<span style="font-family:Verdana;">Existing prioritization techniques do not support communication among stakeholders and this makes it difficult for stakeholders to understand the meaning and essence ...<span style="font-family:Verdana;">Existing prioritization techniques do not support communication among stakeholders and this makes it difficult for stakeholders to understand the meaning and essence of requirements before prioritization commences. When this happens, the ordered list of requirements can be misleading. The aim of this research is to develop a method capable of supporting and computing ranks of requirements based on the criteria defined for each requirement. The proposed method is developed based on fuzzy logic. Results show that ordered requirements reproduced ranks with strong correlations when compared to their linguistic values provided by the stakeholders. The contribution of this paper centers on an improved way of prioritizing requirements with understanding.</span>展开更多
Expert systems are being utilized increasingly in medical fields for the purposes of assisting diagnosis and treatment planning. Existing systems used few symptoms for dental diagnosis. In Dentistry, few symptoms are ...Expert systems are being utilized increasingly in medical fields for the purposes of assisting diagnosis and treatment planning. Existing systems used few symptoms for dental diagnosis. In Dentistry, few symptoms are not enough for diagnosis. In this research, a conditional probability model (Bayes rule) was developed with increased number of symptoms associated with a disease for diagnosis. A test set of recurrent cases was then used to test the diagnostic capacity of the system. The generated diagnosis matched that of the human experts. The system was also tested for its capacity to handle uncommon dental diseases and the system portrayed useful potential.展开更多
<p align="left"> <span style="font-family:Verdana;">During the model-based software testing process, test cases are generated from modeled requirements to conduct acceptance testing. Ho...<p align="left"> <span style="font-family:Verdana;">During the model-based software testing process, test cases are generated from modeled requirements to conduct acceptance testing. However, existing approaches generate erroneous test cases, lack full coverage criteria and prototype tools. Therefore, the aim of this research is to develop an approach capable of reducing erroneous test case generation based on full coverage criteria and a prototype tool. The method employed was to develop a parser to extract information from the XMI file of a modeling diagram where a tree is constructed and a traversal operation executed on the nodes and edges to generate test cases. The results obtained from the proposed approach showed that 97.35% of the generated test cases were precise and comprehensive enough to conduct testing because 99.01% of all the nodes and edges were fully covered during the traversal operations.</span> </p>展开更多
文摘<span style="font-family:Verdana;">Existing prioritization techniques do not support communication among stakeholders and this makes it difficult for stakeholders to understand the meaning and essence of requirements before prioritization commences. When this happens, the ordered list of requirements can be misleading. The aim of this research is to develop a method capable of supporting and computing ranks of requirements based on the criteria defined for each requirement. The proposed method is developed based on fuzzy logic. Results show that ordered requirements reproduced ranks with strong correlations when compared to their linguistic values provided by the stakeholders. The contribution of this paper centers on an improved way of prioritizing requirements with understanding.</span>
文摘Expert systems are being utilized increasingly in medical fields for the purposes of assisting diagnosis and treatment planning. Existing systems used few symptoms for dental diagnosis. In Dentistry, few symptoms are not enough for diagnosis. In this research, a conditional probability model (Bayes rule) was developed with increased number of symptoms associated with a disease for diagnosis. A test set of recurrent cases was then used to test the diagnostic capacity of the system. The generated diagnosis matched that of the human experts. The system was also tested for its capacity to handle uncommon dental diseases and the system portrayed useful potential.
文摘<p align="left"> <span style="font-family:Verdana;">During the model-based software testing process, test cases are generated from modeled requirements to conduct acceptance testing. However, existing approaches generate erroneous test cases, lack full coverage criteria and prototype tools. Therefore, the aim of this research is to develop an approach capable of reducing erroneous test case generation based on full coverage criteria and a prototype tool. The method employed was to develop a parser to extract information from the XMI file of a modeling diagram where a tree is constructed and a traversal operation executed on the nodes and edges to generate test cases. The results obtained from the proposed approach showed that 97.35% of the generated test cases were precise and comprehensive enough to conduct testing because 99.01% of all the nodes and edges were fully covered during the traversal operations.</span> </p>
