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008			260206t20252025my a|||fr|||| 000 0 eng d
020			_aTHE0010328 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFKOM .A44 2025 r Thesis
100	1		_aTan, Amelia, _eauthor.
245	1	0	_aA fuzzy multi-criteria requirements prioritization model using cost-value on stakeholder perspectives / _cTan Amelia
264		1	_aKuantan, Pahang : _bUMPSA, _c2025
264		4	_c© 2025
300			_axii, 198 pages : _billustrations ; _c30 cm. + _e1 CD-ROM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Computing
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang Al Sultan Abdullah - 2025
504			_aInclude bibliographical reference
520	3		_aRequirements prioritization is a critical phase in requirements engineering to ensure limited resources are allocated to produce optimum business value. Traditional cost-value models, while commonly used, suffer from issues of subjectivity, limited stakeholder participation, and scalability problems. Stakeholders usually label all requirements as high priority, which complicates decision-making. This, combined with inadequate stakeholder involvement, an excessive number of evaluation criteria, and a lack of empirical validation, reduces the effectiveness and practicality of prioritization models in real-world applications. This study aims to enhance the cost-value approach in requirements prioritization by integrating the BOCR classification with FAHP and FTOPSIS. The objective is to measure the perspectives of clients and developers in producing prioritization results while improving overall accuracy, reducing time consumption, and increasing usability. A semi-automated prioritization model named Reprocolla is developed by combining the Fuzzy Analytical Hierarchy Process (FAHP) for weighting criteria and the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) for ranking requirements. Reprocolla adopts the BOCR (Benefits, Opportunities, Costs, Risks) framework to classify evaluation criteria and utilizes fuzzy logic to mitigate human judgment uncertainty. The proposed model was validated using seven datasets derived from both real-world software projects and benchmark datasets. The evaluation used a combination of inferential and descriptive statistics. The Pearson Correlation Coefficient was used to assess accuracy to measure alignment with ground truth prioritization. The Mann–Whitney U test was used to identify significant differences in time consumption between clients and developers. The Chi-Square test was used to determine the relationship between user roles and perceived usability levels. Reprocolla improved prioritization accuracy by 5% compared to the base model (without BOCR), as measured by the Pearson Correlation Coefficient. It also reduced average prioritization time by 36% and received the highest ease-of-use score by 70%. Statistical analysis revealed no significant difference in prioritization outcomes between clients and developers. This suggests that Reprocolla is consistent and balanced across different stakeholder perspectives. This research contributes an empirically validated, stakeholder-inclusive, and scalable prioritization model that improves decision-making performance in software development. It also provides an open dataset and a web-based application, which is a software requirements prioritization tool. It reinforces the relevance and applicability of the cost-value approach to supporting effective decision-making in software requirements engineering.
610	2	0	_aFaculty of Computing _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS
999			_c104072 _d104078