Automated Software Requirement Classification in Data-Scarce Environments

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This study explores the pressing need for automated requirement classification in software development, addressing high project failure rates and the challenges posed by manual classification. It reviews existing classification techniques, highlighting their limitations and the lack of systematic hypothesis engineering. Current methods face issues like dependency on extensive datasets and embedding-based approaches. A novel 5-dimensional framework is proposed, demonstrating the effectiveness...