Exploring New Horizons in Functional Automation with AI

The COVID-19 pandemic has compelled businesses and service units to rethink the way they function. From increasing digital adoption and enabling remote working for employees, to offering services online and reducing consumer touchpoints, organizations have implemented a decade worth of changes in a few months.

As enterprises have adopted digitalization, they have come to rely on data for everything. Data helps enterprises create better machine learning (ML) and artificial intelligence (AI) algorithms, which can be centralized on the cloud. Such algorithms help organizations perform the same tasks faster.

All the social media and mobile applications that we use daily need some stringent checks before they reach the end user. So, it’s critical to have the most powerful and effective automation tool that fully automates the quality assurance (QA) process.

In today’s world, AI is present in every sector – automobile, IT, manufacturing, banking finance, and more, but in the area of functional automation, AI is still at the nascent stage. Automation script development or maintenance still rely on human effort.

So, can AI be used to develop an as-a-service solution in functional automation?

Functional Automation Today

Some market leaders have built tools for codeless automation. While these tools speed up the coding process, they do not resolve issues related to object identification and flow maintenance of application under test (AUT) due to frequently changing user interfaces (UIs). Firms still rely on manpower to design and maintain flows by using codeless automation. Moreover, these tools are completely dependent on a developer’s intelligence in either building the appropriate flow or fixing it.

So far, automated testing has remained largely human-intensive, but hyperautomation can reduce human effort significantly. How? Let’s understand the challenges organizations face presently and how they can move ahead for a better solution.

Challenges with the Traditional Automation Approach in Testing

The IT industry has increasingly moved toward agile and DevOps over the past decade to launch applications faster in the market with frequent builds and deployments. Each deployment follows a repetitive process that comprises multiple phases. The QA phase can be completed faster and with better quality results if we use automated tests. Though firms can opt for application programming interface (API) automation over UI, the latter cannot be completely ignored, as the deployed applications must be quality assured from an end-user perspective.

Quality is especially critical in domains like banking, finance, recruitment, and accounting. One of the important and widely accepted type of functional automation is User Interface (UI) automation as it tests application as an end user. However, organizations face various challenges with UI automation as it does not go hand in hand with agile and DevOps because:

1. UI automation is a slow activity with complete dependency on the UI. Though codeless automation can quicken the development process, it’s still fueled by significant human effort.

2. Automated scripts fail to operate even when small changes are made to the attributes of web elements due to a lack of self-healing processes.

3. Maintaining in-sprint automation in applications with fast-changing UIs and frequent builds have high failure rates.

4. In-sprint UI automation execution is difficult because user stories are delivered in the latter half of the sprint. An automation engineer does not have enough time to automate scenarios and execute them in the same sprint.

These challenges increase human dependency, which in turn increase costs and reduce overall savings by automation.

Augmenting Automation Testing with Digital Technologies

Adoption of digital technologies has changed the face of the world in many ways and yet there are other possible outcomes of infusion of digital tech with any existing technology which could be a game changer. One of its application could be in automation space where we could expect the following benefits:

• AI, ML, and augmented learning (AL) in UI automation processes can improve the quality of product delivery. For instance, an AI engine can generate scripts in any language by interpreting it using an action sequencer and intelligent object identifier.

• Triggering self-healing processes to identify failures of web elements, possibly could include the following logics
  • The use of a secondary identifier to continue the automation flow without failing
  • Analyzing and updating the primary or obsolete identifier automatically with the latest identifier which is generated based on ML algorithms and learning patterns

• Sophisticated AI algorithms can be used to update existing application flows in case of any changes.

A containerized, cloud-based, and on-demand engine can be deployed to run quality checks on applications and generate reports for human analysis.

The Business Context

By adopting similar approaches, our businesses can be offered as a holistic service rather than per resource basis service and with this, AI-driven testing can give rise to automation-as-a-service (AaaS), similar to platform-as-a-service (PaaS), infrastructure-as-a-service (IaaS), and software-as-a-service (SaaS).

Clients may get benefits like faster time to market, early bug detection, faster quality analysis and checks keeping spirits of Agile and DevOps cycle high in Application development lifecycle instead of running over them to in rush of deployments with inferior quality checks.

The organizations that have adopted AI-based testing have an edge over their competitors in automation services, as it helps them offer their customers deeper value.