Shanmugapriya Selvakumar
Bengaluru,KA
Summary
QA Automation Engineer with 2+ years of experience in test automation, framework development, and end-toend testing using Robot Framework and Selenium WebDriver. Hands-on experience in Telecom and Network Security testing, with expertise in API testing, CI/CD Integration, defect lifecycle management, and Agile methodologies. Skilled in leveraging AI tools to optimize test coverage and improve test efficiency.
Overview
2
2
years of professional experience
1
1
Certification
Work History
Software Engineer
Alcatel-Lucent Enterprise - R&D
Chennai
07.2022 - 08.2024
- Engineered a robust Robot Framework automation suite using Python, covering HR management workflows including employee profile creation, updates, and personnel data management, significantly improving test efficiency and reliability.
Developed and automated 120+ functional and regression test cases using Robot Framework, reducing manual testing effort by ~60% and accelerating Agile release cycles.
Automated provisioning and configuration of user accounts and telephone assignments on Alcatel dual node systems, including SIP trunk validation, ensuring seamless inter-node communication and system reliability.
Integrated Robot Framework test suites Jenkins CI/CD pipelines, enabling continuous testing, automated execution, and streamlined defect detection.
Implemented secondary Selenium automation framework with Page Object Model (POM) and BDD (Cucumber, Gherkin), ensuring maintainability, readability, and reusability of web application test scripts.
Leveraged Generative AI tools to auto-generate Robot Framework test cases, optimize Python test scripts, enhance coverage, and accelerate defect triage in Agile environments.
Executed API testing using Postman and SoapUI, validating request and response payloads, headers, and HTTP status codes for REST and SOAP services.
Conducted advanced web application and network security testing, including protocol validation, port scanning (Nmap), and vulnerability assessments using Burp Suite Pro, Nessus, and Qualys.
Generated detailed automation execution reports with TestNG and Robot Framework reporting libraries, ensuring transparency and traceability of test results.
Logged, tracked, and validated defects using JIRA and HP ALM, collaborating with developers, product owners, and stakeholders throughout the defect lifecycle.
Created and maintained comprehensive test cases and scenarios in TestRail, ensuring alignment with functional, technical, and business requirements.
Education
M.E - Communication System
SSN Institutions
07.2022
B.E - Electronics & Communication
Kongu Engineering College
08.2020
Skills
- Test Automation: Robot Framework, Selenium WebDriver, TestNG, Cucumber (BDD)
- Integrated Development Environment: PyCharm, Eclipse, Google Colab, Jupyter Notebook
- Security & Networking Tools: Burp Suite Pro, Nessus, Nmap, Qualys, Metasploit, Wireshark
- Test Management: JIRA, TestRail, HP ALM, YouTrack, Microsoft OneDoc
- Programming Languages: Python, Java, C
- DevOps & Version Ctrl: Git, Jenkins
- API Testing: Postman, SoapUI
- AI & Tools: Generative AI, Witai
- Operating Systems: Linux, Windows
Certification
- Alcatel Certified Field Expert - ACFE
- Alcatel Certified System Expert - ACSE
- ISTQB Certification - Foundation Level (In Progress)
Journal Publication
Research Trends in Dermatologist Level Automatic Classification of Various Skin Lesions Using Deep Learning, Indian Journal of Public Health Research and Development, 11, 2, 2020, 10.37506/v11/i2/2020/ijphrd/194882
The main objective of this work is to review the indexed papers that addresses the issues of automatic classification of skin lesions. This paper sumarizes about the different types of datasets available, the type of deep learning models used for training and the parameters used for performance measurements.
Keywords: Skin lesion classification, Deep Learning, Data Augmentation, Artifacts, Transfer Learning.
Project
Designed an image encryption algorithm that integrates chaotic logistic map–based permutation (confusion) with cellular automata–based diffusion to enhance security. Encryption keys are generated using a logistic map–based round key generation mechanism. The process involves truncating the four least significant bits (LSBs) of pixel values, applying chaotic permutation, and recombining the bits to produce a securely encrypted image. Better performances of the encryption algorithm found in analyses such as entropy, histogram depict algorithm's strength. The analysis proved that the algorithm is not vulnerable to third-party attacks.
IDE: Jupyter Notebook, Google Colaboratory, Matlab,
Timeline
Software Engineer
Alcatel-Lucent Enterprise - R&D
07.2022 - 08.2024
M.E - Communication System
SSN Institutions
B.E - Electronics & Communication
Kongu Engineering College