gerard.j.holzmann4jpl.nasa.gov

Statistical debugging uses lightweight instrumentation and statistical models to identify program behaviors that are strongly predictive of failure. However, most software is mostly correct; nearly all monitored behaviors are poor predictors of failure. We propose an adaptive monitoring strategy that mitigates the overhead associated with monitoring poor failure predictors. We begin by monitoring a small portion of the program, then automatically refine instrumentation over time to zero in on bugs. We formulate this approach as a search on the control-dependence graph of the program. We present and evaluate various heuristics that can be used for this search. We also discuss the construction of a binary instrumentor for incorporating the feedback loop into post-deployment monitoring. Performance measurements show that adaptive bug isolation yields an average performance overhead of 1 % for a class of large applications, as opposed to 87 % for realistic sampling-based instrumentation and 300 % for complete binary instrumentation.

this paper, we focus on methods of fault tolerance, and investigate the differences between hardware fault tolerance and software fault tolerance. 1.2 Fault, Error and Failure

It is a cruel reality that the goal of producing "perfect software " remains elusive. When software is part of a critical system, it is necessary to estimate the risk associated with its use. Software reliability is defined as the probability of failure free execution given a specific environment and a fixed time interval. The goal of reliability assessment is not just to estimate the failure probability of the program, `, but to gain the statistical confidence that ` is realistic. The transformational approach to software reliability assessment is a novel methodology which combines the strengths of formal verification and statistical testing in a unified and original reliability assessment framework. Program transformations and partial program proofs are used to amplify the effect of test cases; that is, they allow us to infer the behavior of the program on many inputs based on its behavior on one input. The main effect of the application of these transformations is the reduction in t...