Managing QA Cost By Adopting Intelligent Test Automation

The current IT landscape demands faster time to market with a high level of customer satisfaction, but in a cost-efficient manner. Needless to say, software testing becomes a critical activity to accelerate releases. Test automation has helped in achieving this goal to some extent, especially in terms of time management and quality assurance. However, it still needs to do better when it comes to cost-effectiveness and overall efficiency. That is where intelligent test automation steps in. We have discussed the benefits of intelligent test automation in detail in our blog. 

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

Factors driving the cost in software testing

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

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

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

Flaws in test automation

Test automation has proved beneficial for software testing, however, increasing demand and the need for speed with superior quality have exposed certain chinks in the armor.

For instance, false failure reports slow down the testing process. We have covered this in detail in our blog “Bane of automation-false failures”.

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

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

Discovering bugs late in the testing cycle

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

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

Improper test planning

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

How intelligent test automation manages cost

As stated in the previous section, the organizations are earmarking bigger IT budgets, so naturally, the general expectation is to maximize the benefits. Investing in AI-augmented software development is another aspect that is gaining importance due to its efficiency, reliability, and better ROI. The findings of another survey by Gartner Inc. for the increase in industry-wide funding for AI in 2022 are shared below. Organizations are heavily investing in AI and there is a good reason for it. The stakes involved are pretty high.

Let us now understand how AI-based testing helps in managing software development costs.

Reduced cost of test execution and management

AI-based testing tools help in creating test cases covering all possible scenarios leading to better test coverage. Test cases are updated as a result of continuous feedback generated after every testing.

AI-based testing tools are capable of keeping the test data source updated. It can improvise the testing by generating a large volume of varied test data for multiple test conditions. As a consequence, the time and effort involved in generating and maintaining test cases and test data are saved, resulting in saving overall cost.

Webomates CQ is a revolutionary AI-based testing tool that has a superior test execution and test management process. It can set up 100 test cases within a week and up to 200 test cases and test automation scripts within a matter of 4 weeks.

Our AI Modeler engine can help organizations in generating and automating the right test cases. Additionally, our patented AI Test Strategy and creator helps in devising a well-rounded test strategy for the software. Webomates’ AI Test Package Analyzer helps in keeping the test suite updated by providing a continuous feedback loop of defects to user stories/epics/requirements.

All this is packaged together in a single testing tool – Webomates CQ, at a very nominal cost.

Improved regression testing

Agile development and continuous testing lead to frequent code changes due to new requirements or defect rectifications, impacting the test cases. AI-infused testing tools can identify, understand and analyze those changes and aid in self-healing. The modified and healed test cases are then executed within the same test cycle, thus speeding up the testing. AI-based testing tools can adjust the scope of testing as per the need.

Webomates applies AI/ML to its test automation framework to achieve self-healing and scalability in testing, making regression testing much more efficient.

Executing the whole test suite for every change is not a feasible solution. Webomates addresses the dilemma of executing the whole test suite or the impacted test cases only. It has an option of executing mini test-suites saving substantial man-hours which further makes a difference in project cost.

Better reporting

Continuous testing generates a huge amount of test result data that needs to be continuously analyzed and results have to be shared across the board for expedited decision making. Quick feedback and comprehensive reporting help in the early resolution of issues saving precious time and resources. This is only possible when there is a high degree of collaboration, communication, and transparent decision-making within the teams.

Intelligent analytics coupled with smart decisions convert the testing results into actionable items. This aids the teams in understanding faster, making quick decisions, acting with speed, resulting in the accelerated release.

Webomates has an exemplary method of triaging and reporting, paving the way for easier and faster decision-making. Our patented AI defect predictor can predict defects earlier in the cycle, thus saving thousands of man-hours spent in triaging. It can identify the false failures with 99% accuracy.

Webomates’ AI Defect creator automates the process of gathering the data for a defect, including the key 20 seconds of video that shows the defect occurring, leading to a significant reduction in the effort for the testing and development team. 

All this accumulated time and effort saving results in a bonus of cost-saving.

Customer satisfaction at reduced cost builds credibility and loyalty

Above mentioned points categorically talk about how AI-based testing tools can aid in faster time to market by reducing the time taken to fix bugs and re-test the modules. These tools also help in covering more test scenarios, resulting in a resilient product, resulting in building credibility, and gain customer’s confidence. At the same time, managing the project costs effectively.

Webomates CQ is a dependent, cost-effective AI-based testing tool with the ability to scale up or down as per the customer requirement. We conduct testing as per the scope of the build saving thousands of man-hours. Additionally, we provide service level guarantees to all our customers.

Webomates provides intelligent automation (AI Testing) solutions with intelligent analytics. It leverages the power of data processing, analysis, reasoning, and machine learning to provide an end-to-end testing solution for your business. If this has piqued 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. If you liked this blog, then please like/follow us Webomates or Aseem.

Tags: AI Testing, Artificial Intelligence, Intelligent Test Automation, Test Automation