome of QAOps and then charter the future course of action based on the findings. Let us understand how these metrics make the QA process more effective.

Efficiency evaluation:

The software metrics help you in understanding whether you were right about the budget or estimating the time based on the quantum and complexity of work. They also help you in evaluating the effectiveness of test planning and execution.

Identifying areas of improvement:

Do you want to understand the testing and patterns that emerge from the results? Software testing metrics are there to help you. They provide insights into software behavior and help the teams to observe and evaluate defect patterns, defect density, defect distribution Keeps stakeholders informed: No need for the managers and the customers to worry about slipped timelines or overshooting the estimates, because the metrics keep them all apprised about the project progress and quality

Help in decision making:

Now that all the concerned parties are equipped with the knowledge of quantifiable measures, it becomes much easier to make decisions about the project and optimization of process and resources. Help in charting the next course of action: With timely decisions made, it becomes even easier to plan for future work strategies.

Serves as proof for predictions: Didn’t you set certain estimates at the outset of the project? These metrics will help in proving you right or wrong w.r.t. time, effort, cost, skill, etc.

Few useful metrics for test analytics

Let’s face the fact that it is a herculean task to track and measure every parameter during software development. Hence it is wise to identify the metrics that align with your business priorities, and they need to be analyzed and revisited from time to time. A few thumb rules that you need to follow for metrics independent of business are that they should be easy to understand, use consistent measurement units, adaptable, reliable, and accurate. This section will list down a few common metrics used in software development for you. Read More about : Api automation

Tags: AI test automation, AI Testing, Software testing metrics, Test Automation, Testing metrics

Api automation testing

Background In the world of internet-driven information consumption, sluggish, and non-contextual internet response on requested information is a huge mood swinger. To solve this problem in web and mobile applications, in 2012, Facebook invented an alternative to the long prevalent REST API and called it GraphQL. GraphQL is a data query language that increases developer productivity and reduces the amount of data transferred in the API requests. After proving this advantage, Facebook open-sourced it in 2015 and since then it has been used by many companies like Github, New York Times, PayPal in application development.

Advantages of GraphQL

Universal data translation

GraphQL has a huge positive implication on the developer community as a whole, because it removes errors between applications and servers, and helps incomplete data translation between various applications. This removes the bottleneck and pressure on the end application developers to ensure compatibility. Close to 100%, GraphQL requires an organized schema which in itself becomes a document.

Schema-based data definition

Once the schema is defined, GraphQL considers it as a model with all the properties. It does not require to specify data requirements at the server-side and hence based on the schema data definition happens on the client-side. Thus, prototyping becomes much faster. This is apparent from the fact that emerging product companies are relying on GraphQL.

Rapid product development

GraphQL is a boon to front end developers. The GraphQL libraries allow for realtime updates on the UI without touching the backend and therefore, iterations on app development are fast. Without GraphQL, it would have required longer than usual and an entire team to deliver the same. GraphQL facilitates schema driven development which means features are designed as per the schema created on the server. GraphQL APIs can be stitched together and therefore a single API can be developed from an existing API. This reduces the complexity to deal with multiple endpoints. Schema stitching also makes the API re-usable, thereby shortening the product development time.

Enhance backend stability

Stability comes from the simplicity with which GraphQL structures the data, independent of the client requests. GraphQL acts like a translatable layer between the client and the server which directs the server data requests to the necessary resources and systems. This allows modifications in the application backend, without restructuring the client. Since the data is passed in the language path itself, the query looks simple and elegant. Stability is further achieved because GraphQL defines the shape of the objects returned by the API.

Improved query efficiency

GraphQL gives the application developer a single entry point for multiple queries instead of multiple or ad hoc endpoints and complex data retrievals. The data is defined in a schema on the server, it is delivered in a single predictable package rather than differentiated packets. The software developers don’t have to rely on the REST endpoints which return pre-defined and fixed data structures. Pre-defined data structures lead to either getting more information than needed, called over fetching, or getting less information than needed, called under fetching, in a single query. Underfetching and Overfetching are two commonly observed problems in the REST APIs. Schema-based data on the server, adds predictability to the APIs output.

GraphQL decreases the number of resources required in each data query. Servers are able to publish contextual output in the APIs and the client can request the data with simple data calls. This means the application developers can implement data restrictions naturally with minimal impact on consumers. GraphQL improves queuing efficiency as it adds specificity and predictability in the output which improves the product development speed. This type of query output also reduces the load on the backend, which need not be modified every time there is an iteration in the product development. Read More about : Api automation testing

Tags: API Testing, Postman, REST API, Swagger

Sanity testing vs Smoke testing

Software development and testing are an ecosystem that works on the collective efforts of developers and testers. Every new addition or modification to the application has to be carefully tested before it is released to the customers or end-users. In order to have a robust, secure, and fully functional end application, it has to undergo a series of tests. There are multiple testing techniques involved in the whole process, however, Smoke and Sanity are the first ones to be planned.

Let us examine Smoke and Sanity testing in this article. Smoke testing essentially checks for the stability of a software build. It can be deemed as a preliminary check before the build goes for testing.

Sanity testing is performed after a stable build is received and testing has been performed. It can be deemed as a post-build check, to make sure that all bugs have been fixed. It also makes sure that all functionalities are working as per the expectations.

Let us first take a quick look at the details of both types of testing, and then we can move onto identifying the differences between them. There is a thin line between them, and people tend to confuse one for another.

What is smoke testing?

Smoke testing, also known as build verification testing, is performed on initial builds before they are released for extensive testing. The idea is to net issues, if any, in the preliminary stages so that the QA team gets a stable build for testing, thus saving a significant amount of effort and time spent by the QA.

Smoke testing is non-exhaustive and focuses on testing the workflow of the software by testing its critical functionalities. The test cases for smoke testing can be picked up from an existing set of test cases. The build is marked rejected in case it fails the smoke tests.

Note that, it is just a check measure in the testing process, and in no way, it replaces comprehensive testing.

Smoke tests can be either manual or automated.

What is sanity testing?

Sanity testing is performed on a stable build which represents minor changes in code/functionality to ensure that none of the existing functionality is broken due to the changes. For this, a subset of regression tests is conducted on the build. Read more about : Sanity testing vs Smoke testing

Tags: Sanity Testing, Smoke testing, Smoke testing vs Sanity testing