Multiverse debugging extends classical and omniscient debugging to allow the exhaustive exploration of non-deterministic and concurrent systems during debug sessions. The introduction of user-defined reductions significantly improves the scalability of the approach. However, the literature fails to recognize the importance of using more expressive logics, besides local-state predicates, to express breakpoints. In this article, we address this problem by introducing temporal breakpoints for multiverse debugging. Temporal breakpoints greatly enhance the expressivity of conditional breakpoints, allowing users to reason about the past and future of computations in the multiverse. Moreover, we show that it is relatively straightforward to extend a language-agnostic multiverse debugger semantics with temporal breakpoints, while preserving its generality. To show the elegance and practicability of our approach, we have implemented a multiverse debugger for the AnimUML modeling environment that supports 3 different temporal breakpoint formalisms: regular-expressions, statecharts, and statechart-based Büchi automata.

Multiverse debugging is an extension of classical debugging methods, particularly adapted to non-deterministic systems. Recently, a language-independent formalization was proposed. Moreover, multiverse debugging is particularly beneficial for specification and design languages, such as UML. However, this method suffers from scalability issues during breakpoint lookup. This problem arises due to the exhaustive exploration performed on the potentially infinite state-space of the system.

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Cybersecurity is becoming vital as industries are gradually moving from automating physical processes to a higher level automation using cyber physical systems (CPS) and internet of things (IoT). In this context, security is becoming a continuous process that runs in parallel to other processes during the complete life cycle of a system. Traditional threat analysis methods use design models alongside threat models as an input for security analysis, hence missing the life-cycle-based dynamicity required by the security concern. In this paper, we argue for an attacker-aware systems modeling language that exposes the systems attack surfaces. For this purpose, we have designed Pimca, a domain specific modeling language geared towards capturing the attacker point of view of the system. This study introduces the formalism along with the Pimca workbench, a framework designed to ease the development and manipulation of the Pimca models. Finally, we present two relevant use cases, serving as a preliminary validation of our approach. © Copyright 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

Summary Model‐checking enables the automated formal verification of software systems through the explicit enumeration of all the reachable states. While this technique has been successfully applied to industrial systems, it suffers from the state‐space explosion problem because of the exponential growth in the number of states with respect to the number of interacting components. In this paper, we present a new reachability analysis algorithm, named Past‐Free[ze], that reduces the state‐space explosion problem by freeing parts of the state‐space from memory. This algorithm relies on the explicit isolation of the acyclic parts of the system before analysis. The parallel composition of these parts drives the reachability analysis, the core of all model‐checkers. During the execution, the past states of the system are freed from memory making room for more future states. To enable counter‐example construction, the past states can be stored on external storage. To show the effectiveness of the approach, the algorithm was implemented in the OBPObservation Engineand was evaluated both on a synthetic benchmark and on realistic case studies from automotive and aerospace domains. The benchmark, composed of 50 test cases, shows that in average, 75%of the state‐space can be dropped from memory thus enabling the exploration of up to 14 times more states than traditional approaches. Moreover, in some cases, the reachability analysis time can be reduced by up to 25%. In realistic settings, the use of Past‐Free[ze] enabled the exploration of a state‐space 4.5 times larger on the automotive case study, where almost 50%of the states are freed from memory. Moreover, this approach offers the possibility of analyzing an arbitrary number of interactions between the environment and the system‐under‐verification; for instance, in the case of the aerospace example, 1000 pilot/system interactions could be analyzed unraveling an 80 GB state‐space using only 10 GB of memory. Copyright © 2016 John Wiley & Sons, Ltd.