A study highlights that 62% of users will abandon an app due to bugs. Another surgery posits that 53% of users will remove or uninstall an app riddled with errors, crashes, and freezes. These data give a clear picture: software testing directly affects customer experience (CX). In the current competitive market, companies are constantly pressured to continually deploy software applications based on market demand.
Parallelly, developers are expected to develop high-quality apps, ensuring enhanced customer experience. Software testing is thus paramount. However, traditional software testing methodologies are time-consuming and expensive, and this is where artificial intelligence (AI) steps in.
Thanks to AI, the landscape of software testing has undergone a significant transformation. It has increased the pace of the testing process, enabling business owners to deploy their products within weeks.
How AI Can Enhance Software Testing
AI in software testing has reimagined our approach to testing. The substantial use of DevOps practices and agile methodologies in software development has raised the need for AI in software testing due to its competence in conducting continuous testing.
AI automates repetitive tasks and quickly identifies potential risks and bugs in software, empowering the software testing process to be more efficient, effective, and unfailing. AI’s ability to scan and evaluate large datasets and unknown anomalies enables testers to swiftly work on the issues concerned and release error-free apps for users.
Types of AI-driven techniques in software testing
AI-driven software testing chiefly consists of 4 types of techniques. These include:
- Machine Learning (ML) based test automation: This method generates new test scenarios based on existing data and user behavior. It helps with application interface changes and significantly reduces the maintenance time for dynamic applications, streamlining test automation.
- Natural Language Processing (NLP): NLP test automation helps comprehend user test cases, requirements, and specifications, producing relevant automated test scripts. It elevates test case development, leading to effective collaboration between organization stakeholders and testers.
- Visual testing: Visual testing is effective in detecting visual inconsistencies and ensuring the software’s stable UI rendering across devices.
- Anomaly detection: Many companies are embracing AI/ ML to detect anomalies in quality assurance (QA) effectively. For example, it helps to understand probable malfunctions and issues in software by highlighting deviated results or patterns.
Benefits of AI-empowered software testing
Now that we know the techniques of AI in software testing let us dig deeper and explore how AI enhances end-to-end software testing:
Enhanced Test Coverage and Quality
Determining whether your tests include all probable case scenarios can be tricky. Using AI in software testing resolves this issue. AI-driven testing boosts test coverage by 85%. A MIT Technology Review find out that 96% of the executives at the leader group agree that scaling AI and ML for use case generation is a high priority strategy to accelerate business value.
AI algorithms skim through and analyze large datasets to identify potential gaps and vulnerabilities that might be undetectable to the naked eye. For example, while testing a complex software application, traditional testing will encompass the obvious case scenarios. However, AI enables testers to delve deeper and uncover and remove elusive bugs prior to the software release. This, in turn, significantly enhances the quality of software in its entirety, empowering testers to adapt to the changing requirements and modify app issues at scale.
End-to-end software testing with AIT (Artificial Intelligence Team)
Another benefit of AI in QA is enhanced end-to-end software testing, with the combined effort of human expertise and AI. A recent study by Lambda Test highlights that nearly 46% of testers effectively use AI tools. The highest usage of these tools is either in test case creation or in test data generation.
Testers and AI work as a team to achieve the best of both worlds. Testers use their professional expertise and use-case knowledge and implement AI to unearth potential ‘unknown-unknowns’ risks. This enables testers to consider potential software risks from multiple angles and devise robust testing solutions.
Evoke, for example, assisted a client in testing and deploying 20+ applications through a hybrid testing methodology. Our expert team implemented manual software testing and AI-powered automated scans to assess the software applications. 316 vulnerabilities were found, 29 of which were critical, and 122 needed high attention. Our team ensured 24×7 support to address the ‘configuration and deployment management vulnerabilities. ‘
Humans and AI together can do wonders!
AI-based software testing saves time
Thanks to the wave of digital transformation, the pressure to ‘speed up’ with market demand is high—and quality engineering is no exception. Software developers and testers are constantly under pressure to develop and deploy high-quality applications within set timelines. AI plays a crucial role in this regard. It runs regression tests, finds anomalies, and checks performance with an accelerated testing cycle, ensuring quality throughout the process.
A recent 2023 report states that 55% of businesses have invested in AI solutions to enhance their quality engineering. Furthermore, it reduces the rate of defects by 29%, enabling testers to focus on developing detailed end-to-end software testing strategies. For instance, Evoke’s expert QA team uses AI-powered tools to deliver highly secured and robust app security testing solutions to clients. It empowers the client to transform the DevSecOps culture within the organization. Admittedly, time-saving is one of the major benefits of AI-enabled QA, prompting businesses to embrace it for faster software testing and reduced time-to-market app delivery.
Enhanced software testing accuracy
Guaranteeing full accuracy devoid of errors in software testing is a Herculean feat—humanly speaking. Oversighting a small amount of information or a chance of typographical error can lead to erroneous results.
This, in turn, immediately raises reliability and credibility concerns. This is where artificial intelligence saves the day. For example, during data processing or algorithm testing, AI can create a diverse category of inputs, ensuring the expected results and outputs correspond. Its precision and high accuracy avoid unnecessary deviations, ensuring applications work smoothly.
Real-time monitoring
Speaking of precision, the smart assistant’s ability to conduct real-time monitoring is a major fundamental metric in QA. This explains the growing shift towards AI for real-time software testing.
An HBR study found that it reduces testing costs by 50% and enhances software quality by 20%. More businesses are acknowledging its feasibility and efficiency. From providing data insights and generating risk case scenarios to anomaly filtering and detection, real-time monitoring empowers testers to identify and resolve quality challenges at the early stage of software development. For instance, our experts at Evoke leveraged NLP to automate spam detection, simplifying inbox management and enhancing productivity. Using AI-enabled testing tools enables us to perform real-time monitoring and reporting that ensures high-performance apps with minimal need for ‘fixing and patching.’
Enhanced software testing boosts customer engagement:
Quality software testing enhances app functionality, and this directly enhances customer engagement:
- Reduced bugs and crashes, positive customer experience Bugs and crashes are common reasons for customer churn. Total eradication of issues is not possible since unexpected errors can occur; however, AI in software testing can swiftly identify and resolve the errors. Evoke’s agile software testing automation methodology offers prompt resolution of software application issues, ensuring a positive customer experience.
- Increased customer trust Consistent software performance creates a positive quality perception among users. Customers develop trust and reliability in the software concerned, and our QA experts ensure that. Equipped with 250+ resources and 150+ ISTQB-certified professionals, our experts implement AI/ ML automation frameworks to deploy top-notch software swiftly. It enables businesses to elevate customer engagement.
- High customer engagement Additionally, better testing means seamless performance and a responsive application, enhancing customer engagement and confidence.
Conclusion
AI in software testing is a game-changing tool, and it is here to stay. Businesses integrating AI in QA will lead the market. However, understanding the ‘how to’ along with its purpose is vital for successful digital transformation. This is where Evoke’s QA expertise lies. From test advisory and tool assessment to test automation strategies and their implementation, our QA solutions offer flawless software, enabling you to focus more on business and connect with your customers.
