Joint Optimization and Reachability Analysis in Graph Transformation Systems with Time

Gyapay, Szilvia; Schmidt, Ákos; Varró, Dániel

doi:10.1016/j.entcs.2004.02.062

Cited by 12 publications

(2 citation statements)

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“…Outside the SBMDE community, there are many crossover and mutation operations proposed [16], [17], [18]. These surveys identify more than 50 crossover operations [20] -Random Kessentini et al [21] -Random Faunes et al [22], [23] N point Random Baki et al [24] Single-point Random Saada et al [5] Single-point Random Kessentini et al [25] -Random Mkaouer et al [26] Single-point Random Gyapay et al [27] --Denil et al [3] -Random Abdeen et al [28] Single-point Random Fleck et al [29] Single-point Random Nisbet et al [30] --Cooper et al [31] Single-point Random Fatiregun et al [32] --Fatiregun et al [33] Single-point Random Kulkarni et al [34] Single-point Random Fatiregun et al [35] Single-point Random Fleurey et al [4] -Random Cadavid et al [36] -Random Wang et al [37] -Random Shelburg et al [38] -Random Jilani et al [39] -Random Sahin et al [40] Single-point Random Rose and Poulding [41] -Random Batot [42] Single-point Random Kessentini et al [43], [44] Single-point Random Faunes et al [45] Single-point Random Kessentini et al [46] Single-point Random Mansoor et al [47] Single-point Random Debreceni et al [48] --Ghannem et al [49] Single-point Random Ghannem et al [50] Single-point Random Amoui et al [51] Single-point Random Jensen and Cheng …”

Section: Related Workmentioning

confidence: 99%

Utilizing Automatic Query Reformulations as Genetic Operations to Improve Feature Location in Software Models

Pérez

Ziadi²,

Cetina

2022

IIEEE Trans. Software Eng.

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In the combination of Model-Driven Engineering (MDE) and Search-Based Software Engineering (SBSE), genetic operations are one of the key ingredients. Our work proposes a novel adaptation of automatic query reformulations as genetic operations that leverage the latent semantics of software models (the cornerstone artefact of MDE). We analyze the impact of these reformulation operations in a real-world industrial case study of feature location in models. As baselines, we use: 1) the widespread single-point crossover plus random mutation; and 2) mask crossover plus random mutation, which is the best performer for feature location in models. We also perform a statistical analysis to provide quantitative evidence of the impact of the results and to show that this impact is significant. Our reformulation operations improve the results of the best baseline by 37.73% in recall and 14.08% in precision. These results are relevant for the task of feature location in models (one of the main activities performed during software maintenance and evolution). Furthermore, given that the only requirement to apply our approach is term availability in models, our work opens a new research direction to improve more tasks in MDE such as bug location or requirements traceability.

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Section: Related Workmentioning

confidence: 99%

Utilizing Automatic Query Reformulations as Genetic Operations to Improve Feature Location in Software Models

Pérez

Ziadi²,

Cetina

2022

IIEEE Trans. Software Eng.

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“…In policies, what is modeled is the possible persistence of an element in a state over a period, rather than the occurrence of specific transitions triggered by any type of events. In this line, the work in [8] capitalises on [6] translating graph transformation systems into transition systems to be input to a model checker, restricting valid execution paths to time-ordered transformation sequences. In general, several techniques have been developed to derive inputs for model-checkers from the specification of system behaviour in the form of graph transformations (see [9] for a comparison of two general approaches to the development of these techniques).…”

Section: Related Workmentioning

confidence: 99%

Policy enforcement and verification with Timed Modeling Spider Diagrams

Bottoni

Fish

2013

2013 IEEE Symposium on Visual Languages and Human Centric Computing

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Abstract-Timed Modelling Spider Diagrams (TMSDs) are a visual language which supports the modeling of object-oriented systems with time constraints. They are used to define policies in which TMSDs specify admissible evolutions of the state of some instance. We define a process for deriving a rewriting system from a policy specification, so that the generated system defines a language of sequences of basic TMSDs satisfying the policy. Moreover, by identifying the different ways in which the constraints set by the policy can be violated, we can produce special rules whose application results in erroneous sequences. The resulting transformation systems can be used both to simulate possible behaviours when reasoning on the definition of policies and to test policy verification algorithms.

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