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 develops a software reliability growth model that incorporates imperfect debugging and learning effects. The objective is to improve the accuracy and practical applicability of software reliability growth models by addressing computational difficulties commonly encountered in parameter estimation. In particular, this study emphasizes confidence interval estimation to enhance the reliability and interpretability of software reliability predictions.
Methods: A simplified estimation approach is proposed to efficiently estimate model parameters and to construct confidence intervals. The proposed method provides both upper and lower bounds for the software reliability growth model. Numerical examples are presented to illustrate the implementation procedure and to demonstrate the practicality of the proposed approach.
Results: The proposed model demonstrates superior performance across several evaluation criteria compared with existing models in the literature, while relying on more realistic and practical assumptions.
Conclusion: This study presents a novel software reliability growth model that effectively captures imperfect debugging and learning effects. The results indicate that the proposed model serves as a valuable tool for assessing software quality and can be readily applied to real-world software systems.
Methods: A simplified estimation approach is proposed to efficiently estimate model parameters and to construct confidence intervals. The proposed method provides both upper and lower bounds for the software reliability growth model. Numerical examples are presented to illustrate the implementation procedure and to demonstrate the practicality of the proposed approach.
Results: The proposed model demonstrates superior performance across several evaluation criteria compared with existing models in the literature, while relying on more realistic and practical assumptions.
Conclusion: This study presents a novel software reliability growth model that effectively captures imperfect debugging and learning effects. The results indicate that the proposed model serves as a valuable tool for assessing software quality and can be readily applied to real-world software systems.
본문·목차
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UCI(KEPA) : I410-151-26-02-094593525
