On an Automated Translation of Satellite Procedures Using Triple Graph Grammars

Hermann, Frank; Gottmann, Susann; Nachtigall, Nico; Braatz, Benjamin; Morelli, Gianluigi; Alain, Pierre; Engel, Thomas

doi:10.1007/978-3-642-38883-5_4

Cited by 12 publications

(7 citation statements)

References 5 publications

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“…The NeoFoX tool is able to process these FOP procedures and translate them automatically to low-level procedure code in SPELL format [1][2][3]. Although advanced technics, such as triple graph grammars, have been proposed to perform automated translation of satellite procedures [4], the conversion between FOP and SPELL formats in NeoFoX is performed by an internal parser derived from a custom-defined grammar of the FOP format.…”

Section: Fig 1 -Example Of Operational Procedures In Fop Formatmentioning

confidence: 99%

vFOX: Web Monitoring of Satellite Operations

Moreno

Fraga

2018

2018 SpaceOps Conference

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Section: Fig 1 -Example Of Operational Procedures In Fop Formatmentioning

confidence: 99%

vFOX: Web Monitoring of Satellite Operations

Moreno

Fraga

2018

2018 SpaceOps Conference

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“…The third step of our methodology is to describe relevant context events and TC operations using GT rules (see Section 4). As described in [78], it is also a realistic assumption that developers acquire these skills. Additionally, the GT rules presented in Section 4 will be reusable, maybe with modifications, in a large number of cases.…”

Section: Are Gt Rules Suitable To Specify Context Events and Tc Operamentioning

confidence: 99%

A systematic approach to constructing incremental topology control algorithms using graph transformation

Kluge

Stein²,

Varr

et al. 2017

Journal of Visual Languages & Computing

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This document corresponds to the accepted manuscript of the article Kluge, R., Stein, M., Varró, G., Schürr, A., Hollick, M., Mühlhäuser, M.: "A Systematic Approach to Constructing Incremental Topology Control Algorithms using Graph Transformation," in: JVLC 2016. The URL of the formal version is https://dx.Communication networks form the backbone of our society. Topology control algorithms optimize the topology of such communication networks. Due to the importance of communication networks, a topology control algorithm should guarantee certain required consistency properties (e.g., connectivity of the topology), while achieving desired optimization properties (e.g., a bounded number of neighbors). Real-world topologies are dynamic (e.g., because nodes join, leave, or move within the network), which requires topology control algorithms to operate in an incremental way, i.e., based on the recently introduced modifications of a topology. Visual programming and specification languages are a proven means for specifying the structure as well as consistency and optimization properties of topologies. In this paper, we present a novel methodology, based on a visual graph transformation and graph constraint language, for developing incremental topology control algorithms that are guaranteed to fulfill a set of specified consistency and optimization constraints. More specifically, we model the possible modifications of a topology control algorithm and the environment using graph transformation rules, and we describe consistency and optimization properties using graph constraints. On this basis, we apply and extend a well-known constructive approach to derive refined graph transformation rules that preserve these graph constraints. We apply our methodology to re-engineer an established topology control algorithm, kTC, and evaluate it in a network simulation study to show the practical applicability of our approach. Meta-modeling [Sec. 2] WSN meta-model Topology control (TC) Graph constraints [Sec. 3] Structural constraints TC constraints Connectivity Consistency Graph transformation [Sec. 4] TC rules TC algorithm Context event rules Rule refinement [Sec. 5] Rule refinement algorithm Restoring rule applicability Enforcing termination Proving preservation of connectivity Evaluation [Sec. 6] RQ1: Correctness RQ2: Incrementality RQ3: Generalizability RQ4: Performance Figure 1: Overview of the proposed constructive methodology and structure of this paper (i) We characterize the required and desired properties of output topologies of a TC algorithm using graph constraints (as described in [42]).(ii) We use GT rules to describe TC operations, which specify the possible modifications during an execution of a TC algorithm, and context events, which specify the possible modifications by the environment.(iii) We apply and enhance the constructive refinement approach for GT rules presented in [42,43] to TC. Our approach is able to cope with temporarily constraint-violating rules, which contrary to [42,43] may not be restricted.(iv) We ...

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“…With the help of the University of Luxembourg (SnT), PIL2SPELL was created. 8,9 PIL2SPELL works by parsing the PIL source code (parsing is performed with Xtext) 10 into an extended abstract syntax tree (implemented using the Eclipse Modeling Framework, EMF).…”

Section: Satellite Name Measat-3bmentioning

confidence: 99%

“…8 The complexity of this stage is justified by the formal approach to the PIL  SPELL translation being addressed. In particular, several correctness properties are ensured due to the usage of TGGs (syntactical correctness of translation results, functional behavior, completeness of translation, i.e., every input graph can be translated).…”

Section: Satellite Name Measat-3bmentioning

confidence: 99%