Reliability Assessment of Legacy Safety-Critical Systems Upgraded with Fault-Tolerant Off-the-Shelf Software

This paper presents a new way of applying Bayesian assessment to systems, which consist of many components. Full Bayesian inference with such systems is problematic, because it is computationally hard and, far more seriously, one needs to specify a multivariate prior distribution with many counterintuitive dependencies between the probabilities of component failures. The approach taken here is one of decomposition. The system is decomposed into partial views of the systems or part thereof with different degrees of detail and then a mechanism of propagating the knowledge obtained with the more refined views back to the coarser views is applied (recalibration of coarse models). The paper describes the recalibration technique and then evaluates the accuracy of recalibrated models numerically on contrived examples using two techniques: u-plot and prequential likelihood, developed by others for software reliability growth models. The results indicate that the recalibrated predictions are often more accurate than the predictions obtained with the less detailed models, although this is not guaranteed. The techniques used to assess the accuracy of the predictions are accurate enough for one to be able to choose the model giving the most accurate prediction

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Too Trivial To Test? An Inverse View on Defect Prediction to Identify Methods with Low Fault Risk

Background. Test resources are usually limited and therefore it is often not possible to completely test an application before a release. To cope with the problem of scarce resources, development teams can apply defect prediction to identify fault-prone code regions. However, defect prediction tends to low precision in cross-project prediction scenarios. Aims. We take an inverse view on defect prediction and aim to identify methods that can be deferred when testing because they contain hardly any faults due to their code being "trivial". We expect that characteristics of such methods might be project-independent, so that our approach could improve cross-project predictions. Method. We compute code metrics and apply association rule mining to create rules for identifying methods with low fault risk. We conduct an empirical study to assess our approach with six Java open-source projects containing precise fault data at the method level. Results. Our results show that inverse defect prediction can identify approx. 32-44% of the methods of a project to have a low fault risk; on average, they are about six times less likely to contain a fault than other methods. In cross-project predictions with larger, more diversified training sets, identified methods are even eleven times less likely to contain a fault. Conclusions. Inverse defect prediction supports the efficient allocation of test resources by identifying methods that can be treated with less priority in testing activities and is well applicable in cross-project prediction scenarios.Comment: Submitted to PeerJ C

Positive Feedback, Memory and the Predictability of Earthquakes

We review the "critical point" concept for large earthquakes and enlarge it in the framework of so-called "finite-time singularities". The singular behavior associated with accelerated seismic release is shown to result from a positive feedback of the seismic activity on its release rate. The most important mechanisms for such positive feedback are presented. We introduce and solve analytically a novel simple model of geometrical positive feedback in which the stress shadow cast by the last large earthquake is progressively fragmented by the increasing tectonic stress. Finally, we present a somewhat speculative figure that tends to support a mechanism based on the decay of stress shadows. This figure suggests that a large earthquake in Southern California of size similar to the 1812 great event is maturing.Comment: PostScript document of 18 pages + 2 eps figure