Let us examine some of the factors that drive the cost of quality assurance activity thus bloating the IT budgets. Taking pre-emptive measures can salvage the situation before it is too late.
Flaws in test automation
Test automation has proved beneficial for software testing, however, with increasing demand and the need for speed with superior quality has exposed certain chinks in the armor.
False failures are the bane of test automation since they lead to slowing down the whole testing process.
Test suite maintenance increasingly becomes a major headache with every test cycle. Updating test cases based on previous test results, or changes in specifications is a challenging and error-prone activity.
Test data maintenance isn’t easy either. Any change in specifications may lead to modification in test inputs. 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 maintenance activity.
Discovering bugs late in the testing cycle
Sometimes, the bug is discovered very late in the test cycle. Reasons could be any- outdated or limited test data, or unexpected errors due to an untested path because of limited test cases.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.
Inadequate 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 AI-based test automation helps in managing QA costs
Efficient test execution and management
AI-based testing helps 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 test.
The testing process is improvised since AI is capable of 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 can help you with working test automation in 4 weeks, with AiHealing by fixing the automation for every build in less than 24 hours, at half the cost.
The Webomates triad of AI Modeler engine, AI Test Strategy creator, and AI Test Package Analyzer is a boon for businesses struggling with rising QA costs.
- AI Modeler engine can help you in generating and automating the right test cases.
- AI Test Strategy and creator can help in devising a well-rounded test strategy for your software.
- AI Test Package Analyzer keeps 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 service at a very nominal cost.
AI-infused testing services can effortlessly identify, understand and analyze frequent changes done to the software and aid in self-healing.
Webomates’ AiHealing process leverages the power of AI/ML to achieve self-healing and scalability in testing, making regression testing much more efficient.
Now, we all know that executing the whole test suite for every change is not feasible. We make it simpler by giving an option of executing mini test suites, instead of the whole test suite, saving substantial man-hours, which further makes a significant difference in QA cost. Read More About: Optimize QA costs
