Effective Usage of Predictions modeling makes you Great! Raja Balusamy, Senior Chief Engineer Samsung R&D Institute India - Bangalore

Abstract Predictive modeling is a latest process that helps test organization or software test team to predict the outcome of future results. Data can be collected from various sources like Google Analytics, Cloud and Previous Reports. There has been a shift in Software testing over the time towards prediction modeling to strengthen user space which helps to improve the business or service quality. Preventive actions and focus can be changed for Software testing based on predictions. This presentation outlines how we can predict the future results from various sources and explain about important parameters.

Why Predictive modeling? Problem Increase costs in SW Testing Production Delays Experience Operational risks Customer Satisfaction on Quality Advantages Helps to making right decisions Helps to improve the productivity Helps to predict the Outcomes

What is Predictive modeling ? Predictive modeling uses statistics to predict outcomes (Data Driven). Current Future Past Review Predictions Reports / Records Predict Results Action

Predictions in Software Testing How to Predict? Data Sources VOC User Data Past Records A. SW Test Predictions B. User Expectations C. Action on VOCs Productivity Predictions in Software Testing Test Plan Defects Most Used Features by Users Most VOCs

A: SW Test Predictions Mobile Testing Defects Trend New Model Total Defects Prediction: 1075 Average Defects Prediction Defects Per New Feature: 90 (25%) Defects Per Major/Minor Enhancement: 25 (10%) Side Effect: 66.6% A B C D E New Model Mobile Models

B. Predictions: User Expectations from Google Analytics Mobile Testing Google Analytics is a web analytics service that tracks and reports website traffic. Most widely used web analytics service on the Internet. We can get details like: No. of Users Mostly used feature Issues. Time Spent, etc.. Important to know the Users pattern to plan better!

Highly important to know the feedback from actual Users! C. Resolving VOCs Mobile Testing VOCs Sources :Google Analytics, Play Store or Market Team Update our test strategy and plan based on Sources. VOCs should be categorized like VOCs which most number of users reported (Pareto Rule) Enhancements or Issues Highly important to know the feedback from actual Users!

Productivity A. SW Test Predictions + Productivity : B. User Expectations C. Action on VOCs Makes to feel great! Help us to improve: Test Effectiveness, Test Satisfaction, Test Productivity and Test Coverage.

Mostly Used in? Limitations History cannot always predict 100% future accurate. The issue of unknown unknowns. Self-defeat of an algorithm.

References & Appendix https://www.clictest.com/blogs/importance-predictive-analytics-software-testing/ https://www.predictiveanalyticstoday.com/predictive-modeling/ https://en.wikipedia.org/wiki/Predictive_modelling

Author Biography Raja Balusamy works as a Senior Chief Engineer at Samsung R&D Institute India, Bangalore and have 13+ years of experience in Software Testing, Test Management and Leadership. He holds a Bachelor's Degree. He is a certified PMP Holder, ISTQB certified, Six-Sigma Green Belt certified and Black belt trained professional. PMP® #1524731

Thank You!!!