Use of Predictive Quality Analytics in Software Testing and Development

Intelligent Transformation – The Next Frontier: Predictive Quality Analytics through Artificial Intelligence”

The changes in technology today aren’t just rapid, they are exponential. Futurist and author Raw Kursweil defined the Law of Accelerating Returns stating that new technology leads the way for even more futuristic technology at an exponential rate. 

The real challenge with digital transformation is keeping pace with this landscape that is continuously shifting, evolving and growing. How do businesses keep up with this relentless pace? 

DevOps and Digital Transformation 

The DevOps practice emerged as an answer to this need for Agile operations and infrastructure. Using the Agile values, principles methods, and tools, the DevOps construct follows close collaboration of stakeholders, product management, developers and of course QAs. The practical implementation of this requires highly specific and Agile practices ranging from Scrum, XP, Kanban, tailor-made processes, to tactical techniques like stand-ups, planning pokers, CI and all the artifacts required to make it happen. 

Concepts like continuous delivery and continuous integration put Quality in the spotlight. By testing early, testing often, and shifting left, there is a significant advantage of improved product design and customer experience.  But the unyielding pace of development and shorter release velocity meant that Quality teams had a bigger role to play in the development cycle. Testing environments and teams cannot keep up with the need for faster, better and more. Enter test automation with its powerful capabilities to increase the efficiency, coverage, and effectiveness of your testing. 

Automated Testing tools have enabled 24/7 availability, higher regression and load testing, instant bug-fixing, less manual dependency, accuracy and reusability, and most importantly continuous testing. Automated Testing now has a starring role in the DevOps story.  

Continuous Testing – Sine Qua Non 

Continuous Testing is, therefore, the linchpin of automated CI/CD processes. Without Continuous Testing, there is no CI/CD. The Agile infrastructure and principles fail, and your DevOps model doesn’t work. 

However, as organizations shift focus to continuous testing, older approaches focused entirely on the user interface(UI) testing become less relevant. The Forrester Wave™: Modern Application

Functional Test Automation Tools, Q4 2016, End-to-End Automation and API Testing Are Key Differentiators. “Going beyond the UI and testing APIs is crucial to avoiding brittle test suites and increasing test coverage. AD&D cannot achieve ruthless automation by focusing solely on test execution automation; it also requires automating test design and process.” 

This is where disruptive technology trends like BOTs, Artificial Intelligence(AI) and Machine Learning(ML) come in to transform the entire Quality Lifecycle, as we know it. Intelligent Test Automation heralds the third wave in the Test Automation or DevOps journey with its pre-emptive, prescriptive and predictive approach to quality. 

Predictive Quality led by AI and ML 

A little background on why AI and ML are making great waves, especially in the software development ecosystem. Machines and computers are now smarter thanks to the ongoing research in machine learning and the application of solutions derived through analytics, business intelligence, and big data. Machine Learning uses statistical techniques to give computers or machines the ability to learn (that improves performance on a given task) with data. With the help of AI, computers can do something they previously could not. That is, draw conclusions based on data and patterns and take decisions. The underlying software is also able to feedback this information into the database for future decision making – creating a self-adaptive learning loop. 

Decades of research into artificial intelligence and natural language are now yielding great results. This research when combined with predictive analytics lays the foundation of cognitive analytics. 

Cognitive analytics uses human-like intelligence for certain activities such as understanding both the meaning of words in the given text but also the full context, recognizing complex images, etc. And cognitive computing brings various applications to reveal context and find answers that are hidden in vast volumes of data.

In the context of software testing, the impact is transformative. Intelligent Test Automation led by AI and ML enables a new level of complex problems computable. That is, problems with a certain level of ambiguity and uncertainty that require a sophisticated decision-making process. 

Intelligent Test Automation: Transforming End-to-End Continuous Quality

AI and ML have enabled the move from descriptive or reactive analytics to predictive and prescriptive analytics. Automated testing tools are now empowered to mine through reams of test data, understand patterns and forecast future trends and outcomes. 

One of the major challenges that organizations face today is the huge amounts of test data and test results, flakiness of tests, maintenance and decision-making with so much information. 

Using this data-driven approach, the software can predict failures, bottle-necks, error categories and productivity struggles across the project cycles. Is this enough coverage? Are you testing more than necessary? What should you prioritize and focus on? This information is exceptionally valuable when you have large volumes of test data and shorter deadlines. With machine learning, you can project data and make informed, proactive decisions. 

These intelligent insights help in deciding the further course of action, improving outcomes and ultimately create a constant feedback loop.

The range and depth of intelligent testing services and is transformative, optimizing all phases of the quality lifecycle. Intelligent Test Automation draws on test data from multiple systems and sources, allowing more concurrent, proactive fixes and granular feedback.

 Here are some ways the use of AI and ML-powered Automated Testing is transforming the way we build and test software:

  • Test Optimization: Removing duplicates, improve test coverage by bridging the gaps
  • Improved Coverage: End-to-end coverage in testing setup, management, control, and visibility
  • Defect prediction and prevention
  • Intelligent test results analysis to increase response time
  • Intelligently automated test execution and infrastructure optimization
  • More accurate and insightful decisions to measure release readiness, testing adequacy, and risk-index
  • Self-optimizing test suites
  • Automated validation of coding standards, test execution status

The insights are more instant, clear and actionable. You can monitor the results and track progress in real-time, ultimately improving slow or under-performing processes.  

There are significant productivity, efficiency and quality gain quantifiable from 30-40% in terms of time, cost of testing and testing redistribution gains. 

The next frontier in the Digital Renaissance era is technologies and tools that stand the test of quality, agility, efficiency, and innovation. Agile and DevOps are at the heart of this digital transformation journey to support the rapid pace of change. Backed by information, insights and AI/ML, Intelligent Test Automation is the game-changer that enterprises need.  

How a fintech business used Cognitive Defect Prevention

Consider the case of a prominent payment technology business that uses cognitive defect prevention and prescriptive analytics. The AI and ML-backed software use classification and adaptive learning to analyze past defect trends and correlated with risk parameters. Using standard deviation regression to estimate efforts and resources, it outlined a risk management process. This had three-fold benefits. Optimized coverage, better quality and user experience and higher than ever go-to-market speed.   

One of the biggest reasons to consider intelligent test automation, however, is adaptivity. Cognitive techniques learn and adapt as the data and goals change. By adopting intelligent test automation, enterprises not only accelerate their time-to-market but essentially future-proof their business against unmitigated risks in a continuously evolving business climate.

About the Author: Jasmine Chokshi

Jasmine Chokshi is a communication and writing professional with 15 years of experience. Interests include reading, writing, research, social media, the Internet and trends that dominate the digital world. Extensive journalistic and writing experience. Currently working as a content writer at QMetry.

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