Timing Analysis of Event-Driven Programs with Directed Testing

Accurately estimating the worst-case execution time (WCET) of real-time event-driven software is crucial. For example, NASA\u27s study of unintended acceleration in Toyota vehicles highlights poor support in timing analysis for event-driven code, which could put human life in danger. WCET occurs during the longest possible execution path in a program. Static analysis produces safe but overestimated measurements. Dynamic analysis, on other hand, measures actual execution times of code under a test suite. Its performance depends on the branch coverage, which itself is sensitive to scheduling of events. Thus dynamic analysis often underestimates the WCET. We present a new dynamic approach called event-driven directed testing. Our approach combines aspects of prior random-testing techniques devised for event-driven code with the directed testing method applied to sequential code. The aim is to come up with complex event sequences and choices of parameters for individual events that might result in execution times closer to the true WCET. Our experiments show that, compared to random testing, genetic algorithms, and traditional directed testing, we achieve significantly better branch coverage and longer WCET

Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis

Even with impressive advances in automated formal methods, certain problems in system verification and synthesis remain challenging. Examples include the verification of quantitative properties of software involving constraints on timing and energy consumption, and the automatic synthesis of systems from specifications. The major challenges include environment modeling, incompleteness in specifications, and the complexity of underlying decision problems. This position paper proposes sciduction, an approach to tackle these challenges by integrating inductive inference, deductive reasoning, and structure hypotheses. Deductive reasoning, which leads from general rules or concepts to conclusions about specific problem instances, includes techniques such as logical inference and constraint solving. Inductive inference, which generalizes from specific instances to yield a concept, includes algorithmic learning from examples. Structure hypotheses are used to define the class of artifacts, such as invariants or program fragments, generated during verification or synthesis. Sciduction constrains inductive and deductive reasoning using structure hypotheses, and actively combines inductive and deductive reasoning: for instance, deductive techniques generate examples for learning, and inductive reasoning is used to guide the deductive engines. We illustrate this approach with three applications: (i) timing analysis of software; (ii) synthesis of loop-free programs, and (iii) controller synthesis for hybrid systems. Some future applications are also discussed

Specifications and programs for computer software validation

Three software products developed during the study are reported and include: (1) FORTRAN Automatic Code Evaluation System, (2) the Specification Language System, and (3) the Array Index Validation System

A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data

A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even remote brain regions is inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models, some techniques as Granger causality (GC) are purely data-driven and rely on statistical prediction and temporal precedence. While powerful and widely applicable, this approach could suffer from two main limitations when applied to BOLD fMRI data: confounding effect of hemodynamic response function (HRF) and conditioning to a large number of variables in presence of short time series. For task-related fMRI, neural population dynamics can be captured by modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI on the other hand, the absence of explicit inputs makes this task more difficult, unless relying on some specific prior physiological hypothesis. In order to overcome these issues and to allow a more general approach, here we present a simple and novel blind-deconvolution technique for BOLD-fMRI signal. Coming to the second limitation, a fully multivariate conditioning with short and noisy data leads to computational problems due to overfitting. Furthermore, conceptual issues arise in presence of redundancy. We thus apply partial conditioning to a limited subset of variables in the framework of information theory, as recently proposed. Mixing these two improvements we compare the differences between BOLD and deconvolved BOLD level effective networks and draw some conclusions