Enterprises are rapidly embracing digital transformation to streamline business operations and leverage new-age technologies. Amidst changing times, quality assurance is also transforming its methodologies and becoming more data-driven to deliver intelligent software testing services.
In the current market scenario, enterprises are looking to identify and fix bugs at an early stage of software development life cycle (SDLC) to expedite quality-to-market while staying cost-efficient. To meet such rising expectations, it has become crucial for enterprises to adopt shift left testing and identify failures even before the apps are ready for quality assurance. QA test engineers are expected to make critical business decisions at a rapid pace and that can be done only by leveraging defect data, feedback and test results.
What Is Predictive Analytics?
Predictive analytics uses statistical algorithms and machine learning to extract data and determine patterns, trends and predict future outcomes. The data-driven method can be leveraged to predict weak points in testing activities and determine future outcomes. With the help of actionable insights, QA teams can make proactive decisions to optimize project data and achieve desired results. By leveraging statistical algorithms, predictive analytics helps in identifying patterns in data and generates the forecast on how data would behave in the future.
Predictive analytics makes use of multiple algorithms for data processing. Here are a few relevant examples:
- Machine Learning
- Regression Algorithms
- Time Series Analysis
Why Predictive Analytics In QA?
Successful software testing involves a perfect combination of a lot of processes, methodologies and tools. Like several other verticals, quality assurance and software testing is also leveraging the benefits of predictive analytics to achieve better results more efficiently.
Enormous amount of data is generated through all the tasks performed in software testing life cycle (STLC). Instead of letting the test data lie idle, test engineers can leverage it through analytics solutions and state-of-the-art tools to streamline software testing. However, predictive analytics is an ongoing activity that requires continuous analysis and reference to actionable insights to make continual adjustments in QA practices for better results.
Key Reasons to Consider Predictive Analytics for QA
Predictive analytics helps test engineers analyze consumer sentiments and understand the overall market scenario to test applications in the right context. This helps in making QA more consumer-centric and enables quality assurance teams to fix focus areas efficiently including performance, compatibility, security or functional issues.
QA teams are collecting customer sentiments and experiences from all possible sources and analyzing them to reach useful business insights. It is helping test engineers to earmark areas that need more focus depending on the areas including performance, compatibility or functional issues. It offers a great way to chalk out a QA strategy centered around great user experience and customer satisfaction.
Data captured from social media channels helps in gaining insights into customers’ sentiments. QA teams should make social analytics as one of the essential inputs to formulate an effective quality assurance strategy. The data captured from social media enables test engineers to identify negative sentiments caused from past performances. It offers a holistic view of behaviour captured from applications in production and the impact caused on customer sentiments. As a result, QA teams can minimize risks, improve agility and make software testing more customer centric.
QA teams are able to factor in the customer feedback and deliver new-age solutions that offer better experience. Enterprises can leverage this technique to meet their digital transformation goals with customer satisfaction as the focal point of the entire process.
Leverage Insights To Prioritize QA Activities
Software development and QA generate tons of data during the entire process. It is crucial for enterprises to gather the data efficiently and leverage it for further optimization. Predictive analytics tools can swiftly analyze test cases, defect logs, test results, application log files, production incidents and project documentation among others.
Test engineers can churn out actionable insights to examine defects in testing phase and production environment, analyze the impact on end-user, determine patterns and align test scenarios among others. Software testing teams can even use predictive analytics to increase test coverage and optimize test environments.
Defect data’s root cause analysis can help QA teams predict hotspots and determine weak spots in the application that needs to be fixed. It assists in workflow optimization of the software development lifecycle (SDLC) and determines points where the app might breakdown through data point analysis.
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Enhanced Efficiency and Customer Experience
It is crucial for enterprises to enhance quality assurance and software testing process to deliver flawless and robust applications. The process needs to be consistent to support digital transformation ventures of organizations.
QA teams can work efficiently with tools to monitor log file and generate test scripts that are capable to achieve required solutions. Predictive analytics efficiently assist in early detection of errors and potential failures. It boosts the prevention of end-minute errors and failures that lead to reduction in time-to-market and development costs.
Through predictive analytics, QA teams can take preventive actions to cut down potential threats and ensure greater customer satisfaction.
With constantly evolving SDLC methodologies, it has become crucial for QA and software testing teams to become more efficient and competitive. Incorporating predictive data analytics can help software testing teams become more agile, cost-efficient and more equipped to take new-age technological challenges.