How Predictive Analytics is Changing Software Testing

Software development has changed significantly in the last five years. The rising demand for speed and reliability from consumers sparked many of these changes. As technology companies continue to set the bar higher and higher for applications, consumers opt for only the best products. Moreover, as the market becomes more saturated with high-performing options, simply offering advanced software is no longer a reliable strategy.

As business leaders continue to seek a new edge, many are turning to big data. Vast scores of data are being collected, analyzed, and applied to various processes throughout the organization to improve products and operational efficiency.

How is big data analytics helping companies to address the speed and reliability demands of the market?

How about testing? QA testing is a long-standing principle in software, but traditional methods aren't producing great results fast enough anymore. The concept of testing remains the same: find and fix mistakes before launching a product to consumers. However, big data is altering when and how development teams conduct testing.

The Challenges with Traditional Testing Methods

Traditional testing relies heavily on the technical and business requirements of the project as a baseline to assess against the code. Is the product meeting the specifications outlined prior to the project beginning? This approach is very inward focused as opposed to user focused. It's a "let's produce a good product, launch it to the market, and hope our assumptions were correct" approach.

Traditional testing is also a one-way street. It focuses on getting the product to launch and lacks the means to adjust the software product based on feedback from users. Modern users expect bug fixes, updates, and new versions on a consistent and timely basis (which can be sometimes mean minutes). Traditional testing doesn't accommodate this need for speed, which can lead to users moving to another product altogether to satisfy their needs quickly.

Consumers are fickle. Why would they wait around for your developers to fix an issue when they can just as quickly find another product that immediately meets their needs?

If not speed then traditional testing must prioritize higher quality outcomes right? Not necessarily. Linear direction strikes again. Traditional testing lack feedback loops, which makes it challenging to know precisely what your users need and what changes to make. If your specifications aren't spot on, you won't produce software that is reliable from your users' perspective.

It's important to note that these challenges didn't exist before the age of big data because we had no advanced means of predicting what users wanted. When big data did not exist, traditional testing methods were considered best practices.

Now, with tons of data at our fingertips, traditional methods have become outdated and are being replaced by a more proactive approach to testing. For example, companies are instituting those feedback loops to collect user usage data. They're also collecting enormous streams of data from every step of the testing process for analysis.

The most effective companies leverage advanced analytics to streamline their data into actionable changes to their testing methods. Some of the changes are inward focused, adjusting processes and systems to make testing more efficient and reliable. Some of these changes are outward facing, adjusting how development teams adapt their products and features based on telling insights from their collected data.

Arguably one of the most impactful changes to testing is the use of predictive analytics to anticipate users needs and make adjustments to products based on those predictions.

What is Predictive Analytics?

Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. The practices aim to generate future insights with a significant degree of precision.

How is Predictive Analytics Improving Software Testing?

Customer-Centric Testing

Predictive analytics allows development teams to shift to a customer-centric testing process rather than purely requirements-focused testing. To an extent, predictive analytics can be used to predict users reactions to an event based on the pattern that they have followed previously. This helps teams gauge user sentiment before launching the product or feature.

In turn, predictive analytics helps make products more reliable to users in a shorter amount of time. You could put a product out on the market and use feedback loops to make adjustments to users reactions. Predictive analytics takes it one step further, enabling you to make many of those changes prior to releasing the product or feature.

Streamline Testing Activities

Predictive analytics, along with other types of analysis, can help teams prioritize and streamline testing activities. By using predictive analytics to understand users needs, organizations can build the testing process around those needs, instead of allocating valuable time and resources to activities that will not make a significant impact on the product outcome.

Other types of analysis are also crucial for streamlining efficiencies and can be used in tandem with predictive analytics. For example, root-cause analysis helps identify the cause of defects and produce a clear understanding of historical data. Predictive analytics can then use that data to predict possible problem spots in the product, directing testers towards those high-priority activities.

Competition in the software industry is getting tighter, and consumer expectations are getting bigger. High-performance products are no longer relying solely on advanced software to gain a competitive advantage. The focus has shifted to superior speed and reliability generated by better internal processes like testing. Big data has opened the doors for new methods like predictive analytics to change the way development teams create, test, and deploy high-performing products that meet user demands.

Don't get left behind by your users. Leverage predictive analytics to give your advanced software product the edge it needs.