No organization in the modern world can operate optimally without enterprise applications, such as those running on ServiceNow. These applications need extensive testing so as to perform under different conditions. Failure to do so will result in serious disruptions in business processes – impacting productivity and revenue.
ServiceNow applications require robust testing as they support business-critical workflows. Insufficient testing will lead to operational failures, which makes testing integral to business strategy.
Current Testing Technologies
For testing purposes, Selenium and ServiceNow Automated Test Framework (ATF) are most commonly used today. The former is very popular among testers, used to simulate user actions within web applications on real browsers.
ServiceNow ATF, on the other hand, is designed specifically for the ServiceNow ecosystem. It helps testers build and run automated tests within ServiceNow. It tests applications where they are developed and deployed, bringing higher consistency and stability to test execution.
As more applications are deployed to address complex work processes, testing too becomes more layered and harder to manage. Despite the existence of automation tools, many ServiceNow applications continue to be tested manually, because the current options don’t fill all the gaps in efficiency.
This is primarily because creating and maintaining automated testing workflows in ServiceNow using ATF requires substantial time and effort. Small changes in requirements lead to significant modifications to test workflows, making it difficult to keep everyone on the same page.
In this article, we’ll dive deeper into the limitations of testing ServiceNow enterprise applications, as well as present a few solutions in the form of AI-assisted automated test case generation.
Limitations of Current Technologies
Selenium and ServiceNow ATF are excellent tools, but have certain gaps in functionality that slow down efficiency and productivity of test cycles. If you’re using either of these technologies, you should be aware of the following challenges:
- Integrating these tools into a pre-existing enterprise infrastructure almost always presents bottlenecks. The process requires significant technical expertise and a non-trivial amount of time. It is also expensive to hire personnel actually capable of completing said integration protocols.
- Setting up and maintaining automated tests for these frameworks is neither easy nor affordable for most small teams. Even though ServiceNow ATF is built for the platform, using it requires senior-level knowledge and a complex initial setup.
- Often, minor changes in technical requirements will require adjustments to test flows. Testers have to keep updating scripts, which slows down the entire SDLC. Once again, these adjustments require high-skill testers to step in and take time away from actual testing.
- Manual testing is certainly necessary for certain segments of the testing pipeline. However, too much manual testing increases the risk of missed bugs. Human beings get tired and make mistakes.
Scaling manual testing is also challenging, because it requires hiring more and more people. On the other hand, software keeps growing more complex and testing must necessarily be scaled consistently. - Whether manual or automated testing, using Selenium or ATF requires too much time and human effort. Setup, execution and maintenance of tests take up time, money and effort that could otherwise have been devoted to developing innovative scripts. Instead, testers have to keep an eye out for updating current scripts and simply keep up with the ATF infra.
The Promise of AI in Testing
AI-powered testing significantly shifts the entire software quality assurance mechanism, especially within complex environments like ServiceNow. It addresses most inefficiencies and limitations associated with ATF and ServiceNow testing.
These tools use machine learning, natural language processing and other technologies to intelligently automate test cases. They also adapt to changes in the environment and apply learnings from previous test cycles.
We’ll dive deeper into how AI improves every major test step, but at a high level, here’s what they enable:
- Test design: Traditional test design requires the dedicated attention of skilled testers able to predict possible/likely issues and failures. AI can automatically generate these test cases based on software data, usage patterns and learnings from previous projects.
- Test scripting: Again, traditional scripting requires highly skilled manual testers who understand the software and the test framework with some depth. AI takes away the need for most of this manual effort; it develops test scripts that adapt to software changes.
- Test execution: AI runs your tests more frequently and thoroughly than current automation workflows. These engines can execute tests continuously in real time and deliver immediate feedback. AI can also replicate a wide variety of user actions and system states, serving to check how the software acts under different conditions.
What AI-powered testing really brings to the table
Test Design
Core challenges with testing emerge right from the design phase. There are multiple limitations in the manual method of test design, limitations that get in the way of achieving comprehensive test coverage. The entire process is also slower, requires a high degree of expertise and prone to human error.
- Human testers are limited by time and cognitive limits. We can only predict a few use cases and scenarios. This leads to inadequate test coverage, especially with complex edge cases.
Manual design is also better at crafting positive scenarios when the software works as expected, rather than negative scenarios where it fails. - Manual test design is slow and labor-intensive. Testers have to pour over requirements and document test cases in a way that all stakeholders understand. This is extremely time-consuming and slows the project in no small way.
AI-powered testing changes the game. Its advanced algorithms produce a wide spectrum of test cases, automatically providing more test coverage. - The AI tool can analyze requirements with multiple parameters quickly, and account for positive and negative scenarios. They can identify potential pain points that human testers would probably miss.
- AI accomplishes much of what humans do in less than half the time. It studies and builds test cases infinitely faster than humans, making the pipeline literally more “agile”.
AI tools can redesign and update test cases to reflect changes in software, once again cutting down on human effort. - AI is also key to performing multi-thread tests – designing tests for multiple user stories/requirements simultaneously. Manual testers absolutely cannot do this, while AI does it effortlessly to speed up and streamline test cases and components.
AI can also update and adjust test scripts when application changes occur, maintaining test consistency.
Test Scripting
Test scripts for modern applications must handle multiple user inputs. They must also be adaptable to changes in the app‘s UI or underlying architecture. These scripts need to be detailed, executable and able to replicate user interactions during tests.
Such intricate scripts require technical proficiency and a close understanding of the software codebase.
- It is expensive to hire highly skilled testers who can create the right scripts. Dependence on high-demand programming talent is a serious obstacle for small and mid-sized teams.
- Even if an org can afford to hire such skilled testers, there’s often not enough of them available to complete testing projects in the required time.
- In competitive markets, the demand for such programmers drives up salaries, which impacts the immediate budget as well as the overall financial strategy of the team/company.
Once again, AI can step in to bridge these gaps and inefficiencies: - AI tools accelerate scripting by analyzing software features and automatically generating commands. This directly reduces the need for manual effort and human intervention.
- AI is more flexible than human scripting. These engines produce accurate scripts that are usable across multiple programming languages and systems.
This adaptability services orgs using variant platforms and tools. Testers no longer have to deal with separate teams with specialization for different tech stacks. AI can unify scripting standards and test practice across divergent environments. - As is obvious, using AI tools is key to reducing dependence on highly skilled testers. Since it can automate the most technical parts of scripting, testers can focus on strategy and innovation. It also reduces the money required to hire the right people.
Test Execution
As with the previous sections, let’s begin with the current challenges in the test execution phase:
- Heavy reliance on HTML scripts becomes an issue when web apps have to keep dealing with recurring updates or dynamic changes. When this happens, the scripts may not be able to find and interact with the updated HTML elements (not accurately, at least).
In other words, test scripts have to be constantly maintained for error-free test execution. - Manual testers often have to verify the visual appearance of software GUIs. Like all manual actions, this process is time-intensive and prone to error – especially when they are testing complex interfaces and layered features.
Here’s how AI solves these operational bottlenecks: - AI can enhance visual inspections by replicating real user interactions with the AI. It uses computer vision and machine learning to examine GUI elements almost as well as humans would do, but without getting tired.
It automatically identifies visual anomalies – mis-shaped text, wrong colors and issues with scaling. - AI systems do not get tired, lose focus, get bored or need breaks. It can run long and repetitive tests with perfect accuracy from beginning to end. Teams can run more tests with no compromise in quality of results.
Use AI to run more tests in a fraction of the time. Simplify iterative testing and give developers more frequent and pointed feedback. Schedule tests during off hours, and complete cycles without disrupting regular workflows.
Overall, how does AI do better than manual testers?
AI provides significant advantages in terms of availability, efficiency and cost. They are crucial to create the robust, reliable and fast testing process required to maintain current software quality standards.
- 24×7 availability. AI systems work around the clock without restricting hours or needing breaks/coffee/sleep. This capacity is ideal for continuous testing cycles in Agile and DevOps landscapes.
Run tests any time, maximize productivity and get instant feedback without any possibility of errors in the process. - AI can run the same routine tasks again and again without getting bored or tired. Regression tests, performance tests and certain aspects of UI testing can be easily replicated via AI. The same tests can be run repeatedly without any mistakes.
This consistency is what keeps an SDLC reliable by eliminating the “chance” introduced by human error and fatigue. - AI testing also brings cost efficiency to the table. While the initial setup and implementation does require some investment, it doesn’t always have to be the case. For example, an application like Coco is already equipped with AI-focused testing capabilities.
Users of Coco can enjoy the benefits of AI in testing without any additional investment on their part. That means businesses can thrive off AI-driven testing – reduced staffing needs, decreased time and resources required to execute tests, and overall lower long-term operational costs.
The Shift Towards AI is Inevitable…
It’s not a question of if, but when.
AI models bring such pivotal advantages to the testing process that its adoption in all industries is only a matter of time. AI models can improve all stages of the process – test design, scripting, execution and monitoring. It facilitates better test coverage, speeds up scripting and makes test runs incredibly efficient.
With Coco, small and mid-size teams can leverage 24×7 availability, resistance to boredom and fatigue, and cost efficiency for their test pipelines. In other words, continuous testing operations accelerate and multiply while human resources become less necessary for routine tasks.
Coco requires no initial investment thanks to its pre-established infrastructure. Your entry barrier into AI-driven testing becomes non-existent, and your testing strategy can meet the standards of competition and innovation.
