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Can Artificial Intelligence replace human in Software Testing?

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.

If you are interested in learning more about Webomates’ CQ service please click here and schedule a demo or reach out to us at info@webomates.com.

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Software Testing Life Cycle

Software Testing is a mandatory part of any Software Development process. It ensures that all technical and business requirements are met satisfactorily. Software Testing Life Cycle (STLC) is a series of systematically planned phases in case of a waterfall software development approach and continuous iterative & agile in case of agile or iterative development of software.

Software testing life cycle comprises of various activities with specific goals. Organizations may tweak these to align with their corporate philosophy, but the basic essence remains the same.

In the waterfall model, the activities are performed in  phases as shown in Waterfall diagram below, whereas in Agile model, all these activities are performed in every release, as shown in the following agile diagram below.

Feature understanding and Test script updates are simultaneous activities which run in every release however, there are activities which are required only once like test environment setup and only new release deployment is required.

Lets understand how testing is carried out in the traditional waterfall approach. To know more about agile testing click here.

Requirement Analysis

The QA team interacts with various stakeholders to understand their requirements for testability. The requirements can be either functional or non-functional in nature.

Priorities are attached to the requirements for testing.

The test conditions are defined in this phase. Every test condition should be traceable to a requirement. To aid this, a Requirement traceability Matrix is maintained where each requirement is mapped with test conditions. Requirement traceability Matrix helps in keeping track of testing.

Testing environment is identified during this phase.

Test Planning and Test Case Development

Test planning phase is sometimes referred to as Test Strategy phase. It is very important from technical and business point of view.

A detailed test plan is created in this phase.

All testing strategies and approaches are defined.

Risk Analysis, Risk management and mitigation strategies are defined.

Scheduling is done for various testing phases.

Once test planning is completed, the team starts working on test cases based on inputs from planning phase.

Detailed Test case Document is prepared.

Test Scripts are prepared for tests marked for automation testing.

Test cases and test scripts are reviewed by peers and managers to ensure complete coverage.

Test data is prepared in test environment. Click to read more about this blog : Software Testing Life Cycle

If you are interested in learning more about Webomates’ CQ service please click here and schedule a demo or reach out to us at info@webomates.com

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Crowdsourced Testing

Crowdsourced testing is an emerging trend in the field of software testing. It is an approach which involves different people with different backgrounds across the globe in testing process. The testers are connected through a crowdsourcing platform and tests are conducted by multiple individuals at different locations. It adds multiple dimensions to manual testing process by providing a diverse and temporary workforce, thus aiding the organizations with limited testing bandwidth in achieving quick results.

For quick references you may want to read our blogs which talk about Crowdsourced Testing, overcoming the challenges posed by crowdsourcing and how it is different from manual and automated testing.

This particular article talks about best practices to be followed in crowdsourced testing and avoiding basic mistakes in order to have best results.

Take a look at the following table for quick understanding of what to do and what not to do while crowdsourcing testing jobs. These points are further discussed in detail in entailing paragraphs.

Do’sDon’tsDetailed Recruitment PolicySelect random testersClear testing GuidelinesUndermine the findingsOpen communicationDelayed responseInternal auditsAlienate internal teamInteresting and challenging tasks

What to do while crowdsourcing the testing

Detailed Crowd Recruitment Policy

📷

Crowdsourced testing is all about hiring the right set of people to do the job. Scouting for right talent and having a good pool of testers requires having good recruitment policy in place. There has to be a wide spectrum of people and right fit can be identified based on mix and match of the requirements like demographics and technical setup. One factor to consider is response rate of the tester based on previous interactions. Sometimes, a tester may not respond well in time, thus delaying the whole process. Such testers should not be considered for time critical projects.

Set clear objectives and guidelines

It is important to lay out the scope of testing with proper guidelines in place. The scope may encompass new features or changes in existing features. Clear definition of scope ensures that time and efforts are channelized correctly. The guidelines will help the testers to know the specific areas to focus thus making the testing efforts productive.

Set testing standards and define specifications

Setting up standards for testing aligned with organization level quality standards is needed to ensure that defect reporting and rectification fits seamlessly in overall quality management process. It also helps the testers in understanding the expectations of the end client (in this case the organization crowdsourcing the work).Clear set of technical specifications help in defining the test environment to be used for conducting the tests. Read for more : Crowdsourced Testing

If you are interested in learning more about the services offered by Webomates then please click here and schedule a demo, or reach out to us at info@webomates.com. You can also avail a free trial by clicking here.

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Overcome UAT Challenges with AI-Based Test Automation

Digitization and automation have revolutionized the way the world perceives services, devices, tools, etc. Automation has permeated almost all fields, be it applied sciences, finance, pure sciences, healthcare, research, education, retail, manufacturing, and the list goes on. Consequently, the demand for high-end software has risen exponentially. In this competitive world, software development organizations have to ensure the digital happiness of their customers for sustainable business and their continued patronage.

User acceptance testing is conducted to ensure that the application under development satisfies the acceptance criteria for specified user requirements, functionality expectations, and business scenarios.

Why is UAT important

User acceptance testing is essential when an organization is contracting out its software development and looking to measure the delivered software against the original expectations.

Consider a scenario where a business venture outsources software development for their business unit within a stipulated time frame. After initial meetings between the stakeholders from both setups, the development process starts.

Frequent builds have to be rolled out to ensure that software is shaping up as per the customer’s expectations. Before the final rollout of the build for the customer, your business team wishes to conduct acceptance testing to ensure that there are no unexpected issues later since the cost of fixing defects after release is much higher.

Here, a speed bump is encountered since no time or resources are allocated to conduct user acceptance testing. If you are in the position of being tasked with the UAT for your company with no resources or time ……we feel your pain!!!

You are now in a Catch-22. If you release the software without UAT, then there are chances of having defects in the build that goes to the end customer. And if UAT is conducted (which was not originally part of the development and delivery plan), then the whole release schedule gets delayed. And you have neither the time nor the resources to get this done. Either way your upper management is NOT going to be happy, as the project was critical enough to get funded and will impact the business if it does not get completed on time.

UAT adds value to business by validating all business requirements and ensuring that the end product is as per the customer specifications.

A comprehensive UAT nets the defects before the software is released. It ensures that the version that is finally delivered to the end-user is as per the customer’s expectations.

Rolling out a high-quality product successfully is a teamwork where everyone has an important role to play. Let us take a look at how different teams take responsibility for conducting effective UAT.

The road to a successful test execution is riddled with certain challenges that need to be addressed to ensure that the collective effort of the people conducting the testing is not wasted.

Adoption of agile methodology leads to frequent changes. It could be due to changes in business objectives, or user acceptance criteria, or updates due to defect rectification. The net result is UAT gets impacted, especially if the timelines are very tight. The situation may get worse if the collaboration among the teams and communication about changes/defects is not very efficient.

Managing these challenges only with a manual testing process is not an easy task.

Read More about : 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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Shift left testing

The Goal of Shift Left Testing

Have you ever experienced a project running into budget issues, schedule slippages and getting delayed due to some last moment defects? These issues are so common so the answer is, probably yes!

The need to implement large, business and customer critical systems in an environment driven by short timelines and tight budgets has led to a change in methods that deliver business value quickly. One such method is Shift-Left Testing.

It is 15 times costlier to fix defects in the test phase of a waterfall project because testing occurs later in the project development lifecycle.

IBM SYSTEM SCIENCE INSTITUTE REPORT

Relegating the entire testing to the last stage just before deployment is no longer feasible. So, how do we fix this? The answer is simple — Shift Left your entire Testing!

According to Larry Smith — who coined the term Shift Left Testing — “Bugs are cheaper when caught young!” The Shift Left testing approach uses the ‘test early and often’ mantra to perform testing earlier in the life cycle (thereby moving left on the project timeline). The goal is to prevent defects and mitigate risks rather than deal with a whole load of bugs and critical issues post-development. Shift Left testing avoids cost overruns and project delays.

Do you really need to Shift-left your testing?

If you answer ‘Yes’ to all these questions, Shift left is the key!

Do you want to automate different testing types like Unit Testing, Functional Testing, API Testing, Security Testing?

Do you want to start feature testing at an early stage to ensure quality releases?

Do you want to detect defects early in the development lifecycle?

Do you want a smaller and quicker feedback loop between the development and testing teams?

Do you want to have maximum test coverage of your functionalities?

How can you Get Started With Shift Left Testing

Shift left does more than just help teams find defects early. To shift left means a change in organizations culture.

Now let’s find out what your teams need to do to get started with shift left testing.

Identify & Plan the Testing Lifecycle :Planning is an integral part of the shift left approach. Shift left works best when the Test analysts identify and plan the entire testing lifecycle before the start of the actual development process. This provides a strong starting point for all activities in the test lifecycle, and will help all business and operational stakeholders, developers and testers understand the tasks, objectives and expected outcome from the project.

Read for more click this link > Shift left testing

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Test automation

In the software testing arena a perennial debate has raged between proponents of manual and automation testing. In our experience, the two are complementary; used together they form a more effective test strategy.

Manual testing

Since pretty much the start of software development in the 1960’s manual testing has been carried out by teams of testers. In this technique a team of people ( qa testers) get access to the latest software build and test it to validate that the software build works correctly. For feature testing there are two broad categories of manual testing that can be carried out.

Test case based testing

In this case test cases are defined up front, prior to the arrival of the next software build and the manual team work through the list of test cases performing the actions defined in the test cases and validating that the test case and hence the software build is operational. This technique requires more domain understanding at the test case creation point and less domain knowledge at the time of execution.

Exploratory testing

Here a manual QA tester that understands the domain of the software fairly well attempts to “break” ( cause a bug to happen) the software. Exploratory testing is an excellent complement to to test case based testing. Together they result in a significant improvement in quality

Automation testing

Since the 1990’s Test automation has risen as strong alternative. In automation testing a software tool is programmed ( In Java for example) to carry out human actions that testers would do in check based testing. The process is typically to first create the test cases like in Manual check based testing and then to program the test cases.

Why Choose Manual or Automation?

Clearly manual and automation testing are great complements to each other and as a combination can create a fast, less brittle solution that has exploratory testing in addition.

WebomateS offers a regression testing service that uses manual testing, automation and crowdsourcing together to guarantee all test cases(even modified ones) are executed in ONE day with exploratory testing!

In fact the output is a list of prioritized defects for software teams to review and fix.

With Webomates CQ we have developed a service that incorporates the benefits of AI into a TAAS ( Testing As a Service). To hear more about WebomatesCQ schedule a demo here.

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Smoke Testing vs Sanity 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.

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

At Webomates, we continuously work to evolve our platform & processes to provide guaranteed execution, which takes testing experience to an entirely different level, thus ensuring a higher degree of customer satisfaction.If you are interested in learning more about Webomates’ CQ service please click here and schedule a demo, or reach out to us at info@webomates.com

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Api automation testing services

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!

If you are interested in learning more about Webomates’ CQ service please click here and schedule a demo, or reach out to us at info@webomates.com#apitestingservices |

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AI Based Test Automation

With AI-based automated testing, you can increase the overall depth and scope of tests which results in overall software hence it increases the quality of the software.

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Api Testing Service Provider Company

The time has never been more right for the api testing service provider company to get involved in mobile app, latest technology and contemporary technology as they can greatly assist your business. Take your business into a next level by using the best technology is available at your fingertips.

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Vist :            https://www.webomates.com/blog/software-testing/top-benefits-of-api-testing/

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Requirement tracebility matrix

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

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 Vist :            https://www.webomates.com/blog/software-requirement-metrics/9-reasons-why-requirements-traceability-is-important-in-agile/

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Intelligent automation testing

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Black box tests

Black box testing can be performed using various approach :

Test case

Automation

Ad-hoc

Exploratory

Before these individual approach can be explained, It is important to know what black box testing is.

Black-box tests is a software testing method antonymous to white-box testing. The black-box is also known as Behavioral testing/functional testing/closed-box testing. It is a method of analyzing the functional and non-functional aspects of software. The focus of the software tester does not involve internal structure/implementation and design of the system involved. This means that the software tester does not necessarily need to have knowledge of programming and does not need to have access to the code. Black-box testing was developed so that it can be applied to customer requirement analysis and specification analysis. During the execution of testing the black-box software, Software tester selects a set of valid and invalid inputs and looks for valid output response.

According to ANSI/IEEE 1059 standard — “Software testing is a process of analyzing a software item to detect the differences between existing and required conditions (i.e: defects) and to evaluate the features of the software item. The approaches to software testing include Black-box testing, White-box testing and grey-box testing.”

Examples of Scenarios the applies simple Black-box testing includes:

These few examples are used to paint a vivid picture of what Black-box testing implies,

Using a Google search engine, a software tester types some texts into the inputs space provided, this server as the input. The search engine collects the data, processes it using the algorithm developed by Google programmers and then outputs the valid result or output that corresponds to the input.

Same applies to a Facebook chatting interface, When the bearer of the information inputs the information into the chat box and sends, the information is processed and sent to the receiver. The receiver from his end retrieves the information and vice versa.

Black-box testing can be used in any software system you want to test, such as a database like Oracle, a mathematical software like Excel and MATLAB, an operating system like windows and Linux and any other custom native or web based or mobile application. Emphasis is laid on the input and output of the software with no interest on the code.

Types of Black-box testing:

There are many types of black-box testing used in software testing which involves many procedures during implementation, we focus on the prominent ones.

Functional testing: This type of black-box testing involves only the input and corresponding valid output analysis. It is mainly done to determine the functionality and behavior of the system. The software testers focus on answering the question ‘Does this system really do what it claims to do?’

Few major types of functional testing are:

Sanity testing

Smoke testing

Integration testing

System testing

Regression testing

User acceptance testing

Non-functional testing: This test focuses on answering the questions ‘how efficient is this system in carrying out its functions?’. It focuses on the non-functional requirements of the system such as performance, scalability and usability which are not related to black-box testing.

Few major types of non-functional testing:

Usability testing

Load testing

Performance testing

Compatibility testing

Stress testing

Scalability testing

Regression testing: Whenever the internal structure of the system is altered regression testing is carried out to ensure existing functionality & behavior is working as expected. The alteration can mean when the system undergoes debugging, code fixes, upgrades or any other system maintenance processes. The software tester makes sure the new code does not change the existing behavior.

The black-box testing method is applicable to various levels of software testing such as:

This diagram Represents levels of black-box testing

Integration testing: in this level of software testing individual software are combined by the tester and tested as a group using black-box testing. This exposes fault experienced in the interaction between the integrated units.

System testing: in this level of software testing a fully complete software is tested to evaluate the compliance with the specified requirements proposed for the system.

Acceptance testing: In this level of software testing the acceptability of the system is tested. Here the software tester checks whether the system complies with the commercial requirement. The user needs and requirement are checked to know whether they are acceptable before delivery.

The higher the level of the black-box testing, and hence the bigger and more complex the system is the more the black-box testing that is applied.

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Techniques involved in black-box testing:

There are many approaches use in designing black-box testing the following are but a few of them:

This diagram Represents Techniques in Black-box testing

Equivalence Partitioning or Equivalence class testing: in this software design technique the software tester divides all the inputs into equivalence classes and pick valid and invalid data from each class, a selected representative is used for the whole as a test data. The selected data is now used to carry out the black-box testing. This technique helps to minimize the number of possible test cases to a high level that maintains reasonable test coverage.

Boundary value analysis: it is a software design technique where the functional tester while carrying out the testing determines boundaries for input values. After determining the boundaries selects inputs that are at the boundaries or just inside or outside the boundaries, and uses it as test data. The test data is now used for carrying out the black-box testing.

Cause-effect graphing: in this software design technique software testers identifies the causes (valid or invalid input conditions) and effects (output conditions). This result in the production of a cause-effect graph, and Generation of test cases accordingly.

Error guessing: this is an example of an experience-based testing in which the tester uses his experience about the application and functionalities to guess the error-prone areas

Others include:

Decision table testing

Comparison testing

Black-box Testing steps

Black-box testing involves some generic steps carried out by testers on any type of Black-box.

The requirements and specifications are provided for the systems and they are thoroughly examined.

The software tester chooses both the valid inputs (the positive test scenarios) and the invalid inputs (the negative test scenarios). He tests the valid input to check whether the SUT processes them correctly then the invalid inputs to know whether they are being detected by the system.

The expected outputs are retrieved for all the inputs.

The software tester then constructs test cases with all the selected inputs.

The constructed test cases are executed.

Actual outputs from the SUT are compared with the expected outputs to determine if they comply with the expected results.

If there is any defect in the results they are fixed and the regression test is carried out.

Black-box testing and software test life cycle (STLC)

Software test life cycle (STLC) is the life cycle of a black-box testing and it is relative to the stages of software development. The cycle continues in a loop structure until a perfect and satisfying result devoid of defects are achieved.

Diagram showing software development life cycle for Black-box testing:

Requirement: This is the stage where the requirements and specifications needed by the software tester are gathered and examined.

Test planning and Analysis: In this stage the possible risks and mitigations associated with the project are determined.

Design: This stage allows scripts to be created on the basis of the software requirements.

Test execution: the prepared test cases are executed and if there is any deviation between the actual and expected results they are fixed and re-tested. The cycle then continues.

Tools used in black-box testing

The main tools used in black-box testing includes records and playback tools. These tools are mainly used for regression testing. When a defected system undergoes bug fixing, there is a possibility that the new codes will affect the existing codes in the system. Applying regression analysis with the records and playback tools helps to checkmate and fix errors.

These record and playback tools record test cases in form of scripts like TSL, VB script, JavaScript, Perl etc.

Advantages and Disadvantages of Black-box testing

Advantages:

There is no technical or programming language knowledge is required. The software tester does not need to have any knowledge of the internal structure of the system. He is required to access only the functionality of the system. This is like being in the user’s shoe and thinking from the user’s point of view.

Black-box testing is more effective for large and complex applications.

Defects and inconsistency in the system can be identified easily so that they can be fixed.

The test cannot be compromised because the designer, programmer, and tester are independent.

Tests are carried out as soon as the programming and specifications are completed.

Disadvantages

The test will not be necessary if the software programmer has already run the test case.

The tester may ignore possible conditions of scenarios to be tested due to lack of programming and technical knowledge.

Complete coverage in cases of the large and complex project is not possible.

Testing every input stream is unrealistic because it would take a large amount of time; therefore, many program parts go untested.

In conclusion, Black-box testing is a significant part of the software testing technique that requires the verification of system functionalities. This help in detection of defect for fixing to ensure the quality & efficiency of the system. 100% accuracy cannot be achieved when testing software; this requires software testers to follow the correct procedure for the testing in order to achieve a higher result.

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API Testing

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