Redefining Test Coverage with AI and Machine Learning

Learn how Deep Learning AI, and Machine Learning Models can help you shift left
You want to ensure high-quality and bug-free applications. But here’s the struggle: conventional software testing methods are just unable to keep up with the pace at which you’re releasing. You’re potentially facing delayed releases, reduced product quality, and higher costs.

But what if the testing landscape could be transformed into something simpler? What if we used AI and Machine Learning to redefine and refine the parameters of coverage and detection? All of this, and more, is what this blog will talk about, as we explore how AI systems are in high demand, and how they are helping change modern testing landscapes for the better.

Current testing methods have their limitations
Delivering high-quality, reliable software is no one man’s feat. It requires efficient collaboration across various teams and processes throughout the testing cycles. These include monitoring the standardization of testing practices, managing distributed test environments, and coordinating efforts among team members located in various locations. Let’s take a deeper dive into some other such challenges:

Application Development is Fragmented
When there are hundreds or thousands of applications, usually driven by new acquisitions, they lead to fragmented testing of Agile development. This in turn makes the testing process time-consuming and resource-intensive.
Complex Frameworks make testing time-consuming
Code-based testing requires complex frameworks that are time-consuming to maintain, difficult to scale, and challenging to integrate.
Complex Infrastructures Require more In-Depth Testing
Due to its complexity, the proliferation of operating systems and devices requires significantly increased testing infrastructure. This leads to higher costs.
Collaborating with Siloed Teams is Difficult
When different teams work in isolation from one another, making it difficult to test end-to-end user and customer journeys, especially when dealing with large amounts of data. This also causes communication breakdown, duplication of efforts, and inconsistency in testing.
Inadequate Testing Results Cause Higher Risks
Lack of testing expertise, insufficient resources, time constraints, and lack of automation leads to inadequate testing results. This often becomes a challenge for organizations as it causes defects that go unnoticed, performance issues that are not identified, and compliance issues not meet regulatory requirements.

Improving Test Coverage with AI and ML
Achieving comprehensive test coverage has been a longstanding challenge in software testing. AI and ML have opened new possibilities to overcome this. According to a recent study by MarketsandMarkets, the global AI in software testing market is expected to reach $1.7 billion by 2025, with a compound annual growth rate (CAGR) of 33.7%. Leading players in this space include Google, Microsoft, IBM, and innovative startups such as Applitools and Functionalize.

Here is how AI and ML tools can help you stay ahead of the curve, especially when you’re working with multiple applications at once:

Accelerate Testing across Web, Mobile, and APIs
By automating testing processes, teams can achieve comprehensive test coverage, identify potential issues, and ensure the reliability of their web applications. Through AI and ML-powered web automation tools, large engineering teams are empowered to build, test, and deploy higher quality, error-free web applications faster and more efficiently.
Power Through with Codeless Testing
Codeless automation capabilities help enhance application quality and improve speed to market. With codeless automated testing, teams can validate code functionality without the need for extensive manual coding. This streamlined approach saves time and effort while ensuring the reliability and security of the software and its integrations.
AI-backed Automated Testing
Leveraging AI and ML tools to provide advanced capabilities for test automation through harnessing AI algorithms, teams automatically identify and fix test scripts, comprehensively test mobile applications, and analyze test data. These AI-driven features improve application quality, increase efficiency, and reduce time to market, ensuring reliable and high-performing software.
Reduce Costs of Manual testing
The increased adoption of AI and ML in software testing brings promising cost-saving opportunities. By automating repetitive and time-consuming tasks, organizations can optimize their testing efforts, reduce the need for extensive manual testing, and speed up the overall testing process

Overall, these accelerated testing processes powered by AI and ML tools allow for faster time-to-market and increased productivity.

Shifting Left with AI Software Testing
Shift Left is a practice intended to find and prevent defects early in the software delivery process. The idea is to improve quality by moving tasks to the left as early in the lifecycle as possible.

With AI and ML projects in the picture, software testing, and shifting left, is transforming software testing, enabling organizations to achieve comprehensive test coverage and detect defects early in the software development life cycle. With industry leaders like Google, Microsoft, and IBM investing in AI-powered testing solutions, the market is poised for exponential growth.

Comprehensive test automation solutions powered by AI and ML provide global engineering teams with the tools they need to ship more reliable software. The power of AI and ML when combined with Quality Assurance, Centralized Reporting, Deep Machine Learning Models, System Intelligence, and Seamless Integrations will truly redefine software testing processes for the better. Qyrus is at the forefront of the shift towards AI and ML-powered software testing. With its innovative platform, and patented AI Algorithms to build its all-in-one testing platform. In fact, here are some patented Qyrus features used by global engineering teams every day to test software faster:

Automated Test Maintenance with Healer
Qyrus’ antidote for test flakiness, brittleness, and fragmentation, Healer is a lifesaver for testers. It reduces efforts in areas like script maintenance with its self-healing capabilities. Healer utilizes AI to detect test step changes and perform automatic script corrections. It reduces script rebuilding efforts in case of UI changes or redevelopment initiatives. This eliminates situations like manual object identification and determination of locator values in case of “object not found” errors.
Exploratory Testing with Rover
Rover is a cutting-edge autonomous testing solution that uses deep reinforcement learning to generate intelligent tests, eliminating the need for human involvement. It operates as a fully automated black-box testing solution, requiring only your application. Rover generates and executes real-time test inputs effortlessly, without relying on an expert tester to determine testing scope, timing, or inputs. It represents a groundbreaking advancement in testing.
AI-Based Conversational Interface Testing with Sage
Qyrus’ Sage is a Conversational BOT Testing Platform, an AI that tests an AI. It provides users (“a chatbot developer”) the ability to test their bot in a highly automated and regressive manner. Qyrus Sage provides AI capabilities specifically tailored for testing AI-based conversational interfaces. With Sage, companies can efficiently validate and enhance the quality of their AI-driven conversational interfaces without the need for exclusive test script development.
Natural Language Test Building with QyrusBot
QyrusBot, a powerful tool offered by Qyrus, enables natural language test building. Testers can write or speak their test cases, streamlining the test creation process and enhancing productivity.

If you would like to streamline the software testing processes powered by AI and ML to see significant improvement in software development outcomes and produce high-quality code, try Qyrus for free now.

Authors