Interactive Strategy-Based Validation of Behavioral Models

When behavioral models are derived automatically based on observed stakeholder interactions, requirements engineers need to validate whether the stakeholders agree with the synthesized behavioral models. Allowing stakeholders to experience such models through simulation and animation allows them to comment on, amend to and correct these models. However, to ensure an efficient stakeholder validation, the simulation has to be guided instead of confronting the user with random situations over and over again. In this paper, we present a strategy-driven simulator capable of guiding the execution of behavioral models based on graph transformations. By analyzing either the overall structure of a partial state space (look ahead) or by performing an in-depth analysis of the states therein, the simulator is able to determine which transformations should be executed next to continue on the most promising path through the overall state space. The discussed implementation is illustrated with a case study

Model Based Analysis and Test Generation for Flight Software

We describe a framework for model-based analysis and test case generation in the context of a heterogeneous model-based development paradigm that uses and combines Math- Works and UML 2.0 models and the associated code generation tools. This paradigm poses novel challenges to analysis and test case generation that, to the best of our knowledge, have not been addressed before. The framework is based on a common intermediate representation for different modeling formalisms and leverages and extends model checking and symbolic execution tools for model analysis and test case generation, respectively. We discuss the application of our framework to software models for a NASA flight mission

Modelling and Analysis Using GROOVE

In this paper we present case studies that describe how the graph transformation tool GROOVE has been used to model problems from a wide variety of domains. These case studies highlight the wide applicability of GROOVE in particular, and of graph transformation in general. They also give concrete templates for using GROOVE in practice. Furthermore, we use the case studies to analyse the main strong and weak points of GROOVE

Tackling Complexity: Process Reconstruction and Graph Transformation for Financial Audits

A key objective of implementing business intelligence tools and methods is to analyze voluminous data and to derive information that would otherwise not be available. Although the overall significance of business intelligence has increased with the general growth of processed and available data it is almost absent in the auditing industry. Public accountants face the challenge to provide an opinion on financial statements that are based on the data produced by the automated processing of countless business transactions in ERP systems. Methods for mining and reconstructing financially relevant process instances can be used as a data analysis tool in the specific context of auditing. In this article we introduce and evaluate an algorithm that effectively reduces the complexity of mined process instances. The presented methods provide a part of the foundation for implementing automated analysis and audit procedures that can assist auditors to perform more efficient and effective audits

On the Complexity of Local Graph Transformations

We consider the problem of transforming a given graph G_s into a desired graph G_t by applying a minimum number of primitives from a particular set of local graph transformation primitives. These primitives are local in the sense that each node can apply them based on local knowledge and by affecting only its 1-neighborhood. Although the specific set of primitives we consider makes it possible to transform any (weakly) connected graph into any other (weakly) connected graph consisting of the same nodes, they cannot disconnect the graph or introduce new nodes into the graph, making them ideal in the context of supervised overlay network transformations. We prove that computing a minimum sequence of primitive applications (even centralized) for arbitrary G_s and G_t is NP-hard, which we conjecture to hold for any set of local graph transformation primitives satisfying the aforementioned properties. On the other hand, we show that this problem admits a polynomial time algorithm with a constant approximation ratio

From Graph Transformations to Differential Equations

In a variety of disciplines models are used to predict, measure or explain quantitative properties. Examples include the concentration of a chemical substance produced within a given period, the growth of the size of a population of individuals, the time taken to recover from a communication breakdown in a network, etc. The models such properties arise from are often discrete and structural in nature. Adding information on the time and/or probability of any actions performed, quantitative models can be derived. In the first example above, commonly referred to as kinetic analysis of chemical reactions, a system of differential equations describing the evolution of concentrations is extracted from specifications of individual chemical reactions augmented with reaction rates. Recently, this construction has inspired approaches based on stochastic process specification techniques aiming to extract a continuous, quantitative model of a system from a discrete, structural one. This paper describes a methodology for such an extraction based on stochastic graph transformations. The approach is based on a variant of the construction of critical pairs and has been implemented using the AGG tool and validated for a simple reaction of unimolecular nucleophilic substitution (SN1)

A PBPO+ Graph Rewriting Tutorial

We provide a tutorial introduction to the algebraic graph rewriting formalism PBPO+. We show how PBPO+ can be obtained by composing a few simple building blocks, and model the reduction rules for binary decision diagrams as an example. Along the way, we comment on how alternative design decisions lead to related formalisms in the literature, such as DPO. We close with a detailed comparison with Bauderon's double pullback approach.Comment: In Proceedings TERMGRAPH 2022, arXiv:2303.1421

Mutation-based Fault Localization of Deep Neural Networks

Deep neural networks (DNNs) are susceptible to bugs, just like other types of software systems. A significant uptick in using DNN, and its applications in wide-ranging areas, including safety-critical systems, warrant extensive research on software engineering tools for improving the reliability of DNN-based systems. One such tool that has gained significant attention in the recent years is DNN fault localization. This paper revisits mutation-based fault localization in the context of DNN models and proposes a novel technique, named deepmufl, applicable to a wide range of DNN models. We have implemented deepmufl and have evaluated its effectiveness using 109 bugs obtained from StackOverflow. Our results show that deepmufl detects 53/109 of the bugs by ranking the buggy layer in top-1 position, outperforming state-of-the-art static and dynamic DNN fault localization systems that are also designed to target the class of bugs supported by deepmufl. Moreover, we observed that we can halve the fault localization time for a pre-trained model using mutation selection, yet losing only 7.55% of the bugs localized in top-1 position.Comment: 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approach

A growing demand is witnessed in both industry and academia for employing Deep Learning (DL) in various domains to solve real-world problems. Deep Reinforcement Learning (DRL) is the application of DL in the domain of Reinforcement Learning (RL). Like any software systems, DRL applications can fail because of faults in their programs. In this paper, we present the first attempt to categorize faults occurring in DRL programs. We manually analyzed 761 artifacts of DRL programs (from Stack Overflow posts and GitHub issues) developed using well-known DRL frameworks (OpenAI Gym, Dopamine, Keras-rl, Tensorforce) and identified faults reported by developers/users. We labeled and taxonomized the identified faults through several rounds of discussions. The resulting taxonomy is validated using an online survey with 19 developers/researchers. To allow for the automatic detection of faults in DRL programs, we have defined a meta-model of DRL programs and developed DRLinter, a model-based fault detection approach that leverages static analysis and graph transformations. The execution flow of DRLinter consists in parsing a DRL program to generate a model conforming to our meta-model and applying detection rules on the model to identify faults occurrences. The effectiveness of DRLinter is evaluated using 15 synthetic DRLprograms in which we injected faults observed in the analyzed artifacts of the taxonomy. The results show that DRLinter can successfully detect faults in all synthetic faulty programs