Customers want highly engaging, top-quality digital experiences. For development teams that want to maintain a competitive edge, this means being able to evolve and scale at a more rapid rate than ever before. However, organizations cannot afford to focus on speed over quality, or quality over speed during software development and testing. Instead, both components, speed, and quality, must be balanced during software testing to produce superior user experiences.
That’s simpler said than done. While software delivery cycle times may be decreasing, the complexity needed to deliver great user experiences continues to spiral upwards. Users expect the highest quality experiences - and teams must deliver to stay relevant and successful. The demands placed on development teams are greater than ever, which makes traditional approaches to achieving software quality inadequate.
The essence of quality engineering has always remained the same, but the tactics used to achieve that high quality is evolving through the adoption of Machine Learning and Artificial Intelligence.
The challenges of balancing speed and quality
The adoption of machine learning in software development and quality engineering is driven by the critical need to balance quality code with faster cycle times.
Software developers and quality engineers face a wide variety of challenges. Companies expect increased release demands, along with increased app complexity. Cloud service options continue to expand, and DevOps teams face new challenges: different kinds of workload, stricter compliance requirements, and an expansion of target device counts. Factor in the sheer amount of releases in a month and it’s clear that traditional test automation solutions aren’t able scale along with these growing delivery challenges.
The landscape of software testing and development is tougher than ever before. In order to deal with an onslaught of ever-evolving challenges, developers can utilize machine learning and AI to streamline their test automation practices and scale to meet the increased pace of software delivery.
Machine Learning and AI for Higher Quality Software
With the evolution of the software testing landscape being driven by business-critical delivery, a recent EMA (Enterprise Management Associates) report, Disrupting the Economics of Software Testing Through AI, outlines the five key categories of test automation that machine learning and AI are helping to streamline and automate portions of testing cycles. These functions include test creation and smart crawling, self-healing, visual inspection, coverage detection, and anomaly detection.
Source: EAM Report
Machine Learning and AI can be used to automatically uncover new or changed test requirements. Test creation and smart crawling AI tools streamline the process of writing tests and reduce the risk of gaps in testing coverage through the automatic creation of new tests or fixing tests to match new requirements. Smart crawling AI analyzes changes in your application and uses NLP (natural language processing) on documented requirements to identify these gaps.
AI also helps to boost speed and quality with automated identification and eradication of broken test workflows with self-healing AI, saving developers precious time and improving application quality. Testim and Parasoft offer testing automation capabilities within the self-healing category.
AI and machine learning are also taking on user experience with visual inspection capabilities. Products including Applitools train deep learning models to assess an app from the eyes of an end user, giving holistic coverage of the overall user experience. Visual inspection AI is adaptable to new situations without needing code-based rules maintenance.
The coverage detection AI category can automatically detect different paths the end user can take in an application and report code coverage gaps. Similar to visual inspection, the coverage detection category focuses on end-user testing optimization to enhance the efficacy of testing.
The final AI category is anomaly detection which automatically detects inconsistent system behavior compared to the ML model predictions. This type of test automation promotes developer productivity by automatically alerting and prioritizing tasks.
The Pioneer in AI/Machine Learning Testing Categories
EMA has missed one of the most exciting AI/ML categories to emerge in recent years. This category is predictive test selection with AI/ML. With this approach, a machine learning model analyzes and learns from your test suites over time and identifies the tests that are meaningful AND meaningless to run for a change, saving developer time with reduced cycle times.
Launchable is the pioneer in Predictive Test Selection SaaS, speeding up your development feedback loop. Incorporating machine learning into test creation and selection means running fewer unnecessary tests, which ultimately means happier teams.
With Launchable’s Flaky Test Insights, teams can also identify the top flaky tests based on flakiness scores (how likely they are to be flaky) and focus on assessing the right tests. By eliminating flaky tests, developers can be confident in their code and focus on delivering quality code releases, faster.
What are the best use cases for ML/AI in software development?
In software development, some areas are better suited to automation than others. Three types of tests to consider incorporating machine learning models to further optimize your cycle times are unit testing, integration testing, and functional testing.
Typically considered the fastest kind of testing, unit testing tests a single unit of code, or the smallest piece of code that can be isolated in a system. In order to speed up the velocity of unit tests, machine learning can be deployed in cases where developers have an application with a large number of unit tests. If a developer commonly runs their test suite for 30 minutes four times a day, they can shift left their critical tests to find risks earlier. By using Launchable’s machine learning, developers slash total test execution time with smarter testing.
Another great use of machine learning and AI to boost testing speed and software quality is integration testing. Integration testing is the process of ensuring the various integrated modules of an application all work together. Integration testing is a notoriously time-consuming step, but it is also vital for successful software development. For teams that run smoke tests, Launchable takes the guesswork out of which tests are critical to run. And when changes are made, Predictive Test Selection can run tests relevant only to the latest changes, speeding up integration testing.
Automating functional tests with machine learning and AI can save serious time and effort, and even reduce test run times by 1/3 or more.
The implementation of machine learning and AI into software testing cycles can help developers and organizations increase their productivity to keep up with consumer demands for more complex digital experiences.
Machine learning advancement in testing automation will continue to improve the challenges software developers, quality engineers, and DevOps teams face during software testing cycles.