Dr Chong Chun Yong

Senior Lecturer
Course Coordinator, Bachelor of Software Engineering
School of IT

+603 5515 9704
Room 2-4-25


Personal statement

Dr. Chong Chun Yong is currently working as a senior lecturer at the School of Information Technology, Monash University Malaysia. Prior to that, he worked as a senior lecturer at Tunku Abdul Rahman University College. He obtained his MSc degree in Computer Science from University of Malaya, Malaysia in 2012 and his Ph.D degree in Computer Science from the same university in 2016. His current research interests include software maintenance, software clustering, software remodularization, software engineering education and metamorphic testing.

Academic degrees

  • Doctor of Philosophy in Computer Science (Software Engineering), University of Malaya, Malaysia
  • Master in Computer Science (Software Engineering), University of Malaya, Malaysia
  • Degree in Software Engineering, Coventry University, UK

Professional affiliations

Member of International Professional Bodies

  • IEEE Computer Society, Member
  • Institute for Systems and Technologies of Information, Control and Communication (INSTICC), Member

Member of National Professional Bodies

  • Malaysian Software Testing Board (MSTB), Member

Research Interests

Dr. Chong has a strong interest in all aspect of software engineering, include but not limited to software quality, software maintenance, software remodularization, mining software repositories and metamorphic testing.

Research Projects

Title: A Big Data Analytics Approach for Identifying Reuse-Proneness of Object-Oriented Classes in Source Code Management Systems

Developing classes with high reuse-proneness is difficult and expensive, and hence, often neglected by software developers. The issue with class reuse is that developers would need to spend a significant amount of effort to: i) identify the candidate classes to be reused, ii) isolate the reusable classes and all their dependencies from other classes, and iii) make sure that the candidate classes can be reused in a new operating environment. In order to better manage software development, developers have started to adopt Source Code Management Systems (SCMS) as a medium of collaboration to track, audit, and report software bugs and defects. Vast information stored in SCMSs made it easier for developers to assess the reuse-proneness of classes.Eventually, the assessment of reuse-proneness will give developers who want to reuse a class a better idea of how easy the reuse can be accomplished. In this research, we focus on using a big data mining approach to assess the reuse-proneness of classes in a software system. Data from diverse sources such as change log, change history, bug reports, developers’ activities, and developers’ contributions of a software project are collected and analysed to assess and identify potential reusable classes. The proposed approach is supported by a lightweight parser that reads through and analyse the software artifacts stored in the SCMS. Weighted complex networks are created to illustrate the interactions of all software components from a graph theory point-of-view. Subsequently, software metrics and graph theory metrics correlated to reuse-proneness are applied to reveal reuse-prone software components. Finally, an automated tool will be developed to recommend a list of highly reusable classes to software developers based on the findings. The proposed approach can aid in provide a better understanding of how easy to reuse a particular class, and also assess the quality of software systems.

Title: A Genetic Algorithm-based Software Clustering Approach to Aid in Remodularization of Software Systems

This research focuses on software clustering as one of the solutions to aid in software remodularization. Software clustering can be performed either in supervised or unsupervised approach to pick from a collection of software entities, then form multiple groups of entities such that entities within the same group are similar to each other, while dissimilar from entities in other groups. The typical processes of clustering are as follows. First, common features are chosen to determine similarity between entities. Second, a similarity measure is chosen to determine the similarity strength between two entities. Third, a clustering algorithm is chosen to group similar entities together. Finally, a form of validation is required to measure the quality of clustering results. In this research project, we will investigate new approaches that facilitate in the first and second processes, which is to effectively identify common feature between software entities and propose a novel way to quantify the similarity between multiple entities. The specific tasks are: 1. Study the state-of-the-art approaches in software clustering to aid in remodularization of software systems. 2. Perform experimental evaluation of the proposed approach on real datasets (source code retrieved from open-source software projects).

Title: Robustness Evaluation of Stacked Generative Adversarial Networks using Metamorphic Testing

Synthesising photo-realistic images from natural language is one of the challenging problems in computer vision. Over the past decade, a number of approaches have been proposed, of which the improved Stacked Generative Adversarial Network (StackGAN-v2) has proven capable of generating high resolution images that reflect the details specified in the input text descriptions. In this paper, we aim to assess the robustness and fault-tolerance capability of the StackGAN-v2 model by introducing variations in the training data. However, due to the working principle of Generative Adversarial Network (GAN), it is difficult to predict the output of the model when the training data are modified. Hence, in this work, we adopt Metamorphic Testing technique to evaluate the robustness of the model with a variety of unexpected training dataset. As such, we first implement StackGAN-v2 algorithm and test the pre-trained model provided by the original authors to establish a ground truth for our experiments. We then identify a metamorphic relation, from which test cases are generated. Further, metamorphic relationships were derived successively based on the observations of prior test results. Finally, we synthesise the results from our experiment of all the metamorphic relationships and found that StackGAN-v2 algorithm is susceptible to input images with obtrusive objects, even if it overlaps with the main object minimally, which was not reported by the authors and users of StackGAN-v2 model. The proposed metamorphic relations can be applied to other text-to-image synthesis models to not only verify the robustness but also to help researchers understand and interpret the results made by the machine learning models.


Units taught

  • FIT2107 - Software Quality and Testing
  • FIT3140 - Advanced Programming
  • FIT5122 - Professional practice
  • FIT2101 - Software Engineering Process and Management
  • FIT3077 - Software Architecture and Design
  • FIT3170 - Software Engineering Practices
  • FIT4003 - Software Engineering Research Project

Local grants

  • A Big Data Analytics Approach for Identifying Reuse-Proneness of Object-Oriented Classes in Source Code Management Systems, Principle Investigator, 2019-2021, Ministry of Higher Education, Fundamental Research Grant Scheme (FRGS), RM52,200
  • Sustainable Intelligent Transportation System, Co-Investigator, 2017-2018, Monash University Malaysia, Sunway Sustainability Grant Scheme, RM250,000
  • Transforming Cognitive Frailty to Later Life Self-Sufficiency - AGELESS, Co-Investigator, 2019-2022, Ministry of Higher Education, Long Term Research Grant Scheme (LRGS), RM615,600
  • Enhanced Machine Learning Prediction of Left Ventricular Heart Failure Exacerbation for Patients with Implantable Devices Using Contextual Data via Smartphone App, Co-Investigator, 2020-2022, Monash University Malaysia, RM182,655
  • Addressing Health Communication Needs of Deaf Sign Language Users with DITE, Co-investigator, 2020-2022, Monash University Malaysia, NEED Research Grant Scheme, RM268,700
  • Addressing Disparity in Diabetes Prevention Through Digital Health Supported PRIME Program, Co-investigator, 2020-2022, Monash University Malaysia, NEED Research Grant Scheme, RM428,400
  • An Integrated CNN-LSTM based Model for Predicting Mental Health Risk using Social Media Data, Co-investigator, 2020-2022, Ministry of Higher Education, Fundamental Research Grant Scheme (FRGS), RM73,200

International grants

  • Travel grant to XLAB d.o.o. Principle Investigator, 2018-2019, XLAB d.o.o, Marie Sklodowska-Curie Research Exchange Fellowship (Horizon 2020) project, RM20,270
  • A game theoretic approach to evolving  analysing  mechanism  design in WES,  Co-investigator, Jan 2021 - Dec 2021, Facebook Inc, Facebook Research Award, USD49,200

Current supervision


Bilal Mehboob 
A Big Data Analytics Approach for Assessing and Predicting Reusability and Stability of Software Systems
2018 - Present
Monash University Malaysia

Angelica Lim 
A Big Data Analytics adoption decision-making framework for Malaysian manufacturing Small and Medium Enterprises 
2019 - Present
Monash University Malaysia

Matthew Yeow 
Identifying Reuse-Proneness of Object-Oriented Classes in Source Code Management Systems 
2020 - Present
Monash University Malaysia

Alvin Tan Jian Jia 
Explainable Automated Software Remodularization to Aid in Maintenance of Object- Oriented Softwares 
2020 - Present
Monash University Malaysia

Ahmad Zairi Zaidi (Co-supervision)
Continuous Authentication of Mobile Phone Users Based On Touch Dynamics Biometrics
2017 - Present
Monash University Malaysia

Tay Heng Ee (Co-supervision)
Multimodal Analysis of Social Media Data to Reveal the Footprint of the Behaviours of Users
2019 - Present
Monash University Malaysia

Local Award/Recognition/Exhibition/Stewardship

  • Faculty of Information Technology Teaching Award 2017 – Faculty of Information Technology, Monash University, 2018


Ho, Mun Chon; Lim, Joanne Mun Yee; Soon, Kian Lun; Chong, Chun Yong (2019) An improved pheromone-based vehicle rerouting system to reduce traffic congestion, Applied Soft Computing Journal, Volume: 84, Issue Number: 15684946, 10.1016/j.asoc.2019.105702

Zakari, Abubakar; Lee, Sai Peck; Chong, Chun Yong (2018) Simultaneous Localization of Software Faults Based on Complex Network Theory, IEEE Access, (23990-24002), Volume: 6, 10.1109/ACCESS.2018.2829541

Chong, Chun Yong; Lee, Sai Peck (2017) Automatic clustering constraints derivation from object-oriented software using weighted complex network with graph theory analysis, Journal of Systems and Software, (28-53), Volume: 133, Issue Number: 01641212, 10.1016/j.jss.2017.08.017

Chaudhry, Muhammad Tayyab; Chong, Chun Yong; Ling, T. C.; Rasheed, Saim; Kim, Jongwon (2016) Thermal prediction models for virtualized data center servers by using thermal-profiles, Malaysian Journal of Computer Science, (1-14), Volume: 29, Issue Number: 01279084, 10.22452/mjcs.vol29no1.1

Chong, Chun Yong; Lee, Sai Peck (2015) Analyzing maintainability and reliability of object-oriented software using weighted complex network, Journal of Systems and Software, (28-53), Volume: 110, Issue Number: 01641212, 10.1016/j.jss.2015.08.014

Chong, Chun Yong; Lee, Sai Peck; Ling, Teck Chaw (2014) Prioritizing and fulfilling quality attributes for virtual lab development through application of fuzzy analytic hierarchy process and software development guidelines, Malaysian Journal of Computer Science, (1-19), Volume: 27, Issue Number: 01279084, https://www.scopus.com/record/display.uri?eid=2-s2.0-84896540438&origin=resultslist

Chong, Chun Yong; Lee, Sai Peck; Ling, Teck Chaw (2013) Efficient software clustering technique using an adaptive and preventive dendrogram cutting approach, Information and Software Technology, (1994-2012), Volume: 55, Issue Number: 09505849, 10.1016/j.infsof.2013.07.002

Zainab, A. N.; Chong, C. Y.; Chaw, L. T. (2013) Moving a repository of scholarly content to a cloud, Library Hi Tech, (201-215), Volume: 31, Issue Number: 07378831, 10.1108/07378831311329013


Chong, Chun Yong; Lee, Sai Peck (2019) Can Commit Change History Reveal Potential Fault Prone Classes? A Study on GitHub Repositories, Communications in Computer and Information Science, (266-281), Volume: 1077, Issue Number: 18650929, 10.1007/978-3-030-29157-0_12

Poh, Zhuo; Chong, Chun Yong; Teh, Pei Lee; Joseph, Saramma; Huey, Shaun Lee Wen; Ramakrishnan, Narayanan; Parthiban, Rajendran (2019) What Do Users like About Smart Bottle? Insights for Designers, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), (325-336), Volume: 11577 LNCS, Issue Number: 03029743, 10.1007/978-3-030-22580-3_24


Chong, Chun Yong; Lee, Sai Peck (2019) A commit change-based weighted complex network approach to identify potential fault prone classes, ICSOFT 2018 - Proceedings of the 13th International Conference on Software Technologies, (437-448), https://www.scopus.com/record/display.uri?eid=2-s2.0-85071470061&origin=resultslist

Chong, Chun Yong; Lee, Sai Peck (2015) Constrained agglomerative hierarchical software clustering with hard and soft constraints, ENASE 2015 - Proceedings of the 10th International Conference on Evaluation of Novel Approaches to Software Engineering, (177-188), https://www.scopus.com/record/display.uri?eid=2-s2.0-84933530369&origin=resultslist

Chong, Chun Yong; Lee, Sai Peck; Ling, Teck Chaw (2012) Development of virtual lab system through application of fuzzy analytic hierarchy process, Proceedings - ICIDT 2012, 8th International Conference on Information Science and Digital Content Technology, (207-211), Volume: 1, https://www.scopus.com/record/display.uri?eid=2-s2.0-84866992365&origin=resultslist