Using reversible computing to achieve fail-safety

This paper describes a fail-safe design approach that can be used to achieve a high level of fail-safety with conventional computing equipment which may contain design flaws. The method is based on the well-established concept of reversible computing. Conventional programs destroy information and hence cannot be reversed. However it is easy to define a virtual machine that preserves sufficient intermediate information to permit reversal. Any program implemented on this virtual machine is inherently reversible. The integrity of a calculation can therefore be checked by reversing back from the output values and checking for the equivalence of intermediate values and original input values. By using different machine instructions on the forward and reverse paths, errors in any single instruction execution can be revealed. Random corruptions in data values are also detected. An assessment of the performance of the reversible computer design for a simple reactor trip application indicates that it runs about ten times slower than a conventional software implementation and requires about 20 kilobytes of additional storage. The trials also show a fail-safe bias of better than 99.998% for random data corruptions, and it is argued that failures due to systematic flaws could achieve similar levels of fail-safe bias. Potential extensions and applications of the technique are discussed

MC/DC based estimation and detection of residual faults in PLC logic networks

A logic coverage measure related to MC/DC testing is used to estimate residual faults. The residual fault prediction method is evaluated on an industrial PLC logic example. A randomized form of MC/DC testing is used to maximize coverage growth and fault detection efficiency

Overcoming non-determinism in testing smart devices: how to build models of device behaviour

Justification of smart instruments has become an important topic in the nuclear industry. In practice, however, the publicly available artefacts are often the only source of information about the device. Therefore, in many cases independent black-box testing may be the only way to increase the confidence in the device. In this paper we provide a set of recommendations, which we consider to be the best practices for performing black-box assessments. We present our method of testing smart instruments, in which we use the publicly available artefacts only. We present a test harness and describe a method of test automation. We focus on the analysis of test results, which is made particularly complex by the inherent non determinism in the testing of analogue devices. In the paper we analyse the sources of non-determinism, which for instance may arise from inaccuracy in an analogue measurement made by the device when two alternative actions are possible. We propose three alternative ideas on how to build models of device behaviour, which can cope with this kind of non-determinism. We compare and contrast these three solutions, and express our recommendations. Finally, we use a case study, in which a black box assessment of two similar smart instruments is performed to illustrate the differences between the solutions

Worst Case Reliability Prediction Based on a Prior Estimate of Residual Defects

In this paper we extend an earlier worst case bound reliability theory to derive a worst case reliability function R(t), which gives the worst case probability of surviving a further time t given an estimate of residual defects in the software N and a prior test time T. The earlier theory and its extension are presented and the paper also considers the case where there is a low probability of any defect existing in the program. For the "fractional defect" case, there can be a high probability of surviving any subsequent time t. The implications of the theory are discussed and compared with alternative reliability models

A grounded theory of young tennis players’ use of music to manipulate emotional state

The main objectives of this study were (a) to elucidate young tennis players’ use of music to manipulate emotional states, and (b) to present a model grounded in present data to illustrate this phenomenon and to stimulate further research. Anecdotal evidence suggests that music listening is used regularly by elite athletes as a preperformance strategy, but only limited empirical evidence corroborates such use. Young tennis players (N = 14) were selected purposively for interview and diary data collection. Results indicated that participants consciously selected music to elicit various emotional states; frequently reported consequences of music listening included improved mood, increased arousal, and visual and auditory imagery. The choice of music tracks and the impact of music listening were mediated by a number of factors, including extramusical associations, inspirational lyrics, music properties, and desired emotional state. Implications for the future investigation of preperformance music are discussed

Using a Log-normal Failure Rate Distribution for Worst Case Bound Reliability Prediction

Prior research has suggested that the failure rates of faults follow a log normal distribution. We propose a specific model where distributions close to a log normal arise naturally from the program structure. The log normal distribution presents a problem when used in reliability growth models as it is not mathematically tractable. However we demonstrate that a worst case bound can be estimated that is less pessimistic than our earlier worst case bound theory