메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
배정호 (국방과학연구소 제1기술연구본부) 장부철 (국방과학연구소 제1기술연구본부) 구봉주 (국방과학연구소 제1기술연구본부)
저널정보
한국군사과학기술학회 한국군사과학기술학회지 한국군사과학기술학회지 제20권 제5호
발행연도
2017.1
수록면
688 - 699 (12page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
Model-based test, a well-known method of the black box tests, is consisted of the following four steps : model construction using requirement, test case generation from the model, execution of a SUT (software under test) and detection failures. Among models constructed in the first step, state-based models such as UML standard State Machine are commonly used to design event-based embedded systems (e.g., weapon control systems). To generate test cases from state-based models in the next step, coverage-based techniques such as state coverage and transition coverage are used. Round-trip path coverage technique using W-Method, one of coverage-based techniques, is known as more effective method than others. However it has a limitation of low failure observability because the W-Method technique terminates a testing process when arrivals meet states already visited and it is hard to decide the current state is completely same or not with the previous in the case like the GUI environment. In other words, there can exist unrevealed faults. Therefore, this study suggests a Extended W-Method. The Extended W-Method extends the round-trip path to a final state to improve failure observability. In this paper, we compare effectiveness and efficiency with requirement-item-based technique, W-Method and our Extended W-Method. The result shows that our technique can detect five and two more faults respectively and has the performance of 28 % and 42 % higher failure detection probability than the requirement-item-based and W-Method techniques, respectively.

목차

등록된 정보가 없습니다.

참고문헌 (10)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0