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Why Is Predictive Analytics Imperative for Software Testing?

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Why Is Predictive Analytics Imperative for Software Testing?

Traditional software QA is shifting gears and taking on new responsibilities. There's an increasing need for teams to take an analytics-based approach to next-gen QA.

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Predictive analytics as a concept has been widely applied across industries and businesses to derive the required inferences and take informed business decisions. Traditional software quality assurance (QA) is shifting gears and taking on new responsibilities. Hence, there is an increasing need for teams to take an analytics-based approach towards next-generation QA. Organizations need to achieve both quality and speed, which intensifies the pressure on development teams to foresee the kind of challenges and failures that might come up.

One of the greatest highlights of implementing analytics in QA is its capability to predict the future failures in view of the past data sources. Predictive analytics helps extract project or business-critical information from data sets by implementing statistical algorithms and machine learning. This helps generate patterns and estimate future trends that are useful for identifying failure points. This kind of forecast and data is very much required in QA for making proactive decisions.

How Can Predictive Analytics Reduce Time-to-Market?

Predictive analytics implements multiple algorithms to process the data, namely, regression algorithms, time series analysis, and machine learning. Quality assurance and testing has been a complex activity and involves many dependent variables. It needs to be efficiently managed to deliver the expected results. Analytics can be effectively leveraged to streamline and smoothly perform software testing activities.

Moreover, it is not a one-time activity, as it has to be continuously conducted to analyze the data that is constantly generated during the software development process. When the stored data is analyzed with analytic solutions and tools, it will continue to add business value towards the end of the development process. The process needs a good amount of data churned from software development cycle to deliver these results efficiently.

Digital transformation is changing the business dynamics, where quality assurance plays a major role to deliver strong solutions for dealing with the customer base. For competing organizations, there is very less scope for error. Analytics can help support teams in the testing process to not only bring down the testing costs but also cut down the testing efforts. Ultimately, help businesses to reach faster to the market and cut the chase.

Key Reasons to Consider Predictive Analytics for Software Testing

The need to reach faster to the market and stay accurate as much as possible are two of the most critical reasons for considering predictive analytics in QA. Let's evaluate some key reasons to adopt analytics in the QA and testing space.

Build Customer-Centric QA

It is important to understand the overall market scenario and consumer sentiment to develop the right applications for the consumers. Analytics applied in QA helps gauge the consumer sentiment on product and applications. This makes QA much more consumer-centric and helps teams to address focus areas such as compatibility issues, performance issues, functional issues, or security issues with the application.

Practically, it helps teams embrace customer feedback and deliver contemporary solutions for a better experience. There is nothing more important than taking customer feedback and imbibing it in your QA activities. This will ultimately help enterprises to meet their digital transformation objectives now and even in the future.

Facilitates Insights for Prioritizing Testing Activities

Information gathered from the software development and testing process is massive and has to be effectively stored so that it can be used for further improvisation. After all the information is gathered from the development and testing process, it has to be stored and then analyzed with appropriate tools. This data can comprise defect logs, test cases, test results, production incident, application log files, project documentation, and much more that concerns QA.

Predictive analytics can be applied on this data for various tasks such as examining defects in test and production environments, evaluating the impact on customer experience, identifying patterns of issues, aligning test scenarios, and much more. Teams can even use this data to achieve higher test coverage and optimize testing activity. Moreover, root cause analysis of defect data can help identify weak spots and predict hotspots within an application that needs attention. It helps optimize the workflow of the application development process and identify where the application might break down with the help of analyzed data points.

Boost Testing Efficiency and Enhance Customer Experience

We have already spoken about encouraging and building customer-centric QA with predictive analytics. QA teams work with tools, monitor application log files, and generate test scripts to arrive at relevant solutions. In a way, it helps in early detection of potential failures and defects. The idea of the shift-left approach in testing is to enable early detection of errors and reduce potential defects in the future. Predictive analytics can boost this process and enable QA and testing teams. It will help teams to take preventive action and bring down potential threats or dissatisfaction amongst consumers of the application.

It is important to boost testing efficiency to deliver robust applications that are compatible and secure for customers. This has to be a consistent process to support the digital transformation activities and deliver desired customer experience. Applying predictive analytics tools within QA helps to achieve these objectives on a consistent basis.

In Conclusion

According to a report released by Gartner in 2017:

"Global revenue in the business intelligence (BI) and analytics software market is forecast to reach $18.3 billion in 2017, an increase of 7.3 percent from 2016, according to the latest forecast from Gartner, Inc. By the end of 2020, the market is forecast to grow to $22.8 billion."

Why are global enterprises across various business domains considering predictive analytics? Businesses need to get more foolproof by making informed decisions and enabling their teams with proven data points to consider. Similarly, even QA needs these data points from its own testing and development repository to make informed decisions for building frameworks to test applications.

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Topics:
big data ,predictive analytics ,software testing ,data analytics ,business intelligence ,time to market

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