Search Results - (( developing team extraction algorithm ) OR ( java implication based algorithm ))

Finding an effective classification technique to develop a software team composition model by Gilal, Abdul Rehman, Jaafar, Jafreezal, Capretz, Luiz Fernando, Omar, Mazni, Basri, Shuib, Abdul Aziz, Izzatdin

“…Ineffective software team composition has become recognized as a prominent aspect of software project failures.Reports from results extracted from different theoretical personality models have produced contradicting fits, validity challenges, and missing guidance during software development personnel selection.It is also believed that the technique/s used while developing a model can impact the overall results.Thus, this study aims to: 1) discover an effective classification technique to solve the problem, and 2) develop a model for composition of the software development team.The model developed was composed of three predictors: team role, personality types, and gender variables; it also contained one outcome: team performance variable.The techniques used for model development were logistic regression, decision tree, and Rough Sets Theory (RST).Higher prediction accuracy and reduced patte rn complexity were the two parameters forselecting the effective technique.Based on the results, the Johnson Algorithm (JA) of RST appeared to be an effective technique for a team composition model.The study has proposed a set of 24 decision rules for finding effective team members.These rules involve gender classification to highlight the appropriate personality profile for software developers.In the end, this study concludes that selecting an appropriate classification technique is one of the most important factors in developing effective models.…”
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Finding an effective classification technique to develop a software team composition model by Gilal, A.R., Jaafar, J., Capretz, L.F., Omar, M., Basri, S., Aziz, I.A.

“…The model developed was composed of 3 predictors: team role, personality types, and gender variables; it also contained 1 outcome: team performance variable. …”
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Finding an effective classification technique to develop a software team composition model by Gilal, A.R., Jaafar, J., Capretz, L.F., Omar, M., Basri, S., Aziz, I.A.

“…The model developed was composed of 3 predictors: team role, personality types, and gender variables; it also contained 1 outcome: team performance variable. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Shared mental model as an enabler of Malaysia waqf land development by Ismail, Nur Azlin, Omar, Ismail, Abu Bakar, Mohamad Noor Ropiah, Suhaili, Nur Aqidah, Hussin, Rohayati

“…The adoption and adaption cycle was going so quickly along the waqf land development. 2.SMM also being interacted by other indirect team members. who involves in the WSA development 3. …”
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Crowdsource requirements engineering: Using online reviews as input to software features clustering by Bakar N.H., Kasirun Z.M., Salleh N., Halim A.H.

Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Crowdsource requirements engineering: Using online reviews as input to software features clustering by Bakar, Noor Hasrina, M. Kasirun, Zarinah, Salleh, Norsaremah, Halim, Azni H.

“…This information can be valuable for software development teams to enhance the software functionalities in the next releases. …”
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Understanding the Causes of Design Errors in Construction Projects: A DEMATEL-Based Framework by Lee, Chia Kuang, Chai, Changsaar, Bujna, Marián, Muhammad Ashraf, Fauzi, Boo, Ying Qi

“…Nine causes of design errors were extracted and synthesized from the literature. Subsequently, 15 experts were interviewed, and the data collected was analyzed using the decision-making trial and evaluation laboratory (DEMATEL) algorithm. …”
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Automate customer support handling E-Commerce enquiry using ChatGPT by Teo, Wen Jin

“…Machine Learning-based Named Entity Recognition (NER) is employed to identify and extract specific entities, while contextual analysis algorithms determine message relevance for summarization. …”
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