#intelligentanalytics

Static dashboards can no longer keep pace with modern education systems. Across P-20 education, leaders are replacing static KPIs with predictive analytics that surface risk early and guide smarter action.

Powered by AI in education and machine learning, predictive models support student retention, forecast enrollment projections, and expose gaps in digital equity and financial health. This evolution unlocks prescriptive analytics, where systems recommend interventions instead of simply flagging issues.

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 [email protected]. We have more exciting articles coming up every week.

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Black Box Testing, also known as functional testing or 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

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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.Read More about : Ai based 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 [email protected]. We have more exciting articles coming up every week.

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