한국인터넷방송통신학회논문지 (The Journal of the Institute of Internet, Broadcasting and Communication)

  • 제15권4호
  • /
  • Pages.9-18
  • /
  • 2015
  • /
  • 2289-0238(pISSN)
  • /
  • 2289-0246(eISSN)
한국인터넷방송통신학회 (The Institute of Internet, Broadcasting and Communication)
권호 표지
DOI QR코드

DOI QR Code

레거시 소프트웨어 시스템을 위한 문맥 독립적 행위 기반 실시간 오작동 탐지 기법

Runtime Fault Detection Method based on Context Insensitive Behavioral Model for Legacy Software Systems

  • 김순태 (전북대학교 소프트웨어공학과)
  • Kim, Suntae (Dept. of Software Engineering, Chunbuk National University)
  • 투고 : 2015.07.28
  • 심사 : 2015.08.07
  • 발행 : 2015.08.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

최근에는 스마트폰과 같이 임베디드 형태로 다양한 장소에서 서비스를 제공하는 어플리케이션의 수가 늘어나는 추세이다. 기존의 고정된 장소에서의 실행 환경보다 서비스 실행 중 상태가 동적으로 변할 수 있다는 점으로 인해 실행 중 오작동이 발생할 수 있다. 이 문제를 다루기 위하여 본 연구에서는 레거시 소프트웨어 시스템을 대상으로 메서드 수준의 오작동 탐지 기능의 구축기법을 제안한다. 기존의 문맥 의존적 행위 모델 기반으로 비정상 행위를 탐지하는 방식 메서드 수준의 탐지에 적용 시 거짓 양성의 발생 비율 증가, 모니터링 오버헤드 증가 등의 문제가 발생 가능하다. 이를 향상하기 위해 본 연구에서는 문맥 독립적 행위 모델 기반 오작동 탐지(Context-Insensitive Behavior Model-based Failure Detection, CIBFD) 기법을 제안한다. 사례 연구를 통해 기존 연구 대비 탐지 결과를 비교 분석하고, 어플리케이션 도메인 별 기법의 효용성을 분석한다.

In recent years, the number of applications embedded in the various devices such as a smart phone is getting larger. Due to the frequent changes of states in the execution environment, various malfunctions may occur. In order to handle the issue, this paper suggests an approach to detecting method-level failures in the legacy software systems. We can determine if the software executes the abnormal behavior based on the behavior model. However, when we apply the context-sensitive behavior model to the method-level, several problems happen such as false alarms and monitoring overhead. To tackle those issues, we propose CIBFD (Context-Insensitive Behavior Model-based Failure Detection) method. Through the case studies, we compare CIBFD method with the existing method. In addition, we analyze the effectiveness of the method for each application domains.

키워드

참고문헌

  1. Kindberg, T. and Fox, A., "System Software for Ubiquitous Computing," IEEE Pervasive Computing, Vol. 1, No. 1, pp. 70-81, 2002. https://doi.org/10.1109/MPRV.2002.993146
  2. Kim, J. S., Park, S. Y. and Sugumaran, V., "Contextual Problem Detection and Management during Software Execution in Complex Environments," Industrial Management & Data Systems, Vol. 106, No. 4, pp. 540-561, 2006. https://doi.org/10.1108/02635570610661615
  3. DARPA, "Self Adaptive Software," BAA 98-12, Proposer Information Pamphlet, www.darpa.mil/ito/Solicitations/PIP_9812.html, 1997.
  4. IBM, "An Architectural Blueprint for Autonomic Computing, Third edition," Tech. Report, 2005.
  5. Horn, P., "Autonomic Computing: IBM's Perspective on the State of Information Technology," IBM Corporation, 2001.
  6. Wong, W. E. and Debroy, V., "Software Fault Localization," IEEE Reliability Society 2009 Annual Technology Report, January 2010.
  7. Kephart, J. O. and Chess, D. M., "The Vision of Autonomic Computing," IEEE Computer, Vol. 36, No. 11, pp. 41-52, 2003. https://doi.org/10.1109/MC.2003.1160055
  8. Kim, M., Kannan, S., Lee, I., Sokolsky, O. and Viswanathan, M., "Java-MaC: a Run-time Assurance Tool for Java Programs," Electronic Notes in Theoretical Computer Science, Vol. 55, Issue 2, pp. 218-235, 2001. https://doi.org/10.1016/S1571-0661(04)00254-3
  9. Hlady, M., Kovacevic, R., Li, J. J., Pekilis, B. R., Prairie, D., Savor, T., Seviora, R. E., Simser, D. and Vorobiev, A., "An Approach to Automatic Detection of Software Failures," Proceedings of 6th International Symposium on Software Reliability Engineering, pp. 24-27, 1995.
  10. Mariani, L., Pastore, F. and Pezze, M., "Dynamic Analysis for Diagnosing Integration Faults," IEEE Trans. On Software Engineering, Vol. 37, Issue 4, pp. 486-508, 2011. https://doi.org/10.1109/TSE.2010.93
  11. Chang, H., Mariani, L. and Pezze, M., "In-Field Healing of Integration Problems with COTS Components," Proceedings of ICSE 2009, pp. 166-176, 2009.
  12. Cook, J. E. and Wolf, A. L., "Discovering Models of Software Processes from Event-Based Data," ACM Trans. On Software Engineering and Methodology, Vol. 7, Issue 3, pp. 215-249, 1998. https://doi.org/10.1145/287000.287001
  13. Fischmeister, S. and Lam, P., "Time-Aware Instrumentation of Embedded Software," IEEE Trans. on Industrial Informatics, Vol. 6, No. 4, pp. 652-663, 2010. https://doi.org/10.1109/TII.2010.2068304
  14. Kanstren, T., "Towards A Taxonomy of Dynamic Invariants in Software Behavior," Proceeding of 2nd International Conference on Pervasive Patterns and Applications, pp. 20-27, 2010.
  15. Y. Yang, B. Chang, "A Probability Embedded Expert System to Detect and Resolve Network Faults Intelligently," International Journal of Internet, Broadcasting and Communication, Vol 11, No 2, pp135-143, 2011