Graph Transformation Systems Research Articles

A higher-order transformation approach to the formalization and analysis of BPMN using graph transformation systems

The Business Process Modeling Notation (BPMN) is a widely used standard notation for defining intra- and inter-organizational workflows. However, the informal description of the BPMN execution semantics leads to different interpretations of BPMN elements and difficulties in checking behavioral properties. In this article, we propose a formalization of the execution semantics of BPMN that, compared to existing approaches, covers more BPMN elements while also facilitating property checking. Our approach is based on a higher-order transformation from BPMN models to graph transformation systems. To show the capabilities of our approach, we implemented it as an open-source web-based tool.

A Survey on Syntactic Pattern Recognition Methods in Bioinformatics

Formal tools and models of syntactic pattern recognition which are used in bioinformatics are introduced and characterized in the paper. They include, among others: stochastic (string) grammars and automata, hidden Markov models, programmed grammars, attributed grammars, stochastic tree grammars, Tree Adjoining Grammars (TAGs), algebraic dynamic programming, NLC- and NCE-type graph grammars, and algebraic graph transformation systems. The survey of applications of these formal tools and models in bioinformatics is presented.

Automatic program bug fixing by focusing on finding the shortest sequence of changes

Automatic bug repair as the last step in program repair has attracted a lot of research attention. Various ideas and techniques have been presented in this field. Recent bug fixing techniques use machine learning and graphs to generate fixes. Despite the promising results of recent approaches, maintaining high speed and accuracy as well as recording a wide range of errors may still be a problem. In this paper, a new approach is presented in the field of automatic bug fixing based on graphs and model checking. For this purpose, we have used the graph transformation and model checking system to create a sequence of edits and produce fixes. Then, using meta-heuristic algorithms, we have selected the best solution and fix from the generated solutions. We use the extracted graphs from the buggy JavaScript code and their corresponding bug-free ones. In evaluating the effectiveness of the proposed method, we implement it in GROOVE, which is a toolbox used to design and check graph transformation systems. Experimental results on identical dataset demonstrate that the proposed method outperforms other related methods in generating fixes. Also, this method covers a wider range of bugs compared to previous methods.

Bounded model checking for interval probabilistic timed graph transformation systems against properties of probabilistic metric temporal graph logic

Cyber-physical systems often encompass complex concurrent behavior with timing constraints and probabilistic failures on demand. The analysis whether such systems with probabilistic timed behavior adhere to a given specification is essential. The formalism of Interval Probabilistic Timed Graph Transformation Systems (IPTGTSs) is often a suitable choice to model cyber-physical systems because (a) its rule-based approach to graph transformation can capture a wide range of system's structure dynamics when the states of the system can be represented by graphs while (b) it employs interval specifications for probabilistic behavior as well as lower and upper bounds on delays of steps to support systems where precise probabilities and delays are not known or may change during the runtime of the system. Probabilistic Metric Temporal Graph Logic (PMTGL) has been introduced as a powerful specification language to express worst-case/best-case probabilistic timed requirements such as actor-based soft deadlines using (a) path properties relying on its Metric Temporal Graph Logic fragment to track individual graph elements and (b) an operator inherited from Probabilistic Timed Computation Tree Logic to express worst-case/best-case probabilistic requirements identifying worst-case/best-case resolutions of non-determinism. Bounded Model Checking (BMC) support for Probabilistic Timed Graph Transformation Systems (PTGTSs) w.r.t. properties specified using PMTGL has been already presented. However, for IPTGTSs no analysis support w.r.t. PMTGL properties has been developed for stating metric temporal properties on identified subgraphs and their structural changes over time.In this paper, we adapt the BMC approach developed for PTGTSs to the case of IPTGTSs extending modeling and analysis support to the usage of probability intervals more appropriately covering cyber-physical systems where probabilistic effects cannot be specified precisely and need to be approximated instead. In our evaluation, we apply an implementation of our BMC approach in AutoGraph to a novel running example demonstrating the effect of using probability intervals instead of precise probability values.

Compositional Analysis of Probabilistic Timed Graph Transformation Systems

The analysis of behavioral models is of high importance for cyber-physical systems, as the systems often encompass complex behavior based on, e.g., concurrent components with mutual exclusion or probabilistic failures on demand. The rule-based formalism of Probabilistic Timed Graph Transformation Systems (PTGTSs) is a suitable choice when the models representing states of the system can be understood as graphs and timed and probabilistic behavior is important. However, model checking PTGTSs is limited to systems with rather small state spaces. We present an approach for the analysis of large-scale systems modeled as PTGTSs by systematically decomposing their state spaces into manageable fragments. To obtain qualitative and quantitative analysis results for a large-scale system, we verify that results obtained for its fragments serve as overapproximations for the corresponding results of the large-scale system. Hence, our approach allows for the detection of violations of qualitative and quantitative safety properties for the large-scale system under analysis. We consider a running example in which shuttles drive on tracks of a large-scale topology and autonomously coordinate their local behavior with other shuttles nearby. For this running example, we verify that (a) shuttles can always make the expected forward progress using several properties, (b) shuttles never collide, and (c) shuttles are unlikely to execute emergency brakes in two scenarios. In our evaluation, we apply an implementation of our approach in the tool AutoGraph to our running example.

An Incremental Optimization Algorithm for Efficient Verification of Graph Transformation Systems

An Incremental Optimization Algorithm for Efficient Verification of Graph Transformation Systems

A Foundation for Functional Graph Programs: The Graph Transformation Control Algebra (GTA)

Applications of graph transformation (GT) systems often require control structures that can be used to direct GT processes. Most existing GT tools follow a stateful computational model, where a single graph is repeatedly modified "in-place" when GT rules are applied. The implementation of control structures in such tools is not trivial. Common challenges include dealing with the non-determinism inherent to rule application and transactional constraints when executing compositions of GTs, in particular atomicity and isolation. The complexity of associated transaction mechanisms and rule application search algorithms (e.g., backtracking) complicates the definition of a formal foundation for these control structures. Compared to these stateful approaches, functional graph rewriting presents a simpler (stateless) computational model, which simplifies the definition of a formal basis for (functional) GT control structures. In this paper, we propose the "Graph Transformation control Algebra" (GTA) as such a foundation. The GTA has been used as the formal basis for implementing the control structures in the (functional) GT tool "GrapeVine".

Towards Mechanised Proofs in Double-Pushout Graph Transformation

We formalise the basics of the double-pushout approach to graph transformation in the proof assistant Isabelle/HOL and provide associated machine-checked proofs. Specifically, we formalise graphs, graph morphisms and rules, and a definition of direct derivations based on deletion and gluing. We then formalise graph pushouts and prove with Isabelle's help that both deletions and gluings are pushouts. We also prove that pushouts are unique up to isomorphism. The formalisation comprises around 2000 lines of source text. Our motivation is to pave the way for rigorous, machine-checked proofs in the theory of the double-pushout approach, and to lay the foundations for verifying graph transformation systems and rule-based graph programs by interactive theorem proving.

Resilience of Well-structured Graph Transformation Systems

Resilience is a concept of rising interest in computer science and software engineering. For systems in which correctness w.r.t. a safety condition is unachievable, fast recovery is demanded. We investigate resilience problems of graph transformation systems. Our main contribution is the decidability of two resilience problems for well-structured graph transformation systems (with strong compatibility). We prove our results in the abstract framework of well-structured transition systems and apply them to graph transformation systems, incorporating also the concept of adverse conditions.

Stochastic Graph Transformation For Social Network Modeling

Adaptive networks model social, physical, technical, or biological systems as attributed graphs evolving at the level of both their topology and data. They are naturally described by graph transformation, but the majority of authors take an approach inspired by the physical sciences, combining an informal description of the operations with programmed simulations, and systems of ODEs as the only abstract mathematical description. We show that we can capture a range of social network models, the so-called voter models, as stochastic attributed graph transformation systems, demonstrate the benefits of this representation and establish its relation to the non-standard probabilistic view adopted in the literature. We use the theory and tools of graph transformation to analyze and simulate the models and propose a new variant of a standard stochastic simulation algorithm to recreate the results observed.

Infinite-state graph transformation systems under adverse conditions

Abstract We present an approach for modeling adverse conditions by graph transformation systems. To this end, we introduce joint graph transformation systems which involve a system, an interfering environment, and an automaton modeling their interaction. For joint graph transformation systems, we present notions of correctness under adverse conditions. Some instances of correctness are expressible in LTL (linear temporal logic), or in CTL (computation tree logic), respectively. In these cases, verification of joint graph transformation systems is reduced to temporal model checking. To handle infinite state spaces, we incorporate the concept of well-structuredness. We discuss ideas for the verification of joint graph transformation systems using results based on well-structuredness.

A tale of two graph models: a case study in wireless sensor networks

Designing and reasoning about complex systems such as wireless sensor networks is hard due to highly dynamic environments: sensors are heterogeneous, battery-powered, and mobile. While formal modelling can provide rigorous mechanisms for design/reasoning, they are often viewed as difficult to use. Graph rewrite-based \tmodelling techniques increase usability by providing an intuitive, flexible, and diagrammatic form of modelling in which graph-like structures express relationships between entities while rewriting mechanisms allow model evolution. Two major graph-based formalisms are Graph Transformation Systems (GTS) and Bigraphical Reactive Systems (BRS). While both use similar underlying structures, how they are employed in modelling is quite different. To gain a deeper understanding of GTS and BRS, and to guide future modelling, theory, and tool development, in this experience report we compare the practical modelling abilities and style of GTS and BRS when applied to topology control in WSNs. To show the value of the models, we describe how analysis may be performed in both formalisms. A comparison of the approaches shows that although the two formalisms are different, from both a theoretical and practical modelling standpoint, they are each successful in modelling topology control in WSNs. We found that GTS, while featuring a small set of entities and transformation rules, relied on entity attributes, rule application based on attribute/variable side-conditions, and imperative control flow units. BRS on the other hand, required a larger number of entities in order to both encode attributes directly in the model (via nesting) and provide tagging functionality that, when coupled with rule priorities, implements control flow. There remains promising research mapping techniques between the formalisms to further enable flexible and expressive modelling.

Formal testing of timed graph transformation systems using metric temporal graph logic

Embedded real-time systems generate state sequences where time elapses between state changes. Ensuring that such systems adhere to a provided specification of admissible or desired behavior is essential. Formal model-based testing is often a suitable cost-effective approach. We introduce an extended version of the formalism of symbolic graphs, which encompasses types as well as attributes, for representing states of dynamic systems. Relying on this extension of symbolic graphs, we present a novel formalism of timed graph transformation systems (TGTSs) that supports the model-based development of dynamic real-time systems at an abstract level where possible state changes and delays are specified by graph transformation rules. We then introduce an extended form of the metric temporal graph logic (MTGL) with increased expressiveness to improve the applicability of MTGL for the specification of timed graph sequences generated by a TGTS. Based on the metric temporal operators of MTGL and its built-in graph binding mechanics, we express properties on the structure and attributes of graphs as well as on the occurrence of graphs over time that are related by their inner structure. We provide formal support for checking whether a single generated timed graph sequence adheres to a provided MTGL specification. Relying on this logical foundation, we develop a testing framework for TGTSs that are specified using MTGL. Lastly, we apply this testing framework to a running example by using our prototypical implementation in the tool AutoGraph.

Using knowledge discovery to propose a two-phase model checking for safety analysis of graph transformations

Safety is one of the most important features of modern software systems, especially safety-critical systems such as nuclear power plants, which can be checked exactly by model checking. Model checking is a formal verification technique that analyzes system properties through exploring all reachable states (state space) of a model of a system. The problem of the technique is that it confronts the state space explosion in large and complex systems due to exponential memory usage. Recent researches show that a partial and intelligent exploration of the state space can be a suitable solution to overcome this problem. In this paper, we propose a two-phase model checking for safety analysis of systems specified formally through graph transformations. In the first phase, the beam-search algorithm explores the state space to a specific number of states. In case of failure of the phase, the second phase starts: in systems specified through graph transformations, the rule applied on the previous state can determine the rule that can perform on the next state. In other words, the rule on current state depends on only the rule applied to previous state, not the one on earlier states. Hence, a Markov chain (MC) is estimated to capture dependencies between the sequence of applied rules in the state space explored by the beam-search algorithm. The MC is then employed to explore the remainder of the state space intelligently. To evaluate the effectiveness of the two-phase model checking, we implement it in GROOVE, an open source toolset for designing and model checking graph transformation systems. Experimental results show that the two-phase model checking has the high speed and accuracy in comparison with the existing meta-heuristic and evolutionary techniques.

Describing correct UAVs cooperation architectures applied on an anti-terrorism scenario

In this paper, we propose a solution supporting the correct description of UAVs cooperation architectures and their reconfigurations. This solution is based on a graph transformation system that defines rules for changing UAVs cooperation architecture while being in conformance to an architectural style. An architecture instance is considered correct, if its corresponding graph can be generated by a sequence of rules. To be illustrated, our approach is applied on the SuperSenS project where UAVs have to cooperate during search-and-rescue mission. The UAVs system is used for the surveillance of closed military zones aiming at detecting terrorists and tracking them.

A structural approach to graph transformation based on symmetric Petri nets

Graph Transformation Systems (GTS) and Petri Nets (PN) are two central, theoretically sound, formal models for concurrent or distributed systems. A lot of papers have focused on the relationship between GTS and PN. It is generally accepted that PN are instances of GTS due to the lack of ability to adapt or reconfigure their structure. In this paper, which extends a recent one, we reverse this perspective by presenting a formal definition of GTS in terms of Symmetric Nets (SN). SN are a standard High-Level PN formalism featuring a compact syntax which highlights behavioral symmetries of systems. The major strength lies in the possibility of using well-established tools to edit/efficiently analyze (SN-based) graph transformation models. We follow a structural approach, based on a newly implemented symbolic calculus, to validate Graph Transformation Rules (GTR) and formally verify GTS properties.In the second part of the paper, we supply a semantic characterization of the SN-based graph transformation model, by carrying out a thorough comparison with the classical, algebraic double-pushout (DPO) approach. We constructively and formally show how to translate any DPO rule to a corresponding, elementary or composite, SN rule. We treat both injective and non-injective DPO rules/rule matches. The comparison shows that the SN approach is, in some sense, a generalization of the DPO.

Modeling Adverse Conditions in the Framework of Graph Transformation Systems

The concept of adverse conditions addresses systems interacting with an adversary environment and finds use also in the development of new technologies. We present an approach for modeling adverse conditions by graph transformation systems. In contrast to other approaches for graph-transformational interacting systems, the presented main constructs are graph transformation systems. We introduce joint graph transformation systems which involve a system, an interfering environment, and an automaton modeling their interaction. For joint graph transformation systems, we introduce notions of (partial) correctness under adverse conditions, which contain the correctness of the system and a recovery condition. As main result, we show that two instances of correctness, namely k-step correctness (recovery in at most k steps after an environment intervention) and last-minute correctness (recovery until next environment intervention) are expressible in LTL (linear temporal logic), and that a weaker notion of k-step correctness is expressible in CTL (computation tree logic).

Using memetic algorithm for robustness testing of contract-based software models

Graph Transformation System (GTS) can formally specify the behavioral aspects of complex systems through graph-based contracts. Test suite generation under normal conditions from GTS specifications is a task well-suited to evolutionary algorithms such as Genetic and Particle Swarm Optimization (PSO) metaheuristics. However, testing the vulnerabilities of a system under unexpected events such as invalid inputs is essential. Furthermore, the mentioned global search algorithms tend to make big jumps in the system’s state-space that are not concentrated on particular test goals. In this paper, we extend the HGAPSO approach into a cost-aware Memetic Algorithm (MA) by making small local changes through a proposed local search operator to optimize coverage score and testing costs. Moreover, we test GTS specifications not only under normal events but also under unexpected situations. So, three coverage-based testing strategies are investigated, including normal testing, robustness testing, and a hybrid strategy. The effectiveness of the proposed test generation algorithm and the testing strategies are evaluated through a type of mutation analysis at the model-level. Our experimental results show that (1) the hybrid testing strategy outperforms normal and robustness testing strategies in terms of fault-detection capability, (2) the robustness testing is the most cost-efficient strategy, and (3) the proposed MA with the hybrid testing strategy outperforms the state-of-the-art global search algorithms.

Using search-based techniques for testing executable software models specified through graph transformations

Design by contract is a software development methodology that uses contracts for defining interfaces among interacting components of a software system. Graph transformation system is used to specify the behavioral aspects of software components by defining the pre- and post-conditions of methods as contracts. In this paper, we focus on testing executable software models specified by a graph transformation system. A set of model-specific coverage criteria and a cost-aware search-based test generation approach are introduced. To evaluate the effectiveness of the proposed coverage criteria and the test generation approach, a type of mutation analysis is presented at the model level. Furthermore, a couple of fault-detection methods are used to assess the quality of the generated tests in the model-level mutation analysis. The proposed approach is implemented in GROOVE, a toolset for model checking graph transformation systems. The empirical results based on some well-known case studies demonstrate the efficiency and scalability of each proposed coverage criterion and testing approach. The comparison of the proposed test generation approach with state-of-the-art techniques indicates a significant improvement in terms of fault-detection capability and testing costs.

A novel approach to solve AI planning problems in graph transformations

The aim of AI planning is to solve the problems with no exact solution available. These problems usually have a big search space, and planning may not find plans with the least actions and in the shortest time. Recent researches show that using suitable heuristics can help to find desired plans. In planning problems specified formally through graph transformation system (GTS), there are dependencies between applied rules (actions) in the search space. This fact motivates us to solve the planning problem for a small goal (instead of the main goal), extract dependencies from the searched space, and use these dependencies to solve the planning problem for the main goal. In GTS based systems, the nodes of a state (really is a graph) can be grouped due to their type. To create a small (refined) goal, we use a refinement technique to remove the predefined percent of nodes from each group of the main goal. Bayesian Optimization Algorithm (BOA) is then used to solve the planning problem for the refined goal. BOA is an Estimation of Distribution Algorithm (EDA) in which Bayesian networks are used to evolve the solution populations. Actually, a Bayesian network is learned from the current population, and then this network is employed to generate the next population. Since the last Bayesian network learned in BOA has the knowledge about dependencies between applied rules, this network can be used to solve the planning problem for the main goal. Experimental results on four well-known planning domains confirm that the proposed approach finds plans with the least actions and in the lower time compared with the state-of-the-art approaches.