Falsification of Cyber-Physical Systems with Robustness-Guided Black-Box Checking

For exhaustive formal verification, industrial-scale cyber-physical systems (CPSs) are often too large and complex, and lightweight alternatives (e.g., monitoring and testing) have attracted the attention of both industrial practitioners and academic researchers. Falsification is one popular testing method of CPSs utilizing stochastic optimization. In state-of-the-art falsification methods, the result of the previous falsification trials is discarded, and we always try to falsify without any prior knowledge. To concisely memorize such prior information on the CPS model and exploit it, we employ Black-box checking (BBC), which is a combination of automata learning and model checking. Moreover, we enhance BBC using the robust semantics of STL formulas, which is the essential gadget in falsification. Our experiment results suggest that our robustness-guided BBC outperforms a state-of-the-art falsification tool.Comment: Accepted to HSCC 202

Time-Staging Enhancement of Hybrid System Falsification

Optimization-based falsification employs stochastic optimization algorithms to search for error input of hybrid systems. In this paper we introduce a simple idea to enhance falsification, namely time staging, that allows the time-causal structure of time-dependent signals to be exploited by the optimizers. Time staging consists of running a falsification solver multiple times, from one interval to another, incrementally constructing an input signal candidate. Our experiments show that time staging can dramatically increase performance in some realistic examples. We also present theoretical results that suggest the kinds of models and specifications for which time staging is likely to be effective

Combining k-Induction with Continuously-Refined Invariants

Bounded model checking (BMC) is a well-known and successful technique for finding bugs in software. k-induction is an approach to extend BMC-based approaches from falsification to verification. Automatically generated auxiliary invariants can be used to strengthen the induction hypothesis. We improve this approach and further increase effectiveness and efficiency in the following way: we start with light-weight invariants and refine these invariants continuously during the analysis. We present and evaluate an implementation of our approach in the open-source verification-framework CPAchecker. Our experiments show that combining k-induction with continuously-refined invariants significantly increases effectiveness and efficiency, and outperforms all existing implementations of k-induction-based software verification in terms of successful verification results.Comment: 12 pages, 5 figures, 2 tables, 2 algorithm

The role of falsification in the development of cognitive architectures: insights from a Lakatosian analysis

It has been suggested that the enterprise of developing mechanistic theories of the human cognitive architecture is flawed because the theories produced are not directly falsifiable. Newell attempted to sidestep this criticism by arguing for a Lakatosian model of scientific progress in which cognitive architectures should be understood as theories that develop over time. However, Newell’s own candidate cognitive architecture adhered only loosely to Lakatosian principles. This paper reconsiders the role of falsification and the potential utility of Lakatosian principles in the development of cognitive architectures. It is argued that a lack of direct falsifiability need not undermine the scientific development of a cognitive architecture if broadly Lakatosian principles are adopted. Moreover, it is demonstrated that the Lakatosian concepts of positive and negative heuristics for theory development and of general heuristic power offer methods for guiding the development of an architecture and for evaluating the contribution and potential of an architecture’s research program

What is abductive inference?

Abductive reasoning: constitutes according to Peirce the "first stage" of scientific inquiries (CP 6.469) and of any interpretive processes. "Abduction" is the process of adopting an explanatory hypothesis (CP 5.145) and covers two operations: the selection and the formation of plausible hypotheses. As process of finding premisses, it is the basis of interpretive reconstruction of causes and intentions, as well as of inventive construction of theories

AGAINST MECHANISM: METHODOLOGY FOR AN EVOLUTIONARY ECONOMICS

When the first economics departments were proposed at Cambridge and Oxford, the proponents thought acceptance would be improved if economics could be seen as incorporating the methods of physics. The enterprise was premised on the existence of economic laws that describe invariant relationships between events. These event regularities, like gravity, were not affected by human action. Humans could adapt and use them, but not change them. Thus the metaphor of "mechanism" seemed appropriate and became embedded in economists' language. It is common to use the term market mechanism to link prices and commodities. This suggests the economy is like turning a crank attached to a set of gears where there is a fixed relationship between the crank's motion and the last gear's motion. The gears have no ideas of their own, they don't get mad; there is no cognitive element between events and action.Institutional and Behavioral Economics,