Tuesday, April 07, 2020

Journal of Information and Communication Technology (JICT) Vol.13, 2014

A RULE-BASED APPROACH FOR DISCOVERING EFFECTIVESOFTWARE TEAM COMPOSITION
¹Abdul Rehman Gilal, ²Mazni Omar & ³Kamal Imran Sharif
¹ Sukkur Institute of Business Administration, Pakistan
²School of Computing, Universiti Utara Malaysia, Malaysia
³School of Technology Management & Logistic,
Universiti Utara Malaysia, Malaysia
a-rehman@iba-suk.edu.my; mazni@uum.edu.my; kamalimran@uum.edu.my
 
ABSTRACT FULL TEXT
Human aspects in software engineering play a key role in composing effective team members. However, to date there is no general consensus on the effective personality types and diversity based on software team roles. Thus, this paper aims to discover the effective personality types and diversity based on two software team roles – team leader and programmer by using a rule-based approach. The rule-based approach by employing the rough set technique was used to discover patterns of the data selected. In this study, four main steps were involved to discover the patterns – reduct generation rules, rules generation, rules fi ltering, and rules evaluation. The results show that the rules generated achieved acceptable prediction accuracy with more than 70 per cent accuracy. In addition, the ROC value achieved 0.65, which indicates the rule-based model is valid and useful. The results reveal that the extrovert personality type is dominant for both software team roles and a homogeneous or heterogeneous team plays an equal role to determine an effective team. This study provides useful rules for decision makers to understand and get insight into selecting effective team members that lead to producing high quality software.
 
Keywords: Software team composition, personality types, diversity, team roles, rule-based.
 

 
STATISTICALLY CONTROLLED ROBUST TRUST COMPUTING MECHANISM FOR CLOUD COMPUTING
¹Mohamed Firdhous, ²Osman Ghazali & ³Suhaidi Hassan
¹Faculty of Information Technology, University of Moratuwa, Sri Lanka
² , ³School of Computing, Universiti Utara Malaysia, Malaysia
firdhous@uom.ik; osman@uum.edu.my; suhaidi@uum.edu.my
 
ABSTRACT FULL TEXT
Quality of service plays an important role in making distributed systems. Users prefer service providers who meet the commitments specifi ed in the Service Level Agreements to these who violate them. Cloud computing has been the recent entrant to the distributed system market and has revolutionized it by transforming the way the resources are accessed and paid for. Users can access cloud services including hardware, development platform and applications and pay only for the usage similar to the other utilities. Trust computing mechanisms can play an important role in identifying the right service providers who would meet the commitments specifi ed in the Service Level Agreements. Literature has reported several trust computing mechanisms for different distributed systems based on various algorithms and functions. Almost all of them modify the trust scores monotonously even for momentary performance deviations that are reported. This paper proposes a trust computing mechanism that statistically validates the attribute monitored before modifying the trust scores using a hysteresisbased algorithm. Hence the proposed mechanism can protect the trust scores from changes due to momentary fl uctuations in system performances. The experiments conducted show that the trust scores computed using the proposed mechanism are more representative of the long-term system performance than the ones that were computed without the validation of the inputs.
 
Keywords: Trust management, system performance, system fl uctuations.
 

 
A GENERALIZED E-LEARNING USAGE BEHAVIOUR MODEL BY DATA MINING TECHNIQUE
¹Songsakda Chayanukro, ²Massudi Mahmuddin & ³Husniza Husni
¹Suan Dusit Rajabhat University, Thailand
², ³School of Computing, Universiti Utara Malaysia, Malaysia
songsakda@gmail.com; ady@uum.edu.my; husniza@uum.edu.my
 
ABSTRACT FULL TEXT
Current study on e-Learning user’s behaviour model obtained the specific models. In many cases, the e-Learning user’s behaviour model for open source e-Learning system such as Moodle, which can predict learning outcome or learning performance is still defi cient and cannot generally apply in many institutions due to the fact that the majority of prediction models were developed particularly for certain institutions. This study proposes to produce a general model that can make a prediction of learning outcome inspired by Skinner’s theory, which explains the relationship between learner, achievement, and learner reinforcement. This study proposes similar patterns in e-Learning user’s behaviour models of different institutions by the data-mining