Future of Software Testing With Predictive Analytics

• By Kanika Vatsyayan
• 04-08-2021
• Software

Nowadays, almost all businesses are embracing digital transformation at a quick pace and in order to streamline their operations as well as take advantage of cutting-edge technologies.

Similarly, due to the era of digitalization or changing times, QA (Quality Assurance) has become more data-driven, and experts are transforming their methodologies in the urge of delivering intelligent software testing services.

On the other hand, due to the race of becoming highly competitive, enterprises are striving to discover and resolve issues or bugs at an early stage of SDLC (Software Development Life Cycle) since it allows them to speed up quality-to-market and even given them cost-saving benefits.

Moreover, to satisfy the growing needs and business expectations, enterprises today adopt shift left testing and find bugs even if the app is ready for quality assurance. This is because the desire of QA test engineers is to make critical business decisions faster and which is only possible when they consider shift left testing because it allows them to leverage defect data, feedback, and test results and make it possible for them to fulfill the latest market needs.

In addition, when it comes to simplifying software testing processes, speed has become the new normal for software testing companies, and that’s why they today think about predictive analysis and automation to help you automate software testing without compromising your tech stack.

Also, due to the availability of many excellent tools in the market, they choose software that is appropriate for testing and work wonders with your modern technology stack. Now, the point is what Predictive Analysis is all about and how one can leverage it to groom the future of software testing.

To help you know about this concept, below, we’ve noted down some important information about Predictive Analysis and even explain how it can help fill the gap in Software Testing. So, you can scroll down and enjoy our blog from start to end.

What is Predictive Analysis?

Predictive Analytics is a data-driven technology that can be utilized to predict test failures and know the future. Additionally, it has superpowers to optimize your project data and support business leaders in making fast strategic decisions. Similarly, it aims to forecast future outcomes based on user’s needs, current conditions, and future performance of a product or service, or you can understand it helps generate future knowledge with high precision.

Besides, statistical algorithms and machine learning are used by Predictive Analytics to extract data, determine trends, patterns, and predict future results. Into the bargain, it leverages a data-driven method to assume weak points in testing activities and, based on that, help you know results that can happen in the future.

One of the main benefits of Predictive Analysis is that it lets you obtain actional insights and support QA teams to make proactive decisions to optimize project data and help you gain desired results. In other words, it uses statistical algorithms to help you find trends in data and provides recommendations about how data will behave in the future.
Thus, you may now understand that Predictive Analytics is a type of prediction of future events or analytics-based predictions, and predicting unknown events as per the analysis of past data is somewhat known as Predictive Analytics.

Usually, it uses numerous algorithms for data processing, but as an example, you can check some relevant examples of these algorithms below:

Machine Learning
Regression Algorithms
Time Series Analysis

Importance of Predictive Analytics in QA

Software testing becomes successful when it involves a perfect combination of various processes, tools, and methodologies. Like several other industries, software testing and Quality Assurance are also preferred to attain the maximum benefits of Predictive Analytics and accomplish more efficient and better results.

On the other side, a huge amount of data is produced through all the tasks or activities which are performed in STLC (software testing life cycle).

Rather than letting test data sit idle, test engineers can use it to streamline software testing with analytics solutions and cutting-edge tools. On the other hand, predictive analytics is a continuous activity that necessitates ongoing analysis and reference to actionable insights to make continuous improvements in QA practices for better results.

Therefore, if you want to improve your QA practice, you can give importance to Predictive Analytics and consider it to reshape the future of software testing.

What Are Some Reasons to Choose Predictive Analytics for QA (Quality Assurance)?

Below are some reasons that will help you know why you should consider predictive analytics for your QA.

Customer-Oriented QA

With Predictive Analytics, test engineers can analyze your customers' emotions or sentiments and better understand the market scenario to test apps properly. To boot, with this practice, you can QA more consumer-centric and allow your teams to fix critical areas efficiently, including functional issues, performance, compatibility, and security bottlenecks.

Besides, QA teams can collect and analyze customer’s attitudes and their experiences. Make sure the customer data is collected from possible sources and in order to get meaningful records of the business. During this process, test engineers can readily identify areas that need more attention and perform different types of testing, such as performance testing, functional testing, or compatibility testing. Therefore, it helps you prepare an effective QA strategy in order to offer a positive user experience to your customers so that they can become highly satisfied.

Practically, it aids teams in embracing consumer feedback and delivering cutting-edge solutions for a better customer experience. Nothing is more vital than incorporating client input into your quality assurance processes. This will eventually help your business achieve digital transformation goals and objectives, both now and in the future.

Facilitates Insights to Prioritize Testing Activities

Information collected from the software development and testing process is vast and has to be efficiently stored for further improvisation.

After gathering the information from the development and testing process, one should first store it safely and then analyze it with the right tools. This data can help you find defect logs, test cases, test outcomes, production incidents, application log files, project documentation, and many more things that are important for QA.

Thereafter, one can apply Predictive Analytics on this data for several tasks such as discovering defects during testing and while in the production environment. Also, it lets you examine and evaluate the impact on customer experience. Moreover, you can find a pattern of issues, align test scenarios, and conduct many more activities with Predictive Analytics.

Further, QA teams can consider this data to obtain maximum test coverage and optimize their testing activities. Even the root cause analysis of defect data can help you identify weak spots and forecast some hotspots inside an application that require more attention. In addition, when using analyzed data points, one can better optimize the workflow of the application development process and identify all those areas where your application can break down.

Advantages of Predictive Analysis in Software Testing
Increased customer satisfaction
Effective & efficient testing
Early defect detection
Enhanced time-to-market
Better release control
Reduced cost

Fill the Gap with Predictive Analytics in Software Testing

Thanks to Big Data, AI (Artificial Intelligence), and Machine Learning, because these technologies make it possible for testing experts to know about missing features that are exposed by predictive analytics during testing.

With the aid of Predictive Analytics, one can easily predict likely future trends, patterns, and even user behavior because it incorporates several techniques from statistical modelling, data mining, and the very popular technologies like AI and ML to analyze both historical as well as current data.

Also, with the use of Predictive Analytics, QA teams can take decisive actions to eliminate potential threats and even make it feasible for customers to get 100% product satisfaction.

Not only this, but also it enables software testing team to be more cost-efficient, agile-oriented and gives them the freedom to tackle new-age technological challenges. Therefore, in this way, in the digital era, Predictive Analysis or Analytics is leveraging to improve the future of software testing and gain as many business benefits with positive results and decisions.