Continuous Feedback builds Digital Quality
One of the key drivers of the lean-agile paradigm is continuous development and therefore continuous testing. As software development becomes more complex and delivery cycles become shorter, the efficiency of feedback loop is more important than ever. But manual testing has scalability and speed challenges that can impact the release timelines. The adoption of AI and machine learning within the testing ecosystem has thus provided much-needed pace and accuracy to deliver continuously.
Artificial intelligence supports automation and leverages machine learning to solve QA and testing challenges. There is an obvious shift from manual testing to AI-powered, automated testing. AI-enabled testing seems to be the natural progression after automation.
The future is AI-enabled testing
The use of AI in software testing has its share of pros and cons. One of the biggest reasons to adopt artificial intelligence is to replace repetitive testing activities that have a preset input and output data.
AI has the ability to mine defects and outline test suites that find defects. It can also identify application areas most susceptible to these bugs. This information is incredibly valuable to the tester. Who can make intelligent and informed decisions regarding test suite optimization and test coverage at the speed of business.
Tools such as QMetry Wisdom make test automation smarter by using software quality metrics. Wisdom works by providing a complete drill down of test results giving a holistic view of root causes and failures. It uses machine learning to suggest test action models to solve these issues.
AI’s instant feedback mechanism is a strong selling point that gives testers instant evaluation and feedback. This shortens the feedback loop considerably.
Predictive and Prescriptive analytics
Predictive and prescriptive analytics use AI and machine learning algorithms to anticipate defects earlier on. The algorithms can offer suitable recommendations against these defects to speed up the release cycle. Prescriptive analytics can optimize both quality and speed of your deliverables. Here’s how:
- By shortening the testing cycles and optimizing your testing processes
- Predicting defect ranges and risk of modules for future versions
- Evaluating factors that lead to greater application lifecycle efficiencies and those that don’t
- Converting unstructured data into actionable insights for enhancing test automation
Chatbots were one of the most widely adopted technology trends in 2017. With several messenger apps and chatbots becoming all-pervasive, the testing requirements of chatbots have also become complex and multi-faceted.
BOT testers use AI to automate the testing and digital quality of messenger chatbots and various other bot services. They accelerate the testing of complicated interaction flows and prevent manual testing errors. One of the primary challenges in chatbot apps is intent validation. It is here that tools like QMetry Bot Tester Version 1.0 for example, can validate the intent derived for various texts typed by users.
Another useful application of machine learning in QA is for sentiment analysis of customer feedback from various social and other channels. AI can be used efficiently to mine end user feedback and collate insights on user needs, challenges, issues that impact customer relationships.
Impact on manual testing
AI and machine learning aid automation and resolve some of the scalability and speed challenges of DevOps teams. This means that testers can easily automate repetitive testing tasks and focus on the creative, logical and reasoning aspects of testing that will lead to better outcomes.
AI and machine learning are fast becoming the new normal for digital quality. The application and advantages of using AI for QA are diverse from optimizing test management to faster time-to-market and defect forensics. With this the tools and processes also need to evolve to reap the benefits.
Leverage QMetry’s intelligent software quality metrics and test coverage intelligence for testing smarter, faster and taking your development efficiency to the next level.