17 August 2022
In this quickly moving lifestyle where every task has a tight deadline, automation has been widely accepted for performing tasks without the need for manual intervention. Coding languages like Java and Python are acing the game along with other languages in tasks such as the generation of reports, analysis of data, management of files, and many other duties that are repeated time and again.
Ranjith Gopalan, a seasoned IT Architect with several years of experience, has led a series of impactful automation initiatives that have enhanced operational efficiency at his organization. Utilizing his expertise in Python, Java, and various automation frameworks, he has developed a suite of innovative tools that are transforming the way teams work.
The development of a Python Playwright framework with machine learning (ML) integration is significant as it streamlines UI and API automation workflows, utilizing AI capabilities to predict outcomes and enhance overall effectiveness.
The framework addresses the challenges of slow development times and unreliable object identification, leading to a significant improvement in first-time pass rates. Integration with regression models allows for more precise test data generation, aiding in the development and tuning of machine learning models.
Moreover, Gopalan has also designed a comprehensive dashboard, particularly for data scientists. This user-friendly platform integrates data pre-processing, feature engineering, and hyperparameter tuning for ML and deep learning model creation. It incorporates “Creative AI” with Large Language Models (LLM) to streamline tasks and generate information. This one-stop shop empowers data scientists to work efficiently without switching between different tools or relying heavily on coding expertise.
Understanding the time-consuming nature of tasks like screenshot capture and test result upload, he developed a Python utility that automates these processes. It allows for quick and efficient screenshot capture, comment addition, and bulk upload into test management systems. A web-based application provides side-by-side visual comparisons of PDFs and eliminates the need for manual validation. These tools have increased productivity by up to 45% across various departments.
Utilizing CA DevTest Suite (now Broadcom’s DevTest Solutions), he also helped establish a dedicated test lab. This lab facilitates continuous testing and service virtualization, accelerating the software development lifecycle (SDLC).
He contributed significantly to the development of a custom Java utility for service virtualization. This product records live service transactions that create virtual images of service and database layers ultimately leading to the development of realistic prototypes for client demonstrations.
Through his publications, “Integration of Machine Learning Models with Test Automation Frameworks,” “Mastering Python Test Automation,” and “Mastering Service Virtualization: A Guide for SDLC Professionals,” featured in the International Journal of Computer Science and Engineering Research and Development (IJCSERD) and online platforms, he has made significant contributions to advancing the field of automation.
There have been significant impacts of these initiatives. “The Python Playwright framework has helped secure over $2 million in projects, and the comprehensive dashboard for AIML has facilitated annual savings of $86,000 across three accounts. The automation utilities for screenshots and test uploads have resulted in annual savings of $75,000 across five insurance accounts” he stated. The service virtualization project generated over $3 million in revenue and led to the hiring of over 90 new developers and testers.
Industry experts’ vision for the future includes integrating existing tools with reporting systems like Allure and Cucumber, incorporating deep learning models, and creating a centralized dashboard for failure analysis within the Python Playwright framework.
They believe that service virtualization, coupled with machine learning, will be crucial for enhancing software quality and reducing overall development costs. Similarly, the comprehensive dashboard for AIML is envisioned to enhance its functionalities with advanced visualization tools and further integration with AI models.