Aiatul Al-amin Ador

Software Quality Assurance Engineer

I’m a Computer Science and Engineering graduate from the University of Asia Pacific, driven by a passion for delivering reliable, high-quality software. My specialization in Software Quality Assurance (SQA) and Testing allows me to ensure that every product I work on meets the highest standards of functionality, performance, and user experience.

🧠 Core Expertise

Manual Testing: Requirement Analysis, Test Planning, Test Case Design & Review, Test Execution, Reporting, Root Cause Analysis, Bug Tracking
Automation Testing: Selenium WebDriver, Postman, Newman
API Testing & Architecture: RESTful APIs
Project Management & Agile Tools: Jira, Confluence
Documentation: BRS, SRS
Programming Languages: Java, Python
Web Technologies: HTML, CSS
Databases: MySQL, SQLite
Version Control: Git, GitHub
Other Tools: Word, Excel, PowerPoint, Outlook, Teams, Zoho, Slack

🚀 Career Objective

To grow as a proficient SQA professional who contributes to impactful projects by applying rigorous testing methodologies, fostering team collaboration, and continuously learning emerging technologies. I aim to be a key player in delivering software that users trust.

⚽ Beyond the Code

Outside of work, I’m a passionate football fan and a data analysis enthusiast. I love applying statistical techniques to analyze matches, uncover patterns, and generate insights. This hobby sharpens my critical thinking, attention to detail, and ability to interpret complex datasets—skills that directly enhance my work in software testing.

See My Resume

Institutes and Degrees

⛤ Bachelor of Science in Computer Science and Engineering

↪ University Of Asia Pacific. October 2018 - May 2023

⛤ Higher Secondary School Certificate

↪ Rajbari Govt. College. 2015 - 2017

⛤ Secondary School Certificate

↪ Rajbari Govt. High School. 2010 - 2015

Research

☞ Comprehensive Analysis of Machine Learning and Deep Learning Algorithm’s Performance on Medical Images.

↪ In my study, I focused on using machine learning and deep learning algorithms to improve medical imaging for disease diagnosis. I utilized chest X-ray data from COVID-19 patients to develop a modelthat can identify pneumonia in these patients. After analyzing various algorithms, I found that deeplearning outperformed machine learning, achieving 99% accuracy compared to 93%. This higher accuracyis crucial in healthcare where even small errors can have significant consequences. The findings of mystudy will help enhance the use of deep learning in medical imaging and assist medical professionals with pre-diagnosis.