Dr Chong Chun Yong

Lecturer
School of IT

chong.chunyong@monash.edu
+603 5515 9704
Room 2-4-21
ORCID

Personal statement

Dr. Chong Chun Yong is currently working as a 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, and graph theory.

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, and mining software repositories. He is also interested in IoT, drone-to-drone communication, and cloud computing research.

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).

Education

Units taught

  • FIT2107 - Software Quality and Testing
  • FIT3140 - Advanced Programming
  • FIT5122 - Professional practice

Local grants

  • Reliable Communication Architecture for Navigation and Guidance of Interconnected Aerial Vehicles, Chong Chun Yong, 2018-2021,  Collaborative Research in Engineering, Science and Technology (CREST) Grant Scheme,  RM401,625.50
  • A Big Data Analytics Approach for Identifying Reuse-Proneness of Object-Oriented Classes in Source Code Management SystemsChong Chun Yong, 2018-2020, Ministry of Higher Education, Fundamental Research Grant Scheme (FRGS)
  • Sustainable Intelligent Transportation EcosystemChong Chun Yong, 2017-2019,  Monash University Malaysia Sustainable Community Grant Scheme, RM250,000
  • An Approach for Recovering Unreported Software Bugs in Source Code Management Systems, Monash University Malaysia, Chong Chun Yong, Feb 2017 - Jan 2018,  School of IT Collaborative Research Seed Grant, RM10,000

International grants

  • Travel grant for secondment at XLAB d.o.oChong Chun Yong, Feb 2017 - Jan 2018,,  XLAB d.o.o.,RM10,900

Current supervision

PhD

Ahmad Zairi Zaidi (Co-supervision)
Keystroke Dynamics for Continuous User Authentication with Soft Biometrics
Oct 2017 - Present
Monash University Malaysia

Bilal Mehboob (Co-supervision)
A Big Data Analytics Approach for Estimating Reuse Proneness of Object-Oriented Classes in Source Code Management Systems
Nov 2018 - Present
Monash University Malaysia

Chin Ming Jun (Co-supervision) 
Reliable Communication Architecture for Navigation and Guidance of Interconnected Aerial Vehicles 
Dec 2018 - Present
Monash University Malaysia

Local Award/Recognition/Exhibition/Stewardship

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

Publications

Conference Proceedings

  • R. S. Bashir, S. P. Lee, C. Y. Chong, K. A. Alam and R. W. Ahmad, "A Methodology for Impact Evaluation of Refactoring on External Quality Attributes of a Software Design," 2017 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 2017, pp. 183-188.
  • Chong, C. Y., Lee, S. P., & Ling, T. C. (2012, June). Development of virtual lab system through application of fuzzy analytic hierarchy process. In Information Science and Digital Content Technology (ICIDT), 2012 8th International Conference on (Vol. 1, pp. 207-211). IEEE.
  • Chong, C. Y., & Lee, S. P. (2015, April). Constrained agglomerative hierarchical software clustering with hard and soft constraints. In Evaluation of Novel Approaches to Software Engineering (ENASE), 2015 International Conference on (pp. 177-188). IEEE.

Journals

  • Chong, C. Y., & Lee, S. P. (2017). Automatic clustering constraints derivation from object-oriented software using weighted complex network with graph theory analysis. Journal of Systems and Software133, 28-53.
  • Chong, C. Y., Lee, S. P., & Ling, T. C. (2013). Efficient Software Clustering Technique using an Adaptive and Preventive Dendrogram Cutting Approach. Information and Software Technology. Volume 55, Issue 11. November 2013. Pages 1994-2012, ISSN 0950-5849.
  • Zainab, A. N., Chong, C. Y., & Ling, T. C. (2013). Moving a repository of scholarly content to a cloud. Library Hi Tech, 31(2), 201-215.
  • Chong, C. Y., Lee, S. P., & Ling, T. C. (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. ISSN 0127-9084. Volume:27, No 1.
  • Chong, C. Y., & Lee, S. P. (2015). Analyzing maintainability and reliability of object-oriented software using weighted complex network. Journal of Systems and Software, 110, 28-53.
  • Chaudhry, M. T., Chong, C. Y., Ling, T. C., Rasheed, S., & Kim, J. (2016). Thermal Prediction Models for Virtualized Data center Servers by using Thermal-profiles. Malaysian Journal of Computer Science, 29(1).