Enterprises are rapidly embracing digital transformation to streamline business operations and leverage new-age technologies. Businesses now need rapid application updates to meet evolving customer demands and stay ahead of the market. To meet the increasing challenges of clients, today’s independent software testing company is also transforming quality assurance methodologies by becoming more data-driven and agile to deliver intelligent software testing services.
In the current market scenario, enterprises are looking to identify and fix bugs at an early stage of the 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 defective data, feedback, and test results more smartly.
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What is Predictive Analytics?
Predictive analytics uses statistical algorithms and machine learning to extract data and determine patterns, and 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 a 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 are also leveraging the benefits of predictive analytics to achieve better results more efficiently.
Enormous amounts of data are generated through all the tasks performed in the 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
By now, we know that predictive analytics can bring more efficiency, agility, and accuracy to the software testing process. However, there are other essential key benefits of predictive analytics in software testing that help in delivering more refined and effective software testing solutions.
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 sources and analyzing them to reach useful business insights. It helps test engineers to earmark areas that need more focus depending on the areas including performance, compatibility, or functional issues. It offers a terrific 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 by past performances. It offers a holistic view of behavior captured from applications in production and the impact on customer sentiments. As a result, QA teams can minimize risks, improve agility and make software testing more customer-centric.
QA teams can factor in the customer feedback and deliver new-age solutions that offer a 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 the testing phase and production environment, analyze the impact on the 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 break down through data point analysis.
As a result, QA teams can optimize the cost of projects in a better manner. High-priority tasks are executed well in advance to ensure overall efficiency is achieved.
Enhanced Efficiency and Customer Experience
It is crucial for enterprises to enhance quality assurance and software testing processes to deliver flawless and robust applications. The process needs to be consistent to support the digital transformation ventures of organizations.
QA teams can work efficiently with tools to monitor log files and generate test scripts that can achieve required solutions. Predictive analytics efficiently assist in the early detection of errors and potential failures. It boosts the prevention of end-minute errors and failures that lead to a 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. In some cases, test engineers can even identify and fix gaps even before the application is completely developed.
With constantly evolving SDLC methodologies, it has become crucial for QA and software testing teams to become more efficient, scalable, and competitive. Incorporating predictive data analytics can help a software testing company become more agile, cost-efficient, and better equipped to take on new-age technological challenges.