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Innovations in Systems and Software Engineering
Erschienen in: Innovations in Systems and Software Engineering 4/2013

01.12.2013 | SI: SwHM

Software health management with Bayesian networks

verfasst von: Johann Schumann, Timmy Mbaya, Ole Mengshoel, Knot Pipatsrisawat, Ashok Srivastava, Arthur Choi, Adnan Darwiche

Erschienen in: Innovations in Systems and Software Engineering | Ausgabe 4/2013

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Abstract

Software health management (SWHM) is an emerging field which addresses the critical need to detect, diagnose, predict, and mitigate adverse events due to software faults and failures. These faults could arise for numerous reasons including coding errors, unanticipated faults or failures in hardware, or problematic interactions with the external environment. This paper demonstrates a novel approach to software health management based on a rigorous Bayesian formulation that monitors the behavior of software and operating system, performs probabilistic diagnosis, and provides information about the most likely root causes of a failure or software problem. Translation of the Bayesian network model into an efficient data structure, an arithmetic circuit, makes it possible to perform SWHM on resource-restricted embedded computing platforms as found in aircraft, unmanned aircraft, or satellites. SWHM is especially important for safety critical systems such as aircraft control systems. In this paper, we demonstrate our Bayesian SWHM system on three realistic scenarios from an aircraft control system: (1) aircraft file-system based faults, (2) signal handling faults, and (3) navigation faults due to inertial measurement unit (IMU) failure or compromised Global Positioning System (GPS) integrity. We show that the method successfully detects and diagnoses faults in these scenarios. We also discuss the importance of verification and validation of SWHM systems.
Vorheriger Artikel Maintaining the health of software monitors
Nächster Artikel Deliberative, search-based mitigation strategies for model-based software health management

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Fußnoten
3
If there is a reason that this command signal is not reliable, the command node \(C\) is used in combination with a \(H\) node to impact state \(U\) (Fig. 3) as further discussed below.
 
4
For example, if we observe a vibration on sensor V, then \(\lambda _\mathrm{v} = 1\) and \(\lambda _{\sim \mathrm{v}} = 0\); if we observe no vibration on sensor V, then \(\lambda _{\sim \mathrm{v}} = 1\) and \(\lambda _\mathrm{v} = 0\); if we have not yet observed the sensor V, we leave \(\lambda _\mathrm{v} = 1\) and \(\lambda _{\sim \mathrm{v}} = 1\).
 
5
Open systems and their interfaces for the electronics in motor vehicles; http://​www.​osek-vdx.​org/​.
 
14
Juvenal: “Who guards the guardians?”
 
Literatur
1.
2.
Anderson T, Lee PA (1981) Fault tolerance. Prentice-Hall International, Englewood Cliffs
3.
Avižienis A (1976) Fault-tolerant systems. IEEE Trans Comput 25(12):1304–1312MATH
4.
Avižienis A (1985) The N-version approach to fault-tolerant software. IEEE Trans Softw Eng 11(12):1491–1501CrossRef
5.
Avižienis A, Laprie JC, Randell B, Landwehr C (2004) Basic concepts and taxonomy of dependable and secure computing. IEEE Trans Depend Secure Comput 1(1):11–33CrossRef
6.
Barringer H, Falcone Y, Finkbeiner B, Havelund K, Lee I, Pace GJ, Rosu G, Sokolsky O, Tillmann N (eds) (2010) Runtime verification—first international conference (RV 2010). Lecture Notes in Computer Science, vol 6418. Springer, Berlin
7.
Bernstein L, Kintala CMR (2004) Software rejuvenation. CrossTalk J Defense Softw Eng 6:23–26
8.
Binder RV (1994) Design for testability in object-oriented systems. Commun ACM 37(9):87–101CrossRef
9.
Bochmann G, Dssouli R, Zhao J (1989) Trace analysis for conformance and arbitration testing. IEEE Trans Softw Eng 15(11):1347–1356CrossRef
10.
Brown D, Roggio R, Cross JH, McCreary C (1992) An automated oracle for software testing. IEEE Trans Reliab 41(2):272–280
11.
Chavira M, Darwiche A (2007) Compiling Bayesian networks using variable elimination. In: Proceedings of the twentieth international joint conference on artificial intelligence (IJCAI-07), pp 2443–2449
12.
Chen L, Avižienis A (1995) N-version programming: a fault-tolerance approach to reliability of software operation. Twenty-fifth international symposium on fault-tolerant computing, ’ Highlights from Twenty-Five Years’, p 113
13.
Codetta-Raiteri D, Portinale L, Guiotto A, Yushstein Y (2012) Evaluation of anomaly and failure scenarios involving an exploration rover: a Bayesian network approach. In: Proceedings of the 11th international symposium on artificial intelligence, robotics, and automation in space (iSAIRAS-2012)
14.
Costa M, Crowcroft J, Castro M, Rowstron A, Zhou L, Zhang L, Barham P (2005) Vigilante: end-to-end containment of Internet worms. In: Proceedings of the symposium on systems and operating systems principles (SOSP), pp 133–147
15.
16.
17.
Darwiche A (2009) Modeling and reasoning with Bayesian networks. Cambridge University Press, Cambridge
18.
Deconinck G, Vounckx J, Lauwereins R, Peperstraete JA (1993) Survey of backward error recovery techniques for multicomputers based on checkpointing and rollback. Int J Model Simul 18:262–265
19.
Delgado N, Gates AQ, Roach S (2004) A taxonomy and catalog of runtime software-fault monitoring tools. IEEE Trans Softw Eng 30(12):859–872. doi:10.​1109/​TSE.​2004.​91
20.
Djurdjanovic D, Liu J, Marko KA, Ni J (2010) Immune systems inspired approach to anomaly detection, fault localization and diagnosis in automotive engines. In: Schumann J, Liu Y (eds) Applications of neural networks in high assurance systems, Studies in Computational Intelligence, vol 268. Springer, Berlin, pp 141–163
21.
Doong RK, Frankl PG (1994) The Astoot approach to testing object-oriented programs. ACM Trans Softw Eng Methodol 3(2):101–130CrossRef
22.
Dubey A, Karsai G, Kereskenyi R, Mahadevan M (2010) A real-time component framework: experience with CCM and ARINC-653. IEEE international symposium on object-oriented real-time distributed computing
23.
Elnozahy ENM, Alvisi L, Wang YM, Johnson DB (2002) A survey of rollback-recovery protocols in message-passing systems. ACM Comput Surv 34(3):375–408CrossRef
24.
Firesmith D (1993) Testing object-oriented software. In: Proceedings of the 11th international conference on technology of object-oriented languages and systems (TOOLS), pp 407–426
25.
Forrest S, Beauchemin C (2007) Computer immunology. Immunol Rev 216(1):176–197
26.
Gärtner FC (1999) Fundamentals of fault-tolerant distributed computing in asynchronous environments. ACM Comput Surv 31(1): 1–26
27.
George S, Evans D, Marchette S (2003) A biological programming model for self-healing. In: Proceedings of the 2003 ACM workshop on survivable and self-regenerative systems (SSRS ’03), ACM, pp 72–81
28.
Ghosh D, Sharman R, Rao RH, Upadhyaya S (2007) Self-healing systems—survey and synthesis. Decis Support Syst 42(4): 2164–2185
29.
Groce A, Joshi R (2008) Exploiting traces in static program analysis: better model checking through printfs. Int J Softw Tools Technol Transf 10(2):131–144CrossRef
30.
Hamou-Lhadj A, Braun E, Amyot D, Lethbridge, T (205) Recovering behavioral design models from execution traces. In: Ninth European conference on software maintenance and reengineering, CSMR 2005, pp 112–121
31.
Harrold M, McGregor J, Fitzpatrick K (1992) Incremental testing of object-oriented class structure. In: Proceedings of the 14th international conference of software engineering, pp 68–80
32.
Hart E, Timmis J (2008) Application areas of AIS: the past, the present and the future. Appl Soft Comput 8(1):191–201CrossRef
33.
Havelund K, Rosu G (2004) Efficient monitoring of safety properties. Int J Softw Tools Technol Transf 6(2):158–173CrossRef
34.
Hecht H (1976) Fault-tolerant software for real-time applications. ACM Comput Surv 8(4):391–407CrossRefMATH
35.
Huang Y, Kintala C, Kolettis N, Fulton ND (1995) Software rejuvenation: analysis, module and applications. Twenty-Fifth international symposium on fault-tolerant computing (FTCS-25), pp 381–390
36.
Jensen FV, Lauritzen SL, Olesen KG (1990) Bayesian updating in causal probabilistic networks by local computations. SIAM J Comput 4:269–282MathSciNet
37.
38.
Keromytis A (2007) The case for self-healing software. In: Aspects of network and information security. Proceedings NATO Advanced Studies Institute (ASI) on network security and intrusion detection
39.
Keromytis AD (2007) Characterizing self-healing software systems. In: Proceedings of the 4th international conference on mathematical methods, models and architectures for computer networks security (MMM-ACNS)
40.
Khurshid S, Sen K (eds) (2012) Runtime verification—second international conference, RV 2011. Lecture Notes in Computer Science, vol 7186. Springer, Berlin
41.
Koo R, Toueg S (1987) Checkpointing and rollback-recovery for distributed systems. IEEE Trans Softw Eng 13(1):23–31CrossRefMATH
42.
Lauritzen S, Spiegelhalter DJ (1988) Local computations with probabilities on graphical structures and their application to expert systems (with discussion). J R Stat Soc Ser B 50(2):157–224MathSciNetMATH
43.
Li Z, D’Ambrosio B (1994) Efficient inference in Bayes nets as a combinatorial optimization problem. Int J Approx Reason 11(1):55–81MathSciNetCrossRefMATH
44.
Lindsey AE, Pecheur C (2004) Simulation-based verification of autonomous controllers via Livingstone Pathfinder. In: Proceedings of 10th international conference on tools and algorithms for the construction and analysis of systems (TACAS), Lecture Notes in Computer Science, vol 2988. Springer, Berlin, pp 357–371
45.
Lyu MR (1995) Software fault tolerance. Wiley, New York
46.
Mengshoel OJ (2007) Designing resource-bounded reasoners using Bayesian networks: system health monitoring and diagnosis. In: Proceedings of the 18th international workshop on principles of diagnosis (DX-07), Nashville, pp 330–337
47.
Mengshoel OJ, Chavira M, Cascio K, Poll S, Darwiche A, Uckun S (2010) Probabilistic model-based diagnosis: an electrical power system case study. I. EEE Trans Syst Man Cybern 40(5): 874–885
48.
Mengshoel OJ, Darwiche A, Uckun S (2008) Sensor validation using Bayesian networks. In: Proceedings of the 9th international symposium on artificial intelligence, robotics, and automation in space (iSAIRAS-08)
49.
Mengshoel OJ, Roth D, Wilkins DC (2011) Portfolios in stochastic local search: Efficiently computing most probable explanations in Bayesian networks. Journal of Automated Reasoning 46(2): 103–160
50.
Mengshoel OJ, Wilkins DC, Roth D (2011) Initialization and restart in stochastic local search: computing a most probable explanation in Bayesian networks. IEEE Trans Knowl Data Eng 23(2):235–247MathSciNetCrossRef
51.
Milea NA, Khoo SC, Lo D, Pop C (2011) Nort: runtime anomaly-based monitoring of malicious behavior for windows. In: [7]
52.
Musliner D, Hendler J, Agrawala AK, Durfee E, Strosnider JK, Paul CJ (1995) The challenges of real-time AI. IEEE Comput 28:58–66CrossRef
53.
54.
O’Malley TO, Richardson DJ, Dillon LK (1996) Efficient specification-based oracles for critical systems. In: Proceedings of the California software symposium, pp 50–59
55.
Park JD, Darwiche A (2004) Complexity results and approximation strategies for MAP explanations. J Artif Intell Res (JAIR) 21: 101–133
56.
Pasareanu CS, Rungta N (2010) Symbolic pathfinder: symbolic execution of Java bytecode. In: Proceedings of the conference on automated software engineering (ASE), ACM, pp 179–180
57.
Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Mateo
58.
59.
Peters DK, Member S, David I, Parnas L, Member S (1998) Using test oracles generated from program documentation. IEEE Trans Softw Eng 24:161–173CrossRef
60.
Pierce WH (1965) Failure-tolerant computer design. Academic Press, New York
61.
Poll S, Patterson-Hine A, Camisa J, Garcia D, Hall D, Lee C, Mengshoel OJ, Neukom C, Nishikawa D, Ossenfort J, Sweet A, Yentus S, Roychoudhury I, Daigle M, Biswas G, Koutsoukos X (2007) Advanced diagnostics and prognostics testbed. In: Proceedings of the 18th international workshop on principles of diagnosis (DX-07), Nashville, pp 178–185
62.
Randell B (1975) System structure for software fault tolerance. In: Proceedings of the international conference on reliable software. ACM, New York, pp 437–449
63.
Reed E, Schumann J, Mengshoel OJ (2011) Verification and validation of system health management models using parametric testing. In: Proceedings of Infotech@Aerospace
64.
Richardson DJ, Aha SL, O’Malley TO (1992) Specification-based test oracles for reactive systems. In: ICSE ’92: Proceedings of the 14th international conference on software engineering. ACM, pp 105–118
65.
Ricks BW, Mengshoel OJ (2009) Methods for probabilistic fault diagnosis: an electrical power system case study. In: Proceedings of annual conference of the PHM Society 2009, PHM-09
66.
67.
Rushby J (2004) Runtime certification. In: Proceedings of runtime verification (RV 2008), Lecture Notes in Computer Science, vol 5289. Springer, New York, pp 21–35
68.
69.
Schroeder B (1995) On-line monitoring: a tutorial. Computer 28(6):72–78CrossRef
70.
Schumann J, Bajwa A, Berg P (2010) Parametric testing of launch vehicle FDDR models. In: AIAA space
71.
Schumann J, Gundy-Burlet K, Pasareanu C, Menzies T, Barrett T (2009) Software V &V support by parametric analysis of large software simulation systems. In: Proceedings of IEEE aerospace. IEEE Press
72.
Schumann J, Mbaya T, Mengshoel OJ (2011) Bayesian software health management for aircraft guidance, navigation, and control. In: Proceedings of conference on prognostics and health management (PHM-2011)
73.
Schumann J, Mengshoel OJ, MBaya T (2011) Integrated software and sensor health management for small spacecraft. In: Proceedings of the 2011 IEEE fourth international conference on space mission challenges for information technology, SMC-IT ’11, IEEE
74.
Schumann J, Mengshoel OJ, Pasareanu CS, Reed E, Yang G (2010) D1: report on initial results of parametric analysis and prototype definition of model-based test case generation. Technical Report NASA/OSMA (SARP)
75.
Schumann J, Mengshoel OJ, Srivastava AN, Darwiche A (2010) Towards software health management with Bayesian networks. In: Proceedings of the FSE/SDP workshop on future of software engineering research, FoSER ’10, ACM, pp 331–336
76.
Schumann J, Morris R, Mbaya T, Mengshoel OJ, Darwiche A (2011) Report on Bayesian approach for dynamic monitoring of software quality and integration with advanced IVHM engine for ISWHM. Technical Report USRA-RIACS
77.
Shenoy PP (1989) A valuation-based language for expert systems. Int J Approx Reason 5(3):383–411CrossRef
78.
Shepard P, Bhatti JA, Humphreys TE (2012) Drone hack: spoofing attack demonstration on a civilian unmanned aerial vehicle. GPS World
79.
Smith R, Korel B (2000) Slicing event traces of large software systems. In: Proceedings of the fourth international workshop on automated debugging (AADEBUG)
80.
Somayaji A, Hofmeyr S, Forrest S (1997) Principles of a computer immune system. In: Proceedings of the second new security paradigms workshop, pp 75–82
81.
Srivastava AN, Schumann J (2011) The case for software health management. In: Proceedings of the 2011 IEEE fourth international conference on space mission challenges for information technology, SMC-IT ’11. IEEE Computer Society, Washington, DC, pp 3–9
82.
Tippenhauer NO, Popper C, Rasmussen K, Capkun S (2011) On the requirements for successful GPS spoofing attacks. In: Proceedings of Chicago communications security conference
83.
Wang J, Guo C, Liu F (2005) Self-healing based software architecture modeling and analysis through a case study. In: Proceedings of networking, sensing and control, IEEE, pp 873–877
84.
Wang Y, King G, Court I, Ross M, Staples G (1997) On testable object-oriented programming. SIGSOFT Softw Eng Notes 22(4):84–90CrossRef
85.
Wang Y, King G, Wickburg H (1999) A method for built-in tests in component-based software maintenance. Software maintenance and reengineering, 1999. In: Proceedings of the third European conference, pp 186–189
86.
Wang Y, Patel D, King G, Court I, Staples G, Ross M, Fayad M (2000) On built-in test reuse in object-oriented framework design. ACM Comput Surv 32:7–12
87.
Garfinkel S (2009) History’s worst software bugs. Wired.com
88.
Zhang NL, Poole D (1996) Exploiting causal independence in Bayesian network inference. J Artif Intell Res 5:301–328MathSciNetMATH
Metadaten
Titel
Software health management with Bayesian networks
verfasst von
Johann Schumann
Timmy Mbaya
Ole Mengshoel
Knot Pipatsrisawat
Ashok Srivastava
Arthur Choi
Adnan Darwiche
Publikationsdatum
01.12.2013
Verlag
Springer London
Erschienen in
Innovations in Systems and Software Engineering / Ausgabe 4/2013
Print ISSN: 1614-5046
Elektronische ISSN: 1614-5054
DOI
https://doi.org/10.1007/s11334-013-0214-y

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