Guru Swarupa Rani Peetla

A passionate and detail oriented software professional with 7+ years of experience in Python driven automation and project leadership. Proven ability to streamline workflows, implement intelligent automation solutions, and coordinate cross functional teams to achieve operational excellence. Adept at translating complex requirements into scalable systems that deliver measurable impact. Seeking to contribute to a forward-thinking organization where I can apply my technical acumen and evolve into a strategic role driving innovation, efficiency and continuous improvement.

Project Title: Automation of Unit Test Generation for C and Java Codebases 

Duration: 1.5years | Team Size: 4 members Tools & Technologies: Python, NLP, Regular Expressions, VectorCAST, Eclipse, IntelliJ IDEA, SonarQube

This project focused on automating unit test generation for both C and Java source code to enhance validation efficiency and improve test coverage.

For C code, the automation pipeline extracted source files and analyzed functions and dependencies. Unit test cases were generated in Polarian format, specifying expected inputs, outputs, and validation criteria. These were then converted into .tst scripts compatible with VectorCAST, which executed the tests to validate code behavior automatically.

For Java code, the system cloned source files from the client repository and generated JUnit test scripts compatible with Eclipse and IntelliJ IDEA. The scripts were executed to validate functionality, and SonarQube was used to analyze code coverage and identify untested sections.

Roles & Responsibilities:

• Developed Python scripts to automate source code extraction and unit test generation for both C and Java codebases.
• Defined and validated test cases in Polarian format and ensured accurate mapping to .tst scripts for VectorCAST execution.
• Automated JUnit test generation and ensured compatibility with Eclipse and IntelliJ IDEA.
• Modified source code based on test results and improved test coverage using SonarQube analysis.
• Designed scalable and reusable automation workflows to streamline testing across diverse environments.

Project Title: AutoScript Generation

Duration: 11 months | Team Size: 6 members Tools & Technologies: Python, HTML/CSS/JavaScript, Selenium, Postman, Git, NLP.

AutoScript Generation is a cross platform automation tool designed to streamline the testing lifecycle by converting natural language test requirements entered via text into executable scripts. It supports multiple programming languages and environments, significantly reducing manual effort and accelerating test coverage.

• Converts structured test cases into executable scripts in Python, Robot Framework, and JavaScript.
• Automatically identifies UI elements and stores their XPath references for efficient script execution.
• Supports Windows, Linux, and macOS environments.
• Adapts dynamically to UI changes with minimal manual updates, reducing testing effort by 60%.
• Accepts test case inputs in English and over 40 additional languages using NLP.
• Offers a user friendly interface for intuitive script generation and management.

Roles & Responsibilities:

• Designed and developed the AutoScript for seamless test case input and script generation.
• Analyzed testing requirements and built a robust Python based automation framework for web application testing.
• Architected and implemented the script generation engine from scratch, leveraging Selenium for dynamic web automation.
• Integrated NLP capabilities to support multilingual test case entry.
• Ensured cross platform compatibility and optimized the tool for scalability and maintainability.

• 🏆 Recognized as Star of the Month for leading initiatives in unit test automation.
• 🎖️ Achieved Spot Awards for proactive contributions and sustained excellence in project execution.