Supporting CRUD Model Operations from EOL to SQL

Model-based software development promises improvements in terms of quality and cost by raising the abstraction level of the development from code to models, but also requires mature techniques and tools. Although Eclipse Modelling Framework (EMF) introduces a default persistence mechanism for models, namely XMI, its usage is often limited as model size increases. To overcome this limitation, during the last years alternative persistence mechanisms have been proposed in order to store models in RDBMS and NoSQL databases. Under this new paradigm, model operations can be performed at model-level and persistence-level, e.g., mapping EOL model operations into SQL statements. In this paper, we extend our framework (called MQT) to support CRUD (Create, Read, Update and Delete) operations from model- (EOL) to persistence-level (SQL), using a streaming execution of queries at run-time. Through comparable evaluation metrics, we evaluate the performance and memory footprint of the framework usi ng the GraBaTs scenario

Evaluating embedded relational databases for large model persistence and query

Large models are increasingly used in Model Driven Development. Different studies have proved that XMI (default persistence in Eclipse Modelling Framework) has some limitations when operating with large models. To overcome them, recent approaches have used databases for the persistence of models. EDBM (Embedded DataBase for Models) is an approach for persisting models in an embedded relational database, providing scalable querying mechanism by runtime translation of modellevel queries to SQL. In this paper, we present an evaluation of EDBM in terms of scalability with existing approaches. GraBaTs 2009 case study (models from 8.8MB to 646MB) is used for evaluation. EDBM is 70% faster than the compared approaches to persist XMI GraBats models into databases and executes the GraBats query faster, as well as having a low memory usage. These results indicate that an embedded relational database, combined with an scalable query mechanism provides a promising alternative for persisting and querying large models

Model Query Translator. A Model-level Query Approach for Large-scale Models

Persisting and querying models larger than a few tens of megabytes using XMI introduces a significant time and memory footprint overhead to MDD workflows. In this paper, we present an approach that attempts to address this issue using an embedded relational database as an alternative persistence layer for EMF models, and runtime translation of OCL-like expressions for efficiently querying such models. We have performed an empirical study of the approach using a set of large-scale reverse engineered models and queries from the Grabats 2009 Reverse Engineering Contest. Main contribution of this paper is the Model Query Translator, an approach that translates (and executes) at runtime queries from model-level (EOL) to persistence-level (SQL)

On the support of multi-perspective process models variability for smart environments

Cloud service-based applications are to be adapted to serve multiple platforms and stakeholders. Atop of such services, Smart Green Buildings are fostering a plethora of processes within their sustainability life-cycle. This introduces a number of challenges, as how to support multiple perspectives of domain-specific variability and how to deal with large collections of related process variants. To tackle this, there is a need to handle multiperspective variability for processes. This paper introduces an approach to manage multi-perspective process variability by means of a meta-model and a modeling methodology, representing separately people and things variability perspectives in smart environments. Initial experimental results are also described, which indicate encouraging results for managing highly complex variability models

Model-Based Testing in Practice: An Industrial Case Study using GraphWalker

Model-based testing (MBT) is a test design technique that supports the automation of software testing processes and generates test artefacts based on a system model representing behavioural aspects of the system under test (SUT). Previous research has shown some positive aspects of MBT such as low-cost test case generation and fault detection effectiveness. However, it is still a challenge for both practitioners and researchers to evaluate MBT tools and techniques in real, industrial settings. Consequently, the empirical evidence regarding the mainstream use, including the modelling and test case generation using MBT tools, is limited. In this paper, we report the results of a case study on applying GraphWalker, an open-source tool for MBT, on an industrial cyber-physical system (i.e., a Train Control Management System developed by Bombardier Transportation in Sweden), from modelling of real-world requirements and test specifications to test case generation. We evaluate the models of the SUT for completeness and representativeness, compare MBT with manual test cases written by practitioners using multiple attributes as well as share our experiences of selecting and using GraphWalker for industrial application. The results show that a model of the SUT created using both requirements and test specifications provides better understanding of the SUT from testers’ perspective, making it more complete and representative than the model created based only on the requirements specification alone. The generated model-based test cases are longer in terms of the number of test steps, achieve better edge coverage and can cover requirements more frequently in different orders while achieving the same level of requirements coverage as manually created test cases

Genetic Algorithm-based Testing of Industrial Elevators under Passenger Uncertainty

Elevators, as other cyber-physical systems, need to deal with uncertainty during their operation due to several factors such as passengers and hardware. Such uncertainties could affect the quality of service promised by elevators and in the worst case lead to safety hazards. Thus, it is important that elevators are extensively tested by considering uncertainty during their development to ensure their safety in operation. To this end, we present an uncertainty testing methodology supported with a tool to test industrial dispatching systems at the Software-in-the-Loop (SiL) test level. In particular, we focus on uncertainties in passenger data and employ a Genetic Algorithm (GA) with specifically designed genetic operators to significantly reduce the quality of service of elevators, thus aiming to find uncertain situations that are difficult to extract by users. An initial experiment with an industrial dispatcher revealed that the GA significantly decreased the quality of service as compared to not considering uncertainties. The results can be used to further improve the implementation of dispatching algorithms to handle various uncertainties

Generating metamorphic relations for cyber-physical systems with genetic programming: an industrial case study

One of the major challenges in the verification of complex industrial Cyber-Physical Systems is the difficulty of determining whether a particular system output or behaviour is correct or not, the socalled test oracle problem. Metamorphic testing alleviates the oracle problem by reasoning on the relations that are expected to hold among multiple executions of the system under test, which are known as Metamorphic Relations (MRs). However, the development of effective MRs is often challenging and requires the involvement of domain experts. In this paper, we present a case study aiming at automating this process. To this end,we implemented GAssertMRs, a tool to automatically generate MRs with genetic programming. We assess the cost-effectiveness of this tool in the context of an industrial case study from the elevation domain. Our experimental results show that in most cases GAssertMRs outperforms the other baselines, including manually generated MRs developed with the help of domain experts. We then describe the lessons learned from our experiments and we outline the future work for the adoption of this technique by industrial practitioners

Evolutionary generation of metamorphic relations for cyber-physical systems

A problem when testing Cyber-Physical Systems (CPS) is the difficulty of determining whether a particular system output or behaviour is correct or not. Metamorphic testing alleviates such a problem by reasoning on the relations expected to hold among multiple executions of the system under test, which are known as Metamorphic Relations (MRs). However, the development of effective MRs is often challenging and requires the involvement of domain experts. This paper summarizes our recent publication: "Generating Metamorphic Relations for Cyber-Physical Systems with Genetic Programming: An Industrial Case Study", presented at ESEC/FSE 2021. In that publication we presented GAssertMRs, the first technique to automatically generate MRs for CPS, leveraging GP to explore the space of candidate solutions. We evaluated GAssertMRs in an industrial case study, outperforming other baselines

Industrial Cyber-Physical System Evolution Detection and Alert Generation

Industrial Cyber-Physical System (ICPS) monitoring is increasingly being used to make decisions that impact the operation of the industry. Industrial manufacturing environments such as production lines are dynamic and evolve over time due to new requirements (new customer needs, conformance to standards, maintenance, etc.) or due to the anomalies detected. When an evolution happens (e.g., new devices are introduced), monitoring systems must be aware of it in order to inform the user and to provide updated and reliable information. In this article, CALENDAR is presented, a software module for a monitoring system that addresses ICPS evolutions. The solution is based on a data metamodel that captures the structure of an ICPS in different timestamps. By comparing the data model in two subsequent timestamps, CALENDAR is able to detect and effectively classify the evolution of ICPSs at runtime to finally generate alerts about the detected evolution. In order to evaluate CALENDAR with different ICPS topologies (e.g., different ICPS sizes), a scalability test was performed considering the information captured from the production lines domain

Evolving Legacy Model Transformations to Aggregate Non Functional Requirements of the Domain

The use of Model Driven Development (MDD) is increasing in industry. When a Non Functional Requirement (NFR) not considered in the development must be added metamodels, models and also transformations are affected. Tasks for defining and maintaining model transformation rules can be complex in MDD. Model Transformation By Example (MTBE) approaches have been proposed to ease the development of transformation rules. In this paper an approach based on MTBE to derive the adaptation operations that must be implemented in a legacy model transformation when a NFR appears is presented. The approach derives semi-automatically the model transformations using execution traceability data and models differences. An example where access control property is integrated on a MDD system is introduced to demonstrate the usefulness of the tool to evolve model transformations