Dependable Computing for Critical Applications by Mel Barnes (auth.), Prof. Dr. Algirdas Avižienis, Dr.

By Mel Barnes (auth.), Prof. Dr. Algirdas Avižienis, Dr. Jean-Claude Laprie (eds.)

The overseas operating convention on liable Computing for severe functions used to be the 1st convention geared up by way of IFIP operating team 10. four "Dependable Computing and Fault Tolerance", in cooperation with the Technical Committee on Fault-Tolerant Computing of the IEEE computing device Society, and the Technical Committee 7 on structures Reliability, security and safety of EWlCS. the reason for the operating convention is healthier expressed via the goals of WG 10. four: " more and more, participants and agencies are constructing or buying subtle computing structures on whose prone they should position nice reliance. In differing situations, the focal point might be on differing homes of such prone - e. g. continuity, functionality, real-time reaction, skill to prevent catastrophic mess ups, prevention of planned privateness intrusions. The thought of dependability, outlined as that estate of a computing procedure which permits reliance to be justifiably put on the provider it provides, permits those a variety of issues to be subsumed inside of a unmarried conceptual framework. Dependability hence comprises as detailed circumstances such attributes as reliability, availability, protection, defense. The operating staff is aimed toward picking and integrating ways, equipment and methods for specifying, designing, development, assessing, validating, working and protecting computers which may still convey a few or all of those attributes. " the idea that of WG 10. four used to be formulated in the course of the IFIP operating convention on trustworthy Computing and Fault Tolerance on September 27-29, 1979 in London, England, held together with the Europ-IFIP seventy nine convention. Profs A. Avi~ienis (UCLA, l. a., united states) and A.

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Hence in a program of L lines: p =jSL/D. This simple formula, based on textual sampling, is attractive. But parameters j, D and S remain to be known. The second model is derived from the sampling theory defined in a different context by Valiant [38]. Equation 7 is used to evaluate the number N of test data required to ensure that, when a program has not failed during a test experiment, the probability is better than (l-p) that the failure probability is less than p. Hence, with our notation, QN ~ l-p.

1. V k E {l, ... (C=k) = l/x. (2x+3) I 24x3 for odd values of x; p(fl) = p(f2) = (x 2-I) 13x2; p(f3) = (x-I) 12x2. 44 Thevenod-Fosse No simple relation gives the three probabilities p(2), p(5), p(6). e. p(2) = 0 for x < 6. Figure 3 shows the values of p(i) and p(fj) with x E [6, ... 332. 069] ~ PCP < pcp. Figure 3. Probabilities p(i) and p(fj) with the inputs distributed uniformly in the range [I-x]. /\Y // / V. 1 500 1000 500 O~--~ 6 7 N exhaustive __ ............ 0 8 ? ~. - /. x 10 12 14 15 Figure 4.

Our present investigation involves two models. Starting from the system specification, a functional model MF is first defined. MF represents the software "elementary" functions and how they interact. The function granularity depends on the software complexity and on the test level (unit or system test). Then a behavior model MB is derived from MF. g. a Markov chain) must be well suited to: • study the influence of various input probability distributions on the function execution probabilities; a distribution such that each software function is properly scanned should be determined; • infer reliability prediction for a given operational input profile.

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