Critical systems, such as space systems, are developed under strict requirements envisaging high integrity in accordance to specific standards. For such software systems, an independent assessment is put into effect (Independent Software Verification and Validation – ISVV) after the regular development lifecycle and V&V activities, aiming at finding residual faults and raising confidence in the software. However, it has been observed that there is still a significant number of defects remaining at this stage, questioning the effectiveness of the previous engineering processes. This paper presents a root cause analysis of 1070 defects found in four space software projects during ISVV, by applying an improved Orthogonal Defect Classification (ODC) taxonomy and examining the defect types, triggers and impacts, in order to identify why they reached such a later stage in the development. The paper also puts forward proposals for modifications to both the software development (to prevent defects) and the V&V activities (to better detect defects) and an assessment methodology for future works on root cause analysis.
Keywords
ODC, Critical systems, Defect, Classification, Root cause analysis, Dependability
Subject
Root cause analysis of space software defects by using an enhanced ODC taxonomy.
Related Project
CECRIS – CErtification of CRItical Systems
Journal
Reliability Engineering & System Safety, Vol. 158, #2, pp. 213-229, Carlos Guedes Soares, February 2017
DOI
Cited by
Year 2020 : 3 citations
Samrat Chatterjee, Shital Thekdi,
An iterative learning and inference approach to managing dynamic cyber vulnerabilities of complex systems, Reliability Engineering & System Safety, Volume 193, 2020, 106664, ISSN 0951-8320, https://doi.org/10.1016/j.ress.2019.106664. (http://www.sciencedirect.com/science/article/pii/S0951832018314558)
João Agnelo, Nuno Laranjeiro, Jorge Bernardino, Using Orthogonal Defect Classification to characterize NoSQL database defects, Journal of Systems and Software, Volume 159, 2020, 110451, ISSN 0164-1212, https://doi.org/10.1016/j.jss.2019.110451. (http://www.sciencedirect.com/science/article/pii/S0164121219302250)
Knutsen, Eric E., Detecting Failures and Locating Faults in Global Scale Online Services Using Bayesian Networks, The George Washington University, ProQuest Dissertations Publishing, 2020. 27544911.
Year 2019 : 2 citations
Seunghwa Jung, Jihwan P. Choi,
Predicting system failure rates of SRAM-based FPGA on-board processors in space radiation environments, Reliability Engineering & System Safety, Volume 183, 2019, Pages 374-386, ISSN 0951-8320, https://doi.org/10.1016/j.ress.2018.09.015. (http://www.sciencedirect.com/science/article/pii/S0951832018304459)
Santoso, S., Bakhri, S., & Situmorang, J. (2019). A Bayesian Network Approach to Estimating Software Reliability of RSG-GAS Reactor Protection System. Atom Indonesia, 45(1), 43-49. doi:http://dx.doi.org/10.17146/aij.2019.775
Year 2017 : 2 citations
Sobel, Karen. "Root Cause Analysis: Parsing Complex Challenges in Academic Libraries." The Journal of Academic Librarianship (2017).
Gallina, B., R. Natella, D. Cotroneo, L. De Simone, S. Rosiello, H. Madeira, A. Lanzaro et al. "Journal special issues." Reliability Engineering & System Safety (Elsevier) 158 (2017): 152-253.
