Predictive analytics - An emerging trend in QA
Author: Indumathi Devi G., Project Manager, Infosys Validation Solutions
As digital transformation is rapidly changing business operations, quality assurance (QA) also has to change from traditional quality control to intelligent quality assurance. Nowadays, clients not only want to test software adequately, but also as early and thoroughly as possible. To accomplish these goals, it is important to opt for shift left testing and predict the failures even before the applications are handed over for testing. Today's business dynamics require QA professionals to make critical decisions quickly. It is imperative to make use of the avenues, such as customer feedback, defect data, and test results available at disposal to make prompt decisions.
What is Predictive analytics?
Predictive analytics is the practice of extracting useful information from data sets using statistical algorithms and machine learning in order to determine patterns and predict future outcomes and trends. It is a data-driven technique, which can be leveraged to predict failure points in testing activities and determine the future. It has the power to help optimize project data and make proactive decisions. Predictive analytics by using statistical algorithms helps us to identify patterns in the data and provides an accurate forecast on how the data behaves in the future.
Predictive analytics uses several algorithms to process the data. Some of them are as follows:
- Regression algorithms
- Time series analysis
- Machine learning
Why analytics in QA?
Predictive analytics is widely used in most of the industries today. Testing has never been an easy activity and it involves lot of aspects that need to be efficiently managed for better results. Need of the hour is, software and especially QA teams have to leverage analytics to streamline and seamlessly perform software testing activities.
Today, various technologies that we use and tasks we perform in software testing life cycle (STLC) generate enormous amounts of data. Storing that data, and analyzing it using state-of-the-art tools and analytic solutions in a timely manner will make that same data work for you instead of simply taking up space on a hard drive.
Predictive analytics is not a one-off activity. We need to continually analyze and infer insights and make adjustments in QA practice for better results. It is also important to have sufficient data to make reasonable predictions.
Customer is the king and his feedback matters a lot
It is business critical to listen and react to the valuable customers' feedback. Sentiment analysis is extremely useful in social media monitoring as it allows us to understand the customer feedback about certain products or applications. Sentiment analytic frameworks will make that process quicker and easier than ever before.
Collect the customer sentiments through proven means from the possible sources and use analytics techniques to arrive at insights. This helps QA teams to identify the areas they need to focus based on the issues reported such as compatibility issues, performance issues or functional issues faced by customers. Embrace customer centricity while strategizing QA to deliver better quality and improved customer experience.
Customer feedback analytics
- Helps in identifying key issues of the customers when they use the digital channels
- Provides insights to prioritize testing and increase QA efficiency
Make social analytics as one of the key inputs to formulate the QA strategy. Data captured from social media gives insights into customer's sentiments. It helps in identifying areas of focus from past performance based on negative sentiments and helps in decision making. It provides a 360-degree view on behavior of applications in production as well as its impact on customer's sentiments. It helps QA teams to minimize risks, increase agility, and bring in customer centricity to QA approach.
Information is wealth and make the best use of it
Each and every task performed in QA process generates enormous amounts of data. Every time you run a test, you are creating log files, logging defects compiling test results and reports. Defect logs, test results, production incidents, project documentation, application log files carry lot of details and when used intelligently can make wonders.
Examine defects we identify in test and production environments and assess how that impacts customer experience. Identify critical issue patterns and align test scenarios to ensure adequate coverage. Data, combined with predictive analytics algorithms, can allow you to find patterns in data and it can help you make increasingly accurate predictions about the future failures based on that data. For example, optimize the order processing workflow in a retail website based on the data that shows on which step many of the customers log out of the site during a transaction. Root cause analysis of defect data will reveal the hotspots of the application and will help in risk-based testing. Analyzing defects help QA teams to prioritize and optimize testing and helps for faster and focused QA.
Apply machine learning algorithms to mine the test case repository, arrive at an optimized regression suite, and figure out any duplicate or redundant test cases. Using analytics on the previous test result will help in forecasting the future pass rate and will help QA teams to focus on the unstable modules of the application.
QA teams have to opt for tools, which continuously monitor the application log files and trigger the relevant test scripts in an unattended manner. This will help in early detection of potential failure areas to take preventive action, thus reducing potential defects.
Going beyond the traditional QA methodologies and taking an analytics-based approach has become key factor in the next generation of QA. Predictive analytics helps in predicting the future failure looking at the past data and taking the proactive measures for future.
Predictive analytics in QA
- Gives insights on the customer sentiments that help in customer-centric QA
- Provides insights to start, stop, and prioritize testing
- Increases testing efficiency and predictability
- Improves customer experience
- Reduces overall cost due to early defect detection
- Accelerates time to market
Predictive analytics helps in improving efficiency and effectiveness of the QA operations and ultimately helps in better understanding the end user. I strongly recommend to use predictive analytics to deliver beyond the reach of traditional QA practice.
We would love to discuss about the Infosys Predictive Analytics in QA solution and many other such solutions and service offerings with you. Infosys is a Silver sponsor of HPE Discover 2016. Do drop in at Booth #134 for a quick chat. More information on our participation is here.