AI-Augmented TestingA Developer’s Superpower.

Ai In Software Development And Testing

The evolution of AI is quickly becoming a group project, with multiple Big Tech companies such as Google and Microsoft building powerful AI/ML developer tools. AI is no longer considered a “nice to have” - today, it’s a critical part of digital transformation to increase business’ stability, flexibility, and scalability.

Software development and applications are at the core of these digitalisation initiatives. However, too many companies still need to rely on legacy tools when it comes to testing. This remains a challenge, as modernising testing isn’t always perceived as being of business value, with other technology functions often invested in first. Quality Assurance (QA) is also often viewed as secondary to development efforts, with DevOps opting for obsolete ‘good-enough’ tools that lack scalability and enterprise-grade features.

Fortunately, more than 86% of CIOs and senior tech leaders consider AI a key criterion for selecting QA solutions. In addition, with more organisations shifting their workload to the cloud, there is an urgent need for low-code/ no-code applications, and AIpowered test automation is key for this transition to be successful.

While some questions may remain around AI adoption, it’s clear that the most valuable teams within enterprises are those that work to harness the full power of the tools available. In the case of AI, its adoption will help to further intertwine developers and testers to work more efficiently together, thereby ensuring that their work will contribute direct business value. In today’s fast-moving world it is the fast and efficient businesses that will thrive. So, how can DevOps teams leverage AI positively in order to fuel innovation and positively impact their business operations?

Understanding The Ai Benefits In Testing

It’s imperative for organisations to understand how much automation they are trying to achieve. According to recent research from Forrester, there are five levels of automation that can be applied to software testing, which help clarify where human intervention will diminish as automation evolves. Whilst most companies might aspire to achieve a fully automated state to increase business velocity, most companies have only achieved levels 0 to 2 out of the possible 5 levels of automation, which means machines can help build API, UI or unit tests after showing them how. So, let’s have a closer look at current use cases of AI in the testing market:

Visual Testing - used for finding cosmetic bugs in applications by comparing a baseline screenshot to a future one, visual testing helps discover issues not picked up by functional testing tools querying the Document Object Model (DOM). It’s imperative to ensure that applications run smoothly, however, it’s impossible for DevOps to test every single device manually. Equally, applications can pass functionality tests whilst failing to scale when, for example, switching from Android to iOS devices. This is where AI-powered solutions can help detect visual issues at scale. However, a human tester is still required to review and accept suggestions, and AI will need to be taught which visual cues to look out for.

Test building from mock-ups –with the help of ML, testers can create the assets by scanning the mock-up designs of an application. This feature allows for testing to start earlier in the development process thus leading to fewer issues as time pressures mount. This also helps accelerate UI maturity and allows testers to run the same test cases without modifications. With many applications having native web and mobile interfaces, reusing the same tests allows DevOps to ship products faster with higher quality. Should any drastic changes be made, self-healing AI - a system to auto-fix broken test cases - can be applied to detect visual changes such as location, control type and design.

Intelligent bug hunting - AI can analyse common failure patterns and prioritise tests with the most common bugs. However, in this case, AI is limited to analysing test case failure data, creating blind spots in high-risk areas of an application; therefore, human input is still required.

Smart impact analytics - powered by AI - helps prioritise what test should be run to verify changes to applications in code, configuration, integration, and security. This dramatically increases risk coverage by focusing on the right testing.

Test design and optimisationhelps analyse production data and process discovery and automatically generates the right combination of test cases. As a result, testers can prioritise features and test cases.

As organisations rush to implement and deliver new digital innovations faster than ever, many still need to rely on clunky and high-maintenance legacy tools, leaving critical gaps in their business operations. AI integration will provide a helping hand for DevOps by empowering exploratory testers to find more bugs and issues upfront, streamlining the process from development to deployment.

Implementing augmentation helps inform decision-making processes for DevOps teams by finding patterns and identifying outliers. This frees up time for teams to focus their brain power on the tasks that they want to do and that are more strategically important to the business.