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Why You Need AI in Your Continuous Testing Strategy

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Why You Need AI in Your Continuous Testing Strategy Why you need AI in your continuous testing strategy tricentis.com © 2021 Tricentis GmbH. All Rights Reserved | 1 Contents Why do we need AI in software testing?......................................................................3 How are business leaders responding to AI? ..............................................................5 How can AI help your testing practice?........................................................................6 What can you do today?.................................................................................................7 Executive Summary At the top of every CIO’s mind is how to drive for- AI is being implemented in an infinite number of ward innovation at speed, increase levels of pro- ways in testing: ductivity, deliver superior and highly scalable dig- • Creating automation based on an applica- ital products and services that increase customer tion’s mockup or simple design satisfaction, and save costs – all at the same time. Unfortunately, without modernizing your testing • Handling maintenance of rapidly changing with intelligent automation, and treating testing applications as a strategic component to your digital success, • Automating hard-to-reach interfaces like you won’t be as competitive and you can’t inno- remote applications vate as quickly. How do we get there? • Performing self-healing upon changes to Artificial intelligence (AI) and machine learning object recognition (ML) technologies are already being adopted by • Reducing the testing scope in large regres- businesses today in QA and test automation to sion suites through smart impact analysis deliver more resilience, agility, speed, and busi- ness growth. More than half of business leaders All these trends show us that smart technologies have witnessed how AI solutions have already will undoubtedly increase cost efficiency, reduce the need for manual testing, shorten time to increased productivity (IDC FutureScape, 2018). Many more executives (63%) say that reducing market, and, most important, help create and costs is a major factor in adopting AI solutions. sustain the circle of continuous testing and continuous quality. Gartner (2019)1 agrees: improved defect detec- tion (48%), reduction in test maintenance costs This white paper explores why business leaders (42%), and improved test coverage (41%) are and CIOs urgently need to adopt AI in their contin- some of the top benefits that AI/ML brings to uous testing strategy, and also provides some software testing. key considerations on how AI/ML can be used to AI is a “must-have” when selecting new QA solu- accelerate business growth, innovation, and tions, products, or tools – and 86% of respon- competitive advantage. dents agree (World Quality Report, 2020-2021). 1 Gartner (2019). Critical Capabilities for Software Test Automation. (Based on the 2019 Software Quality Tools and Practices Survey) tricentis.com © 2021 Tricentis GmbH. All Rights Reserved | 2 Why do we need AI in software testing? This section dives into some of the challenges and market trends of software testing in order to address why new approaches such as AI/ML are urgently needed to drive digital success. A rapid rise in modern application Test maintenance is a huge cost and development resource expenditure Today’s leading enterprises strive to deliver How do you go from releasing once per year to high-performance, highly scalable, and always-on twice every month? This question trickles through digital services. These services are built on the minds of most CIOs who want to increase custom “modern architectures” – an application speed of innovation and drive forward digital suc- stack that relies on new tiers of technology, such cess. Without modernizing testing with intelligent as microservices and containers, typically running automation, you’ll discover that the time and cost on cloud platforms. In fact, the development of of testing will grow disproportionately to your modern applications is surging at a dizzying rate. desired innovation speed. The point is that the IDC predicts that by 2023, over 500 million new maintenance of script-based testing tools make applications will be introduced. it impossible for you to accelerate your testing process and innovation. At the same time, customers rely on legacy systems and old technologies for various busi- With script-based testing approaches, you may ness-critical processes like processing customer initially get high automation rates. However, as data. Technology is often pulling us in multiple, your application evolves and new features get competing directions. We’ve been slow in coming added, and as your landscape of integrated up with better strategies for testing both modern applications and systems increases in scope, and legacy applications. Even the earliest adopt- the maintenance that’s required for you to keep ers of DevOps and agile practices cannot keep up up with those releases will simply become too with the fast-moving technological changes. difficult, resource-heavy, and cost-intensive. Your automation rates will plummet, and your manual The COVID-19 pandemic has also shifted much testing will skyrocket. of the world to remote work environments. Organizations who adopt modern applications We need alternative methods to script-based test- to maintain agility are already realizing benefits. ing. AI/ML approaches to test automation already According to the VMware global survey (2020), exist and are being implemented today to reduce cloud-native solutions are allowing organizations the time and cost of burdensome test mainte- to enable remote workforces, push quick up- nance. AI-powered test automation uses neural dates in response to a changing landscape, and networks to see and steer elements on a page, maintain reliable uptime. This will lead to an even without needing to look at the technical layer. greater demand for virtual and remote desktop interfaces like Citrix and VMware. UI testing is slow, and being slow is costly To cope with these modernization initiatives, The sooner you find a bug, the cheaper it is to fix advanced AI test automation capabilities are it. This is why it is essential to test as early as pos- already being applied. Organizations can reduce sible in the development lifecycle. DZone Trend modernization costs and efforts, as well as build Report (2020) states that more and more orga- enhanced test automation for remote and virtual nizations are shifting their attitude to “test early apps, including legacy and modern apps. and test often.” One major challenge, however, tricentis.com © 2021 Tricentis GmbH. All Rights Reserved | 3 is that the User Interface (UI) of an application In order to close the skills shortage gap as well as has to be built before any test automation can meet the demands of the proliferation of new ap- occur. As a result, UI automation continues to be plications, it has become more urgent than ever to a bottleneck in quicker testing, development, and embed business users or SMEs into the test auto- delivery. mation process. AI can augment the amount of test automation that needs to be achieved by business- According to the 2020-21 World Quality Report, es today and thereby help narrow the skills gap. testing still represents nearly 30% of IT budgets. With AI-powered test automation, we can finally Importance of low-code/no-code solutions create tests as early as possible in the develop- ment lifecycle – even before the UI is created. AI/ Pega (2020) states that code is a major obstacle ML technology provides you with the capability to for IT and business collaboration. In fact, create tests in the design phase – based on a sim- underutilizing low-code platforms is a major ple mockup – which enables an extreme Shift Left missed business opportunity. Adopting low-code approach to testing. Testing earlier means testing or no-code solutions will empower business faster, which can reduce the cost of testing and al- users to take ownership of their applications low you to ship more innovation to your customer. whilst being guided by IT on best practices and security. AI/ML is already being incorporated into Suitable for multi-skilled personas low-code/no-code platforms in order to enhance the automation of business workflows and help Enterprises today prefer a testing platform that is embed the business user even deeper into the more accessible to a broader range of skill levels test automation process. (Forrester Wave Q2, 2020). Script-based test automation leaves non-technical and business DevOps means more automation users out of the testing game, leading to an ineffi- cient utilization of your organization’s resources. DevOps demands more automation in order to meet faster delivery without compromising on In testing, a skills shortage could translate to a quality. Continuous testing, or integrating test- lack of coding and scripting knowledge to create ing continuously throughout the CI/CD pipeline, tests, as well as a lack of domain knowledge of a is important so that testing doesn’t become a business application. Business users or subject bottleneck in the software delivery process. AI is matter experts (SMEs) working in industry-spe- already playing an important role in scaling the cific domains like banking, retail, healthcare, and DevOps process, particularly in achieving more telecom have the advantage of applying domain automation throughout the CI/CD pipeline: knowledge of an application for more efficient and comprehensive testing. Continuous integration (CI) Continuous
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