We have all heard and experienced that “change is the only constant”, but change also brings disruption. In this COVID-19 pandemic, we have witnessed businesses undergo a paradigm shift. Business lines, mode of operations, emphasis on contactless and relevant business communications, changes in the salary structure, and many more have occurred. The adaptation to this ‘new normal’ has surged the demand for efficient and seamless software applications, as most businesses want it to be developed and launched quickly.
What do these changes mean for an IT organization? It is a given that business transformation in the current situation calls for development or modification in the existing application. This can include either introduction of new and relevant features or redesigning and providing a new interface. For organizations, it is also imperative to deliver this in the shortest time and at the same time, not compromise on the quality.
Herein arises the need for determining testing matrices. As businesses are mostly driven by timelines, the duration of testing is automatically handled. The test coverage is, in turn, dependent on this timeline.
However, the key questions left to be answered are:
- Who determines “how much to test?” and “what to test?”
- Is the person’s capability impacting test selection?
- Is there enough proof that the quality will not be further affected in production?
This puts businesses in a Catch-22 situation. The changes need to reach market fast, while maintain the stability. However, balancing the time and quality paradigm is a Rubik’s cube in itself. Add in the cost dimension and we suddenly have the world’s toughest Sudoku on our hands.
Imagine a matrix that could tell you how to optimize test coverage based on the timeline. Moreover, it provided you with an index for failure risk with which you could take your software to the market. This is exactly where ignioTM AI.Assurance, an autonomous software assurance product, fits the bill and allows you to deliver quality software faster.
ignio™ AI.Assurance has the capability to balance between test coverage and testing time. Its test selection strategy allows you to reduce software failure through intelligent constraint-based testing. It enables you to choose from various testing strategies, such as Optimized, 100% Coverage, Business Changes, Custom, and choose scenarios.
You can execute all the scenarios covered under 100% coverage in normal business conditions, while you can choose Optimized and Business Changes for optimum test coverage during time constraints. Optimized test ensures all application paths with various data sets are covered during execution. Business changes shows the exact tests that have undergone change due to business impact that has been implemented in development. The application can only understand the context and its related actions and does not have the ability to improvise through machine learning, unless stated.
For example, assume you train the application to detect the ‘Know Your Customer’ (KYC) as a risk-related scenario. All tests related to KYC will always be selected as a part of regression. Now, consider a scenario where an advertisement page is added to emphasize on contactless banking operations. Will this scenario be automatically detected for regression in this method? Probably not; it has to be trained again to classify this as a risk-related scenario.
However, ignio AI.Assurance has the ability to understand the business impact and highlights this for testing, and hence covers the KYC scenario. It is not a word game anymore; it is an intellectual game.
With intelligent test selections, businesses can achieve delivery of quality software with less turnaround time and reduced costs. Thus, businesses are able to achieve the desired balance – increase in quality, with reduction in time and cost — enabling the software to be delivered faster to the market.