KEYNOTE & invited SPEAKERS

Keynote Speaker I

Prof. Witold Pedrycz (IEEE Fellow)
University of Alberta, Canada

Dr. Witold Pedrycz (IEEE Fellow, 1998) is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. In 2009 Dr. Pedrycz was elected a foreign member of the Polish Academy of Sciences. In 2012 he was elected a Fellow of the Royal Society of Canada. In 2007 he received a prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society. He is a recipient of the IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society, and 2019 Meritorious Service Award from the IEEE Systems Man and Cybernetics Society. His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery and data science, pattern recognition, data science, knowledge-based neural networks, and control engineering. He has published papers in these areas. He is also an author of 21 research monographs and edited volumes covering various aspects of Computational Intelligence, data mining, and Software Engineering. Dr. Pedrycz is vigorously involved in editorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Co-editor-in-Chief of Int. J. of Granular Computing (Springer) and J. of Data Information and Management (Springer).  He serves on an Advisory Board of IEEE Transactions on Fuzzy Systems and is a member of a number of editorial boards of international journals.

Keynote Speaker II

Prof.  Kamal Z. Zamli
Universiti Malaysia Pahang, Malaysia

Prof. Dr. Kamal Z. Zamli obtained his BSc from Worcester Polytechnic Institute, USA in 1992, MSc in Real Time Software Engineering from the Centre for Advanced Software Engineering, Universiti Teknologi Malaysia in 2000, and PhD in Software Engineering from the University of Newcastle upon Tyne, UK in 2003. His current research focuses on Search based Software Engineering specializing on Software Testing, Soft Computing and Optimization.

Speech title : Exploiting Multi-Factorial Optimization: The Case for Test Redundancy Reduction Problem
Speech Abstract : Software testing relates to the process of assessing conformances of the software under test (SUT) to its specification. Typically, a set of tests (i.e., forming a test suite) is often established and is individually mapped against the given user requirements. Owing to lack of close coordination, software development endeavours that involves a large team of people can potentially be susceptible to redundant tests whereby some requirements are covered by more than one test. (i.e., as new tests are added and old tests are dropped reflecting from dynamic changes in the user requirements).
By formalizing the test redundancy reduction as an optimization problem, metaheuristic-based test redundancy strategies have shown promising results in terms of getting optimal test reduction than its general computational based counterparts. Although helpful, much existing metaheuristic-based strategies have focused on solving the test redundancy reduction problem as a single task problem (i.e., one-test redundancy task-at-at-time). Yet, to meet customer deadlines (and increase market capacity), testers are forced to carry out multiple testing related tasks in a simultaneous fashion. Recent advancement within the metaheuristic communities, from single objective (single task) optimization (SOO), multi-objective optimization (MOO) and now multi-factorial optimization (MFO) has now opened new and exciting possibilities for supporting multi-task test redundancy reduction.
Like Multi Objective Optimization (MOO), MFO also involves optimization of multiple objective functions. Unlike MOO which deals with resolving trade-off between two or more competing objectives, MFO allows multiple optimization tasks (with each individual objective) to be addressed simultaneously based on a single evolving population. More precisely, MFO leverages on the concept of skill factor to associate solutions with specific tasks while allowing for the sharing of knowledge between them. During population update processes, individuals are evaluated only by their aligned tasks according to the skill factors. This talk will elaborate on our experiences related to the adoption of MFO for addressing the test redundancy reduction problem. In doing so, we will also highlight the potential opportunities and related issues that can be further explored.

Invited Speakers