enamel 发表于 2025-3-21 18:12:13
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Shivkanya Fuloria,Vetriselvan Subramaniyan,Mahendran Sekar,Yuan Seng Wu,Srikumar Chakravarthi,Ruslifactor 5 shorter than traditional requirements in the same domain. Stakeholders understood them and identified missing demands. Suppliers could write excellent proposals with a modest effort. The requirements were a good basis for writing test cases and resolving conflicts during development. The dHectic 发表于 2025-3-22 01:05:41
Kuldeepak Sharma,Mateja Erdani Kreft,Mateja Škufca Sterle,Darko Vasice individual words, which are then matched automatically to 26 reusable semantic rules. The rules were discovered by classifying 335 unique information type phrases extracted from 50 mobile privacy policies. The method was evaluated on 109 unique information types extracted from six privacy policiesGullible 发表于 2025-3-22 05:04:05
http://reply.papertrans.cn/64/6332/633114/633114_4.pngMediocre 发表于 2025-3-22 11:24:39
Khalid Saad Alharbi,Sattam Khulaif Alenezi,Sulaiman Mohammed Alnasser factor 5 shorter than traditional requirements in the same domain. Stakeholders understood them and identified missing demands. Suppliers could write excellent proposals with a modest effort. The requirements were a good basis for writing test cases and resolving conflicts during development. The d心胸开阔 发表于 2025-3-22 15:26:12
C. Sarath Chandran,Anitha Jose Subin,Alan Raj,K. K. Swathy,Indu Raghunathosystem elements that grant them privileges or lack thereof, and consequences that reverberate positively or negatively towards them. We contribute towards stakeholder analysis activities by presenting our power relationships framework, which can be integrated into the requirements process as a tech冥想后 发表于 2025-3-22 18:04:56
Shibi Muralidar,Gayathri Gopal,Senthil Visaga Ambi with 14,983 sentences from 53 requirements documents originating from 18 different domains and shed light on the form and complexity of causality in requirements. Based on our findings, we develop a tool-supported approach for causality detection (CiRA, standing for Causality in Requirement Artifac一起平行 发表于 2025-3-23 00:44:05
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Shvetank Bhatt,K. Sreedhara R. Pai,C. R. Patil,S. N. Manjula,S. Mohana Lakshmients into a graph and does not require any external knowledge bases or other resources. . To improve the performance, we take a detailed look at five common datasets and adapt the graph structure and semantic search algorithm. Depending on the selected configuration, the predictive power strongly vaEndometrium 发表于 2025-3-23 06:26:56
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