Accepting PhD Students

PhD projects

Reducing the Cost of Testing by Employing Improved Test-Case Prioritization Methods Providing an Improved Software Fault Prediction Model to Decrease Testing Effort Performing Software Test Oracle based on Improved Machine Learning Techniques

20132020

Research activity per year

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Personal profile

Biography

Golnoush Abaei has joined the School of Information Technology, Monash University Malaysia in September 2020 as an Academic Consultant. She got her Bachelor in Computer Engineering (Software Engineering major) from the Islamic Azad University Central Tehran Branch, Iran in 2002. She worked as a system analyst and developer in software companies from 2001 to 2005. She pursued her Master in Computer Application in Mysore University, India from 2005-2008. In 2009 she started working as a faculty member in one of the best Private Universities in Iran, Shahabdanesh University. From 2011 to 2015 she did her Ph.D in Computer Science in University Technology Malaysia. She became an assistant professor and research manager in Shahabdanesh University in 2015 and 2017, respectively. She was a Scientific Secretary in the 24th and 25th round of ICBME which is the most important Biomedical Engineering Conference in Iran. She is very much interested in solution innovation and designing models that dealing with real-world applications. Her research areas mainly focuses on Software Testing (Software Fault Prediction, Test Case Prioritization, and Test AI Products), Correction of INS/GPS Navigation System, and Intrusion Detection.

Research interests

Software Testing

  • Software Fault Prediction
  • Test Case Prioritization
  • Selection Test AI Products

Software Development

  • Effort Estimation
  • Cost Reduction

Correction of INS/GPS Navigation System

Recognition System

Intrusion Detection

Monash teaching commitment

1. Unit code: FIT3152, Data analytics 

2. Unit code: FIT9132, Introduction to databases

3. Unit code: FIT5133, Enterprise architecture and management

4. Unit code: FIT5160, Business process modelling, design and simulation

Research interests

Software testing is a crucial and expensive task during the software development process which guarantees software quality. There are several methods which are used when a software company needs to deliver a finished product while it has limited time and budget for testing it. One of the methods is fault prediction which identifies parts of the system that are more defect prone; however, when the class labels are not identified or the company does not have similar or earlier versions of the software project, traditional classification methods cannot be used. In order to find defected modules in software with high accuracy and improve prediction generalization's ability, we have been focusing on developing an automated software fault prediction model using semi-supervised hybrid methods. Another method to improve the testing process is test case prioritization which schedules the test case's execution. Both mentioned methods can improve the effectiveness of software testing processes.

Research area keywords

  • Software Quality
  • Machine Learning
  • Artificial Intelligence
  • Expert Systems
  • Software Testing

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