A Temporal Model for Interactive Diagnosis of Adaptive Systems

Mouline, Ludovic; Benelallam, Amine; Fouquet, Francois; Bourcier, Johann; Barais, Olivier

doi:10.1109/icac.2018.00029

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…Authors of [7] propose stochastic game analysis and latency awareness, a kind of time-awareness, for proactive self-adaptation. In [27] the authors tackle the problem of tracking historical changes as well. To do that, they use causal relationships between requirements and their corresponding adaptations.…”

Section: Research Baselinementioning

confidence: 99%

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Temporal Models for History-Aware Explainability

Parra-Ullauri

García-Domínguez

Garcia-Paucar

et al. 2020

Proceedings of the 12th System Analysis and Modelling Conference

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On one hand, there has been a growing interest towards the application of AI-based learning and evolutionary programming for selfadaptation under uncertainty. On the other hand, self-explanation is one of the self-* properties that has been neglected. This is paradoxical as self-explanation is inevitably needed when using such techniques. In this paper, we argue that a self-adaptive autonomous system (SAS) needs an infrastructure and capabilities to be able to look at its own history to explain and reason why the system has reached its current state. The infrastructure and capabilities need to be built based on the right conceptual models in such a way that the system's history can be stored, queried to be used in the context of the decision-making algorithms.The explanation capabilities are framed in four incremental levels, from forensic self-explanation to automated history-aware (HA) systems. Incremental capabilities imply that capabilities at Level should be available for capabilities at Level + 1. We demonstrate our current reassuring results related to Level 1 and Level 2, using temporal graph-based models. Specifically, we explain how Level 1 supports forensic accounting after the system's execution. We also present how to enable on-line historical analyses while the self-adaptive system is running, underpinned by the capabilities provided by Level 2. An architecture which allows recording of temporal data that can be queried to explain behaviour has been presented, and the overheads that would be imposed by live analysis are discussed. Future research opportunities are envisioned. CCS CONCEPTS• Software and its engineering → System modeling languages; Integration frameworks; Model-driven software engineering.

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Section: Research Baselinementioning

confidence: 99%

“…According to the current state of these aspects, they can explain why the system has adapted its behavior. Authors in [27] tackle a very related issue, i.e interactive diagnosis.…”

Section: Research Baselinementioning

confidence: 99%

Temporal Models for History-Aware Explainability

Parra-Ullauri

García-Domínguez

Garcia-Paucar

et al. 2020

Proceedings of the 12th System Analysis and Modelling Conference

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“…Temporal extensions have also been applied to specific types of systems (e.g., adaptive systems (Mouline et al 2018)) and DSLs (e.g. timed Petri nets (Bender et al 2008)).…”

Section: Temporal Modeling Languagesmentioning

confidence: 99%

Temporal Models on Time Series Databases.

Mazak¹,

Wolny²,

Gomez³

et al. 2020

JOT

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With the emergence of Cyber-Physical Systems (CPS), several sophisticated runtime monitoring solutions have been proposed in order to deal with extensive execution logs. One promising development in this respect is the integration of time series databases that support the storage of massive amounts of historical data as well as to provide fast query capabilities to reason about runtime properties of such CPS. In this paper, we discuss how conceptual modeling can benefit from time series databases, and vice versa. In particular, we present how metamodels and their instances, i.e., models, can be partially mapped to time series databases. Thus, the traceability between design and simulation/runtime activities can be ensured by retrieving and accessing runtime information, i.e., time series data, in design models. On this basis, the contribution of this paper is four-fold. First, a dedicated profile for annotating design models for time series databases is presented. Second, a mapping for integrating the metamodeling framework EMF with InfluxDB is introduced as a technology backbone enabling two distinct mapping strategies for model information. Third, we demonstrate how continuous time series queries can be combined with the Object Constraint Language (OCL) for navigation through models, now enriched with derived runtime properties. Finally, we also present an initial evaluation of the different mapping strategies with respect to data storage and query performance. Our initial results show the efficiency of applying derived runtime properties as time series queries also for large model histories.

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“…1 Overview of system and InTempo interaction resent the most recent snapshot; the evolution of the model, i.e., its history, has been generally neglected [16,17] although its key role in enabling more informed decision-making during the system lifetime [38,45] and postmortem analysis [14] has been long recognized. Only recently did solutions surface which exploit this potential, e.g., via detection of recurrent behavior patterns and behavior explanation [44], analysis of temporal requirements [84], or inference of probabilistic adaptations based on past interactions [40].…”

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confidence: 99%

Incremental execution of temporal graph queries over runtime models with history and its applications

et al. 2021

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Modern software systems are intricate and operate in highly dynamic environments for which few assumptions can be made at design-time. This setting has sparked an interest in solutions that use a runtime model which reflects the system state and operational context to monitor and adapt the system in reaction to changes during its runtime. Few solutions focus on the evolution of the model over time, i.e., its history, although history is required for monitoring temporal behaviors and may enable more informed decision-making. One reason is that handling the history of a runtime model poses an important technical challenge, as it requires tracing a part of the model over multiple model snapshots in a timely manner. Additionally, the runtime setting calls for memory-efficient measures to store and check these snapshots. Following the common practice of representing a runtime model as a typed attributed graph, we introduce a language which supports the formulation of temporal graph queries, i.e., queries on the ordering and timing in which structural changes in the history of a runtime model occurred. We present a querying scheme for the execution of temporal graph queries over history-aware runtime models. Features such as temporal logic operators in queries, the incremental execution, the option to discard history that is no longer relevant to queries, and the in-memory storage of the model, distinguish our scheme from relevant solutions. By incorporating temporal operators, temporal graph queries can be used for runtime monitoring of temporal logic formulas. Building on this capability, we present an implementation of the scheme that is evaluated for runtime querying, monitoring, and adaptation scenarios from two application domains.

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A Temporal Model for Interactive Diagnosis of Adaptive Systems

Cited by 8 publications

References 19 publications

Temporal Models for History-Aware Explainability

Temporal Models for History-Aware Explainability

Temporal Models on Time Series Databases.

Incremental execution of temporal graph queries over runtime models with history and its applications

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