How to Enhance Software Testing Efficacy Using Predictive Analytics?

by Niranjan Limbachiya | April 6, 2016 | Software Testing | 0 Comment

As the software products are increasingly becoming complex, software testing has become a mandatory ritual for the software companies to ensure that their software will be able to deliver the expected performance without any bugs or issues. As a result, every software testing services company is working on innovating their services to deliver advanced testing services and faultless software products free from any performance, functionality or user acceptance issues.

Advanced software testing requires extensive use of testing tools and advanced technologies to ensure every bug and issue has been identified. Several companies are even leveraging “Predictive Analytics” for enhancing their service offerings subsuming functional testing, mobile testing, cloud based mobile testing etc.

Predictive Analytics in Software Testing

Predictive Analytics is a data oriented technology, which supports the proactive decisions instead of reactive decisions. It enables the software testers to identify and determine the failure points in the testing activities at the earliest.

Software testing is a challenging process as every software product is different and thus, each software testing services company has to apply the industry best practices for reducing any operational issues in the software being tested. However, newer issues keep on rising and several unexpected problems also crop us during testing, which either lead to cost increase or time requirement for completing testing. But, when predictive testing is applied, the potential problems can be identified at the early stages of testing.

How Predictive Analytics is Used for Software Testing?

Predictive Analytics can be leveraged in different ways to ensure efficacious software testing and early divulgence of bugs and issues. The three cardinal techniques which are used in Predictive Analytics include Predictive Model, Decision Model and Descriptive Model. On the basis of the KPI requisites and clients’ requirements, the most suitable Predictive Analytics model can be used.

Integrated Approach Towards Software Testing with Predictive Analytics

In order to achieve accurate results and fulfil the requirements in software testing, it is vital to use multiple testing tools and perform a multitude of testing activities.When these tools and testing methodologies are combined with predictive analytics, it becomes easier to get the desired results. By integrating the predictive analytics tools into your software testing framework, you can reap several benefits apart from optimizing the time, effort and cost.

The major benefits of integrating the predictive analytics with the testing tools, include:

  • Identifying the most appropriate testing tool for a particular task
  • Significant improvement in quality, planning and delivery
  • Early identification of the issues impacting the different project areas
  • Making the right decisions in the early stages of testing
  • Identify the cause and point of delay
  • Proactive identification of risks and the right ways to mitigate those risks

The aforementioned benefits make it clear that by adopting a predictive analytics based testing approach, you can significantly improve your testing, quality and delivery along with driving better ROI into your testing life cycle. Overall, it improves the effectiveness and efficiency of the testing operations whilst reducing the overall cost.

Leave a Reply

Your email address will not be published. Required fields are marked *