인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Purpose: This study aims to improve software reliability growth models (SRGMs) by introducing a decreasing fault detection rate that reflects the temporal characteristics of real testing environments.
Methods: A new non-homogeneous Poisson process (NHPP) SRGM is proposed, in which the fault detection rate is defined as a time-dependent decreasing function. Model parameters were estimated using the least squares method based on an actual failure dataset, and the proposed SRGM was compared with 10 traditional SRGMs across 9 evaluation criteria.
Results: The proposed SRGM demonstrated the best model fit and predictive performance on both datasets.
Conclusion: The proposed NHPP SRGM effectively captures the decreasing fault detection efficiency over time and provides more accurate reliability predictions than conventional SRGMs.
Methods: A new non-homogeneous Poisson process (NHPP) SRGM is proposed, in which the fault detection rate is defined as a time-dependent decreasing function. Model parameters were estimated using the least squares method based on an actual failure dataset, and the proposed SRGM was compared with 10 traditional SRGMs across 9 evaluation criteria.
Results: The proposed SRGM demonstrated the best model fit and predictive performance on both datasets.
Conclusion: The proposed NHPP SRGM effectively captures the decreasing fault detection efficiency over time and provides more accurate reliability predictions than conventional SRGMs.
본문·목차
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