Rayhanul Islam

Work place: Institute of Leather Engineering and Technology, University of Dhaka

E-mail: rayhanul.islam@du.ac.bd


Research Interests: Software Engineering, Computational Learning Theory, Data Mining, Data Structures and Algorithms


Rayhanul Islam is currently working as a Lecturer at the Institute of Leather Engineering and Technology (ILET), University of Dhaka, Dhaka, Bangladesh. He has completed Master of Science in Software Engineering (MSSE) with the thesis in Software source code’s dimension reduction for defect prediction from Information Technology (IIT), University of Dhaka. He also worked as Associate Software Engineer at a renowned software company named KAZ Software Ltd. His research interests include Software Engineering, Machine Learning, and Data Mining. Currently, he is doing researches in his desired fields by combining his work experiences and research interests.

Author Articles
Reduction of Multiple Move Method Suggestions Using Total Call-Frequencies of Distinct Entities

By Atish Kumar Dipongkor Rayhanul Islam Nadia Nahar Iftekhar Ahmed Kishan Kumar Ganguly S.M. Arif Raian Abdus Satter

DOI: https://doi.org/10.5815/ijieeb.2020.04.03, Pub. Date: 8 Aug. 2020

Inappropriate placement of methods causes Feature Envy (FE) code smell and makes classes coupled with each other. To achieve cohesion among classes, FE code smell can be removed using automated Move Method Refactoring (MMR) suggestions. However, challenges arise when existing techniques provide multiple MMR suggestions for a single FE instance. The developers need to manually find an appropriate target classes for applying MMR as an FE instance cannot be moved to multiple classes. In this paper, a technique is proposed named MultiMMRSReducer, to reduce multiple MMR suggestions by considering the Total Call-Frequencies of Distinct Entities (TCFDE). Experimental results show that TCFDE can reduce the multiple MMR suggestions of an FE instance and performs 77.92% better than an existing approach, namely, JDeodorant. Moreover, it can ensure minimum future changes in the dependent classes of an FE instance.

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An Environment Aware Learning-based Self-Adaptation Technique with Reusable Components

By Kishan Kumar Ganguly Md. Saeed Siddik Rayhanul Islam Kazi Sakib

DOI: https://doi.org/10.5815/ijmecs.2019.06.06, Pub. Date: 8 Jun. 2019

Self-adaptive systems appeared in order to reduce the effort of manual software maintenance. Apart from software attributes, for example, different alternative software modules, self-adaptation decisions depend on environmental attributes, for example, service rate, bandwidth etc. Current well-known self-adaptation approaches can be further improved by incorporating environmental attributes. Moreover, reducing maintenance effort includes minimizing both operational and development effort. To reduce the effort of developing self-adaptive software, the constituent components should be reusable. This paper proposes a technique to incorporate environmental attributes to learning-based self-adaptation and to increase the reuse potential of self-adaptive system components. The environmental attributes are provided as a constraint to an optimization problem which results in an optimal software attribute selection. Design patterns for self-adaptive system components are proposed to improve its reusability. The proposed technique was validated on a news serving website called Znn.com. According to renowned reusability metrics such as Lines of Code (LOC), Message Passing Coupling (MPC) and Lack of Cohesion of Methods 4 (LCOM4), the proposed technique improved reuse potential. The website was further tested for adaptation effectiveness under two scenarios – adaptation and without adaptation. According to our experiments, Adaptation gradually improved the main goal response time of the website where it performed poorly without adaptation.

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