#requirementtracebilitymatrix

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.

It is performed to verify the correctness of the application, in light of the changes made. Random inputs and tests are done to check the functioning of software with a prime focus on the changes made.

Once the build successfully passes the sanity test, it is then subjected to further testing.

Sanity tests can be either manual or automated.

Differences between Smoke testing and Sanity testing

This table summarizes the differences between these two tests for quick reading.

Smoke testingSanity testingTest Build TypeInitial BuildStable BuildObjective StabilityCorrectness ScopeWide (application as a whole is tested for functionality without a deep dive)Narrow (focus on one part and performs regression tests)FocusCritical functionalitiesNew/Updated functionalitiesResponsibilityDevelopers and/or testersTestersSupersetAcceptance testingRegression testingRegression testingExploratory testingDocumentation NeededYes(Uses existing test cases)Preferred release notes or released changes document

Is it sane to compare Smoke to Sanity testing?

You must be wondering why are we even comparing these two when on a superficial level, both of them are performing tests before the “big” testing cycle commences.

It may be noted that they are different at many levels, albeit they appear to be similar.

Smoke testing takes care of build stability before any other comprehensive testing (including sanity testing) can be performed. It is the first measure that should be ideally taken in the software testing cycle because conducting testing on an unstable build is a plain waste of resources in terms of time, effort, and money. However, smoke testing is not limited to just the beginning of the cycle. It is an entry pass for every new phase of testing. It can be done at the Integration level, system-level, or acceptance level too.

Whereas, sanity testing is performed on a stable build, which has passed the acid test of smoke tests and another testing.

Both can be performed either manually or with the help of automation tools. In some cases, where time is of the essence, sanity tests can be combined with smoke tests. So, it is natural for the developers/testers to often end up using these terms interchangeably. So, it is important to understand the difference between the two to ensure that no loopholes are left in the testing process due to this confusion.

To summarize, smoke tests can be deemed as general and overall health check-up of the application, whereas sanity tests focus is more targeted health check-up.

Conclusion

Webomates CQ helps in performing effective smoke testing, using various Continuous Testing methodologies that run using Automation and AI Automation channels. It provides better accuracy in testing and the results are generated within a short period, approximately 15 mins to 1 hour. It also provides CI-CD that can be linked with any build framework to give quick results.

Webomates CQ also provides services like Overnight Regression, which are conducted on specific modules, which can be either a fully developed module or work-in-progress module. The advantage of using our product is that it takes less time than a full regression cycle, and testing can be completed within 8–12 hours. Also, the development team gets a detailed execution report along with the defects report.

Webomates tools also help in generating and automating new test scenarios within hours, using AI tools. This reduces manual efforts during sanity testing.

Let us examine Smoke and Sanity testing -

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.

Read More about Smoke Testing vs Sanity Testing

Top Benefits of API Testing

An application programming interface (API) is a computing interface that defines interactions between multiple software intermediaries. It defines the kinds of calls or requests that can be made, how to make them, the data formats that should be used, the conventions to follow, etc. (Source: Wikipedia)

API’s help not only in abstracting the underlying implementation but also improves the modularity and scalability of software applications. APIs encompass the business and functional logic and are a gateway to sensitive data and expose only objects & actions that are needed.

API testing deals with testing the business logic of an application, which is typically encompassed in the business layer and is instrumental in handling all the transactions between the user interface and underlying data.  It is deemed as a part of Integration testing that involves verification of functionality, performance, and robustness of APIs.

Besides checking for the functionality, API testing also tests for error condition handling, response handling in terms of time and data, performance issues, security issues, etc.

API testing also deals with contract testing, which in simpler terms, is verifying the compatibility and interactions between different services. The contract is between a client/consumer and API/service provider.

Clearly, API’s usage is not limited to just one application and in some cases, they can be used across many applications and are also used for third-party integrations. Hence, developing and testing them thoroughly is extremely critical.

The following section highlights why thorough API testing entails many benefits in the long run.

These benefits are discussed in detail below.

Technology independent and ease of test automation

The API’s exchange data in structured format via XML or JSON. This exchange is an independent coding language, thus giving the flexibility to choose any language for test automation.

API testing can be effectively automated because the endpoints and request parameters are less likely to change, unless there is a major change in business logic. Automation reduces manual efforts during regression testing and results in a significant amount of time-saving.

API tests require less scripting efforts as compared to GUI automated tests. As a result, the testing process is faster with better coverage. This culminates in time and resource-saving. Overall, it translates into the reduced project costs.

Reduced risks and faster time to market

API testing need not be dependent on GUI and other sub-modules. It can be conducted independently at an earlier stage to check the core business logic. Issues, if any, can be reported back for immediate action without waiting for others to complete.

APIs represent specific business logic, it is easier for the teams to isolate the buggy module and rectify it. The bugs reported early can be fixed and retested, independently yet again. This reduces the quantum of time taken between builds and release cycles, resulting in faster releasing of products.

The amount and variety of input data combinations inadvertently lead to testing limit conditions, which otherwise may not be identified/tested. This exposes vulnerabilities, if any, at an earlier stage even before GUI has been developed. These vulnerabilities can be then rectified on a priority basis and any potential loophole for breaches is handled.

When there are multiple API’s from different sources involved in development of an application, the interface handshake may or may not be firm. API testing deep dives into these integration challenges and handles them at earlier stages. This ensures that end user experience is not affected because of the issues that could have been handled at API level.

Improved test coverage

API test automation helps in covering a high number of test cases, functional and non-functional. Also, for a complete scenario check API testing requires to run both, positive and negative tests. Since API testing is a data-driven approach, various combinations of data inputs can be used to test such test cases. This gives good test coverage overall.

Good test coverage helps in identifying and managing defects in a larger scenario. As a result, miniscule bugs make way to production, thus resulting in a higher quality product.

Ease of test maintenance

API tests are usually deemed stable and major changes are done mainly when business logic is changed. The frequency and amount of changes are comparatively less. This means less rework in rewriting test cases in event of any changes. This is in sharp contrast to GUI testing, which requires rework at many levels in case of any changes.

API tests can be reused during testing, thus, reducing the overall time quantum required for testing.

Also, it is a good idea to categorize the test cases for better test case management since the number of API’s involved in any application can be large. We have talked about this in our earlier blog “Do’s and Don’ts of API testing”.

Conclusion

APIs evolve and develop as and when business and functional requirements change, thus making it even more important to test them on a continuous basis.

Webomates test APIs using Manual and Automation testing. Webomates provides API testing which focuses on Performance and Security Testing to make sure the application is secure and giving the application a strong backbone.

You can take a quick look at the following table to see which tools are already integrated in our Testing as a Service platform.

At Webomates, we continuously work to evolve our platform and processes in order to provide guaranteed execution, which takes testing experience to an entirely different level, thus ensuring a higher degree of customer satisfaction.

Webomates offers regression testing as a service that is a combination of test cases based testing and exploratory testing. Test cases based testing establishes a baseline and uses a multi-execution channel approach, to reach true pass / true fail, using AI Automation, automation, manual, and crowdsourcing. Exploratory testing expands the scope of the test to take the quality to the next level. Service is guaranteed to do both and even covers updating test cases of modified features during execution in 24 hours.API test automation helps in covering a high number of test cases, functional and non-functional. Also, for a complete scenario check API testing requires to run both, positive and negative tests. For More Details on api automation testing services offered by Webomates schedule a demo now!

Introduction

“Customer Experience is the new battlefield” –  Chris Pemberton, Gartner

Product development involves careful planning, innovation, automation, time, and resources. One really vital piece of the development process is product testing for quality, reliability, and security. However, gone are the days when merely testing software against the business and technical requirements sufficed. Your application or software may work as defined, but Are your customers happy and satisfied? Does your product meet the user’s needs?

One thing that today’s customers won’t appreciate is bad Customer experience (CX). Customer satisfaction can make or break your business. Instant gratification is what today’s customers look for. Thus, CX is the biggest factor driving customer loyalty. Most companies including the FAANG have already made great strides into developing next-gen apps and are integrating AI across their tech stack for delivering higher customer experience.

Delight the customer and beat the competitor – is what all organizations are focused on. And this is not just online retail, it’s across every industry like finance, healthcare, media.

So, how do you measure Customer Experience?

Gartner in its recent study emphasizes the importance of the 5 types of metrics such as customer satisfaction (CSAT), customer loyalty/retention/churn, advocacy/reputation/brand, Quality/operations, and employee engagement to measure customer experience.

These metrics are a measure of how well your products and services meet your end users’  expectations.

But how can executives guide their organizations to reap the benefits from AI and exploit the benefits that beckon?

One way is to put the customer front and center during software development and testing. Artificial intelligence-enabled customer journey analytics can find answers to important CX queries like:

How can we serve the customer better to have an improved CSAT score?

How can we detect defects before the Customers?

Which features should you prioritize to improve CX and achieve business results?

Where does AI Testing come into the picture and how will it benefit the customer?

As per World Quality Report, demands for quality-at-speed and shift-left have placed the onus of ensuring end-user satisfaction on quality assurance teams.

To advance the digital transformation and enhance customer experiences, QA must break loose from their traditional bug testing shackles and embrace frictionless, AI-powered automation, and continuous delivery and continuous testing approach. Providing a higher quality, more reliable product to market faster, or getting answers to queries faster (or immediately) provides a richer, more positive experience for the customer, and thereby a competitive advantage for the company.

Taking the customer experience to the next level

Here are 5 key AI drivers to catalyze high-quality, agile software delivery that can add value to business applications, through its varied technologies and will revolutionize the customer experience.

These solutions are enabled by Webomates patented tools that work as accelerators for faster and better delivery. Webomates uses advanced intelligent test automation framework  to continuously test your build across platforms and devices.

Optimize the customer experience by AI-driven exploratory testing Not all defects are due to a  developer’s coding errors. What happens when there are zero defects in the code but it still fails? Go exploratory – A place where no scripted test has gone before! 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. It helps you scrutinize the potential risks by combining exploratory testing with regression testing.

Find your defects before the user does! With AI-based automated testing, one can increase the overall depth and scope of tests resulting in software quality improvement. The speed at which AI (Intelligent test analytics) can reveal insights allows development and testing teams to resolve defects faster than ever before. Working together shifts the gear from last-minute testing to early defect identification and resolution, thereby rapidly speeding up the time-to-market for every release!

Power of Self Healing Test Automation Framework When new changes are introduced, there is a chance that the automation may fail due to the predefined test scripts. It is then very difficult to identify which test cases should be modified or added. Webomates applies AI and ML algorithms to its self-healing test automation framework to dynamically adapt its testing scope to the changes. With such nimble-witted features, the test scripts adapt thus, making testing a lot easier.

Create exceptional digital experience by Real-time analytics and Insights Delivery AI thrives on information—the more the better. For AI to impact the customer experience (CX), actionable insights must be shared in real-time. Data unification to create a single view is a must for any type of defect analytics. For every incremental build, Webomates CQ can create, execute, maintain, analyze test cases and generate defects for browsers, mobile, Windows, and API applications. The exact state of the system in terms of defects is known after every check-in. With its power to gather and analyze data in real-time, AI is helping in getting a better understanding of the defects and their patterns, and eventually creating an enhanced customer experience strategy.

Deliver business value by addressing the risks that emerge across the digital ecosystem – Conduct in-depth performance, Security Testing Imagine your application runs perfectly fine with zero defects, and your customer is happy too. But then one security incident occurs and all your efforts go in vain! Breach of trust is the biggest no-no in the CX journey. Webomates has proven records of exhibiting agility in scaling up its testing services based on the changing requirements. The use of the  Shift-left testing approach helps in building a good product where UI, API, Load, and Security are not left out to be tested as a different component at the end. Webomates CQ platform helps to achieve effortless software testing and keeping customers happy knowing that their sensitive client data is secure.

Conclusion

It is evident that AI has already begun to create a tangible impact.

Webomates CQ  platform is designed to reduce test cycle duration and mission-critical defects by more than 50% by applying Machine Learning and Artificial Intelligence to software testing. The testing is optimized by combining the patented AI testing platform using multiple channels of execution like Automation and AI with crowdsourcing and manual testing.

This helps in filling the gap with the ability to connect the dots between customer behavior and business outcomes, enabling cross-functional teams to collaborate on the software development journey and better manage, measure, and optimize the development and testing.

The advantages of Intelligent Test Automation are limitless. All in all, AI testing is necessary to speed up the development and deployment of software releases. It not only helps organizations to save time but also helps in delivering high-quality products.

If you are interested in learning more about Webomates’ CQ service please click here and schedule a demo, or reach out to us at [email protected].

Artificial intelligence is a computer science discipline that simulates intelligence in machines, by making them think, act and mimic human actions. There has been a significant development in the artificial intelligence industries, machines are automated to take rational actions and exhibit traits that are associated with humans. As days go by more and more algorithms are being created to mimic human intelligence and are embedded into machines. Software testing and development is a very important aspect where artificial intelligence is applied. With Digitalization improving human efficiency, so has improvements in AI shaped the way software is being tested.

2016-2017 Quality assurance report suggests that AI will help shape software testing by assisting humans in eliminating problems associated with QA and software testing challenges. However, if these needs are met in the software industries there is a possibility that human testers will become extinct. This calls for the question “Can Artificial intelligence replace humans in software testing?” Many software experts believe that artificial intelligence can only assist in software testing and cannot replace humans, because humans are still needed to think outside the box and explore inherent vulnerabilities in the software. Contrary to this, others think otherwise. But after critical thought and weighing both views, it appears that the former obviously holds more tangible points than the latter.

The Evolution in Software testing is continuous with the adoption of Agile and DevOps methodologies. And software development will also continue to evolve in the era of AI. Artificial intelligence is charged with creating software to understand input data versus output data. This is similar to software tests carried out by human software testers, where the tester types in an input and looks for an expected output. Today, testing tools have evolved. Automation tools can be used to create, organize and prioritize test cases. Efficiently managing tests and their outcomes remain essential to giving the developers the feedback they need.

Shortcomings of Humans in Software testing that can be positively transformed by Artificial intelligence

Although humans are considered a reliable source for software testing, humans still have its own shortcomings. This is a disadvantage to human software testers which reduces their efficiency and performance in software testing. These shortcomings are stated as follows:

Time-consuming: The primary disadvantage of performing software testing by humans is that it is time-consuming. Validation of the functionalities of software might take days and weeks, and with the assistance of Artificial intelligence time wastage is reduced to minimal.

Limited possibilities of testing for manual scenarios: Artificial intelligence creates a broad scope for testing contrary to the limited scope available to the human testing scenarios.

Lack of automation: Manual testing requires the presence of the software tester, but testing using artificial intelligence can be done steadily without much human intervention.

In a large organization without the help of Artificial intelligence automated tools in software testing, there will be low productivity.

Manual testing is not always 100% accurate as it can be exposed to certain errors which may elude the software tester. Some glitches in the software are usually not recognized by the software tester, validation only occurs in certain areas and others are ignored. With AI coverage as well as accuracy can be improved.

Scalability issues: manual testing is a linear process and happens sequential manner. this means that only one test can be created and done at the same time, trying to create more test from other functionalities simultaneously can increase complexity.

You may like to read

Importance of Artificial Intelligence Software Testing Tool

Black – Box Testing Explained

What are the advantages and disadvantages of Artificial intelligence testing tools in software testing?

Artificial intelligence testing tools can work side by side with the software testers in order to achieve improved quality in software testing. Modern Applications interacts with each other through a myriad of APIs which constantly grows in complexity exponentially as technology evolve. Software development life cycle is becoming more complicated by day and thus, management of delivery time is still significant. Therefore, software testers need to work smarter and not harder in this new age of software development. Artificial intelligence testing tools have helped to make software releases and updates that happens once a month to occur on weekly or daily basis. An artificial intelligence testing platform can perform tests more efficiently than human beings, and with constant updates to its algorithm, even the slightest change can be observed in the software. But as much as artificial intelligence has positive achievements in software testing industries, it still has its corresponding disadvantages. Some of these disadvantages are reasons why human contributions cannot be neglected.

Advantages:

Improved accuracy and efficiency: Even the most experienced software testers are bound to make mistakes. Due to how monotonous software testing is, errors are inevitable. This is where artificial intelligence tools help by performing the same test steps accurately each time they are executed and at the same time provides detailed results and feedbacks. Testers are freed from monotonous manual tests giving them more time to explore the application & able to give input for improvements or usability areas.

Increases the Overall test coverage: Artificial intelligence testing tools can help increase the scope of tests, this results in overall improvement of software quality. AI testing tools canscan through the memory, file contents, and data tables in order to determine if the software is behaving as it is expected to and at the same time can provide more triage information or even a root cause..

Saved time + Money = Faster delivery to Market: Due to repetitions that exist in software testing every time a new product is created or modified, a human tester is needed to solve the problem associated with each test case by creating and automating tests. This helps to solve the problem of repetition, thereby saving time and money and help achieve faster delivery. By integrating AI software testing, the overall timespan can be reduced which translates directly into cost savings.

Going beyond the limitations of manual testing: AI testing tools or bots can automatically create tens, hundreds or thousands of virtual set of users that can interact with a network, software or web-based application. This helps software testers to execute a controlled web testing with hundreds of users thereby breaking the limitation of manual testing.

Helps both developers and testers

Disadvantages:

Artificial intelligence in software testing use the concept of GIGO (Garbage in Garbage Out): Most people are looking at artificial intelligent to fix all their testing ills. They hope Artificial intelligence will solve all the problems that manual testing has not been able to address. The simple fact is that a problem that cannot be solved manually cannot be solved by an AI tool as well. AI tools can only solve problems that have been solved manually and has been directed for them to solve digitally. Therefore, an Artificial intelligence tool can only do what it is told to do and cannot go beyond that.

High costs: the high cost associated with acquiring AI tools coupled with cost needed to get it updated with time to meet the latest requirements, can make it inaccessible to individual testers or smaller organizations.

Can’t think outside the box: Automated software testers can only do what they are programmed to do. Their capability is limited and cannot go beyond whatever algorithm or programming is stored in their internal circuit.

Unemployment: Much human software testers won’t be needed to perform software testing Jobs because most of the positions have been occupied by automation tools. Thus, limited positions will now be available for human testers to occupy.

Areas, where artificial intelligence can assist and dominate in software testing, include the following:

Generating test case scenarios

Generating automation script

Predicting the defects as per code changes

Reducing the automation false failure

Self healing of automation script

and many more to come….

In conclusion, assistance of artificial intelligence in software testing is significant and has helped achieve tremendous results in software testing, but more emphasis is laid on inevitable human contributions. With every passing day, as artificial intelligence finds its way into Software testing and other quality assurance fields, organizations are contemplating whether it should be adopted wholly within their quality assurance departments. But evidently, Quality assurance cannot do without human contributions. The salient benefits of AI in software testing are these: it goes beyond the limitations of manual testing, it helps achieve improved accuracy and efficiency in bug and algorithm testing, and it saves time and money involved in software development. It is evident that in the long run, AI will not only be confined to helping software testers, but will be applicable to all roles across software development to deliver top quality software to the market. So answering the subject matter question we can see that the extinction and replacement of Humans by AI is fallacious.

Webomates offers a regression testing service, CQ, that uses manual testing (test case- and exploratory-based), automation,  crowdsourcing with Artificial Intelligence to not only guarantee Full regression in under 24 Hours but also to provide traiged defects. Our customer spend only 1 hour a week on a Full Regression of their software.

Webomates requirement tracebility matrix is a framework to estimate the probability of a given requirement being implemented successfully or not in the development cycle.

 Explore more at  : https://www.webomates.com/