ScholarWorks@숙명여자대학교

초록

Disputes over Disputes over software completeness frequently arise in development contracts and outsourced projects due to the ambiguity surrounding the implementation status of contractual requirements. This paper proposes a novel framework to address this issue by introducing an objective and automated approach to software completeness evaluation. The framework utilizes NLP techniques to extract requirements from contract documents and matches them with implemented source code using deep learning-based analysis. By quantitatively assessing the degree to which the implemented code fulfills the specified requirements, the framework reduces reliance on the subjective judgments of human evaluators. It supports both quantitative and qualitative evaluation criteria, offering improvements in assessment reliability, cost-efficiency, and time-effectiveness. A case study is presented to validate the framework’s effectiveness, demonstrate its applicability as a practical tool for appraisal, quality assurance, and development monitoring.

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