Object-oriented programming semantics education based on intelligent agents

Aris, Teh Noranis Mohd; Nazeer, Shahrin Azuan

doi:10.1109/mysec.2011.6140706

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…Our algorithm that has been improved from our previous work [21] used samples of sequential problem and succeeded to produce accurate results as expected. The input, process and output are extracted and transformed correctly by the PAC agent following the correct sequence by the IPO agent and the module number generated accordingly.…”

Section: Results Findingssupporting

confidence: 58%

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A Multi-Agent Model for Information Processing in Computational Problem Solving

Noranis¹,

Azuan²

2013

IJMO

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Abstract-Problem solving is vital in the computer programming course. It is the earliest topic that is emphasized and more time is allocated to teach the topic. Problem solving requires the problem understanding knowledge that novice students usually lacks. In order to assist novice students in computational problem solving, a multi-agent model is designed. The proposed model is different from existing model in terms of the unique architecture that utilizes agents for information processing, specifically to extract, transform and generate information. Five agents are designed for this purpose namely the GUI, PAC, IPO, Flowchart and Algorithm agents. The model is tested with three different kinds of problem statement and produced correct results.Index Terms-Computational problem solving, information processing, multi-agent, problem understanding.

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Section: Results Findingssupporting

confidence: 58%

“…3. The algorithm is extended from our previous work [21] which is integrated inside the process method in the ProblemSolvingAgentGUI class as in Fig. 2.…”

Section: The Process Methodsmentioning

confidence: 99%

A Multi-Agent Model for Information Processing in Computational Problem Solving

Noranis¹,

Azuan²

2013

IJMO

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An Explorative Study on Code Comprehension: Issues and Challenges

Kaur,

Pandey,

Pandey

et al. 2024

Advances in Science, Technology &Amp; Innovation

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Intelligent Method for Cost Estimation during Software Maintenance

Mazumdar

2017

ijecs

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Software maintenance is very important and time consuming task which require a lot of parameters to be taken care during the whole cycle of maintenance of software. The software maintenance engineer has used various cost estimation models in their research work like COCOMO-I, COCOMO-II, SLIM, SEER-SEM and FP. The problem with these models is that they require the complete data and information making any decision and benchmark. Therefore the cost of software maintenance varies with types and complexity of the software under the maintenance. We have identified the various factors which effects the cost of the software maintenance. The various cost estimation models require a lots of metrics and only after calculating the complete parameters, then only the software cost can be determined.To reduce the software maintenance cost the proposed model has been introduce which can overcome the cost even there is missing information. The Probability factor (PF) method has been used which can overcome the cost. The proposed model used the various factors as input and accordingly the probability of each factors has been calculated. Some of quality factor (in different software) can't be computed  Sometimes cost of compute all of quality factors are very high  We need long time for compute all of quality factors  In some cases we need to a lot of people (user, quality manager, member of quality assurance's team) to compute quality factors Some other researchers have also deal with uncertainty in quality factor and matrices. Motameni et al. (2010) proposed a model for software quality with BNs and ISO9126 quality model. There model doesn't have last quality model's problems and can predict software quality with incomplete and uncertain data. Also by this model the time and cost of calculating software quality is reduced.

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Object-oriented programming semantics education based on intelligent agents

Cited by 4 publications

References 6 publications

A Multi-Agent Model for Information Processing in Computational Problem Solving

A Multi-Agent Model for Information Processing in Computational Problem Solving

An Explorative Study on Code Comprehension: Issues and Challenges

Intelligent Method for Cost Estimation during Software Maintenance

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