In the Model-Driven Engineering (MDE) of complex systems, multiple models represent various systems' aspects. In practice, these models are often unconnected and specified using different modeling languages. Model view solutions can be employed to automatically combine such models. However, writing model view definitions is not trivial. When modeling languages are semantically distant and/or have a large number of concepts, it can quickly become difficult to manually identify the language elements to be selected, associated, or queried to build a model view. As a solution, this paper proposes an in-context Large Language Model (LLM)-based approach to assist engineers in writing model-view definitions. Notably, we rely on LLMs and Prompt Engineering techniques to automatically generate drafts of model-view definitions by providing as input only minimal information on the modeling languages to be combined. We implemented our approach by integrating the EMF Views solution for model views with the LangChain framework for LLM-based applications. To this end, we tailored LangChain to handle EMF metamodels. We validated our approach and implementation on a set of model views originally specified either in VPDL, the ViewPoint Definition Language of EMF Views, or as ATL model-to-model transformations. We compared these original model view definitions with the ones we automatically generated. The obtained results show the feasibility and applicability of our approach.

Behavioral models are important assets that must be thoroughly verified early in the design process. This can be achieved with manually-written test cases that embed carefully hand-picked domain-specific input data. However, such test cases may not always reach the desired level of quality, such as high coverage or being able to localize faults efficiently. Test amplification is an interesting emergent approach to improve a test suite by automatically generating new test cases out of existing manually-written ones. Yet, while ad-hoc test amplification solutions have been proposed for a few programming languages, no solution currently exists for amplifying the test cases of behavioral models.

Abstract: As the size of software and system models grows, scalability issues in the current generation of model management languages (e.g. transformation, validation) and their supporting tooling become more prominent. To address this challenge, execution engines of model management programs need to become more efficient in their use of system resources. This paper presents an approach for partial loading of large models that reside in graph-database-backed model repositories. This approach leverages sophisticated static analysis of model management programs and auto-generation of graph (Cypher) queries to load only relevant model elements instead of naively loading the entire models into memory. Our experimental evaluation shows that our approach enables model management programs to process larger models, faster, and with a reduced memory footprint compared to the state of the art.

To test a system efficiently, we need to know how good are the defined test cases and to localize detected faults in the system. Measuring test coverage can address both concerns as it is a popular metric for test quality evaluation and, at the same time, is the foundation of advanced fault localization techniques. However, for Domain-Specific Languages (DSLs), coverage metrics and associated tools are usually manually defined for each DSL representing costly, error-prone, and non-reusable work.

Formal reasoning on model transformation languages allows users to certify model transformations against contracts. CoqTL includes a specification of a transformation engine in the Coq interactive theorem prover. An executable engine can be automatically extracted from this specification. Transformation contracts are proved by the user against the CoqTL specification and guaranteed to hold on the transformation running on the extracted implementation of CoqTL. The design of the transformation engine specification in CoqTL aims at easing the certification step, but this requirement harms the execution performance of the extracted engine.

Reducing the energy consumption of a complex, especially cyber-physical, system is a cross-cutting concern through the system layers, and typically requires long feedback loops between experts in several engineering disciplines. Having an immediate automatic estimation of the global system consumption at design-time would significantly accelerate this process, but cross-layer tools are missing in several domains.

SUMMARY Generative software development has paved the way for the creation of multiple code generators that serve as a basis for automatically generating code to different software and hardware platforms. In this context, the software quality becomes highly correlated to the quality of code generators used during software development. Eventual failures may result in a loss of confidence for the developers, who will unlikely continue to use these generators. It is then crucial to verify the correct behaviour of code generators in order to preserve software quality and reliability. In this paper, we leverage the metamorphic testing approach to automatically detect inconsistencies in code generators via so‐called “metamorphic relations”. We define the metamorphic relation (i.e., test oracle) as a comparison between the variations of performance and resource usage of test suites running on different versions of generated code. We rely on statistical methods to find the threshold value from which an unexpected variation is detected. We evaluate our approach by testing a family of code generators with respect to resource usage and performance metrics for five different target software platforms. The experimental results show that our approach is able to detect, among 95 executed test suites, 11 performance and 15 memory usage inconsistencies.

Elasticity is one of the main features of cloud-based systems (CBSs), where elastic adaptations, such as those to deal with scaling in or scaling out of computational resources, help meet performance requirements under varying workload. There is an industrial need to find configurations of elastic adaptations and workload that could lead to degradation of performance in a CBS, serving possibly millions of users. However, the potentially great number of such configurations poses a challenge: executing and verifying all of them on the cloud can be prohibitively expensive in both, time and cost. We present an approach to model elasticity adaptation due to workload changes as a classification tree model and consequently generate short test sequences of configurations that cover all T-wise interactions between parameters in the model. These test sequences, when executed, help us assess the performance of elastic CBS. Using MongoDB as a case study, test sequences generated by our approach reveal several significant performance degradations.

Industrial use of Model Driven Engineering techniques has emphasized the need for efficiently store, access, and transform very large models. While scalable persistence frameworks, typically based on some kind of NoSQL database, have been proposed to solve the model storage issue, the same level of performance improvement has not been achieved for the model transformation problem. Existing model transformation tools (such as the well-known ATL) often require the input models to be loaded in memory prior to the start of the transformation and are not optimized to benefit from lazy-loading mechanisms, mainly due to their dependency on current low-level APIs offered by the most popular modeling frameworks nowadays. In this paper we present Gremlin-ATL, a scalable and efficient model-to-model transformation framework that translates ATL transformations into Gremlin, a query language supported by several NoSQL databases. With Gremlin-ATL, the transformation is computed within the database itself, bypassing the modeling framework limitations and improving its performance both in terms of execution time and memory consumption. Tool support is available online.

Abstract Authentication delegation is a major function of the modern web. Identity Providers (IdP) acquired a central role by providing this function to other web services. By knowing which web services or web applications access its service, an IdP can violate the enduser privacy by discovering information that the user did not want to share with its IdP. For instance, WebRTC introduces a new field of usage as authentication delegation happens during the call session establishment, between two users. As a result, an IdP can easily discover that Bob has a meeting with Alice. A second issue that increases the privacy violation is the lack of choice for the end-user to select its own IdP. Indeed, on many web-applications, the end-user can only select between a subset of IdPs, in most cases Facebook or Google. In this paper, we analyze this phenomena, in particular why the end-user cannot easily select its preferred IdP, though there exists standards in this field such as OpenID Connect and OAuth 2? To lead this analysis, we conduct three investigations. The first one is a field survey on OAuth 2 and OpenID Connect scope usage by web sites to understand if scopes requested by websites could allow for user defined IdPs. The second one tries to understand whether the problem comes from the OAuth 2 protocol or its implementations by IdP. The last one tries to understand if trust relations between websites and IdP could prevent the end user to select its own IdP. Finally, we sketch possible architecture for web browser based identity management, and report on the implementation of a prototype.

The intensive use of generative programming techniques provides an elegant engineering solution to deal with the heterogeneity of platforms and technological stacks. The use of domain-specific languages for example, leads to the creation of numerous code generators that automatically translate highlevel system specifications into multi-target executable code. Producing correct and efficient code generator is complex and error-prone. Although software designers provide generally high-level test suites to verify the functional outcome of generated code, it remains challenging and tedious to verify the behavior of produced code in terms of non-functional properties. This paper describes a practical approach based on a runtime monitoring infrastructure to automatically check the potential inefficient code generators. This infrastructure, based on system containers as execution platforms, allows code-generator developers to evaluate the generated code performance. We evaluate our approach by analyzing the performance of Haxe, a popular high-level programming language that involves a set of cross-platform code generators. Experimental results show that our approach is able to detect some performance inconsistencies that reveal real issues in Haxe code generators.

Early testing reduces the cost of detecting faults and improves the system reliability. In particular, testing component or service based systems during modeling frees the tests from implementation details, especially those related to the middleware. COSTOTest is a tool that helps the tester during the process of designing tests at the model level. It suggests the possibilities and the lacks when (s)he builds test cases. Building executable tests is achieved thanks to model transformations.

One of the main features of cloud computing is elasticity, where resource is (de-)allocated on demand and at system's runtime. Since elasticity is not trivial, testing cloud-based systems (CBS) is laborious. Among others, testers must set up elasticity parameters on cloud computing infrastructure, specify a sequence of resource variations, and drive CBS through this sequence. In this paper, we propose a Domain-Specific Language (DSL) aiming at reducing the tester's effort in writing and executing CBS elasticity testing. Our DSL abstracts test case specification from different cloud provider's libraries, making it portable. Experiments with two different case studies, a MongoDB replica set and a distributed web application, shows that our approach reduces the effort (in number of words) to write test cases, compared to dedicated libraries. We also see a reduced effort when running the same test case on multiple cloud providers.

Generally, compiler users apply different optimizations to generate efficient code with respect to non-functional properties such as energy consumption, execution time, etc. However, due to the huge number of optimizations provided by modern compilers, finding the best optimization sequence for a specific objective and a given program is more and more challenging. This paper proposes NOTICE, a component-based framework for non-functional testing of compilers through the monitoring of generated code in a controlled sand-boxing environment. We evaluate the effectiveness of our approach by verifying the optimizations performed by the GCC compiler. Our experimental results show that our approach is able to auto-tune compilers according to user requirements and construct optimizations that yield to better performance results than standard optimization levels. We also demonstrate that NOTICE can be used to automatically construct optimization levels that represent optimal trade-offs between multiple non-functional properties such as execution time and resource usage requirements.

While Model Driven Engineering is gaining more industrial interest, scalability issues when managing large models have become a major problem in current modeling frameworks. Scalable model persistence has been achieved by using NoSQL backends for model storage, but existing modeling framework APIs have not evolved accordingly, limiting NoSQL query performance benefits. In this paper we present the Mogwaï, a scalable and efficient model query framework based on a direct translation of OCL queries to Gremlin, a query language supported by several NoSQL databases. Generated Gremlin expressions are computed inside the database itself, bypassing limitations of existing framework APIs and improving overall performance, as confirmed by our experimental results showing an improvement of execution time up to a factor of 20 and a reduction of the memory overhead up to a factor of 75 for large models.

Nowadays the large-scale systems are common-place in any kind of applications. The popularity of the web created a new environment in which the applications need to be highly scalable due to the data tsunami generated by a huge load of requests (i.e., connections and business operations). In this context, the main question is to validate how far the web applications can deal with the load generated by the clients. Load testing is a technique to analyze the behavior of the system under test upon normal and heavy load conditions. In this work we present a peer-to-peer load testing approach to isolate bottleneck problems related to centralized testing drivers and to scale up the load. Our approach was tested in a DBMS as study case and presents satisfactory results.

Testing distributed systems is challenging. Peer-to-peer (P2P) systems are composed of a high number of concurrent nodes distributed across the network. The nodes are also highly volatile (i.e., free to join and leave the system at any time). In this kind of system, a great deal of control should be carried out by the test harness, including: volatility of nodes, test case deployment and coordination. In this demonstration we present the PeerUnit framework for testing P2P systems. The original characteristic of this framework is the individual control of nodes, allowing test cases to precisely control their volatility during execution. We validated this framework through implementation and experimentation on two popular open-source P2P systems.

Developing peer-to-peer (P2P) systems is hard because they must be deployed on a high number of nodes, which can be autonomous, refusing to answer to some requests or even unexpectedly leaving the system. Such volatility of nodes is a common behavior in P2P system and can be interpreted as fault during tests.In this paper, we propose a framework for testing P2P systems. This framework is based on the individual control of nodes, allowing test cases to precisely control the volatility of nodes during execution.We validated this framework through implementation and experimentation on an open-source P2P system.

Peer-to-peer (P2P) is becoming a key technology for software development, but still lacks integrated solutions to build trust in the final software, in terms of correctness and security. Testing such systems is difficult because of the high numbers of nodes which can be volatile. In this paper, we present a framework for testing volatility of P2P systems. The framework is based on the individual control of peers, allowing test cases to precisely control the volatility of peers during execution. We validated our framework through implementation and experimentation on two open-source P2P systems. Through experimentation, we analyze the behavior of both systems on different conditions of volatility and show how the framework is able to detect implementation problems.

In this article, we investigate the use of the OO computational paradigm for the formulation of knowledge model patterns as OO analysis patterns. We seek to take advantage of research on design pattern specification, aimed at modelling patterns by means of structural and behavioural "meta-level" constraints, introducing appropriate modifications into the UML. We illustrate our argument with the formulation of an OO "assessment pattern" in analogy to the well known "assessment task template".

Design-for-testability is a very important issue in software engineering. It becomes crucial in the case of OO designs where control flows are generally not hierarchical, but are diffuse and distributed over the whole architecture. We introduce the concept of a "testing conflict" when potentially concurrent client/supplier relationships between the same classes along different paths exist in a system. Such conflicts may be hard to test, especially when dynamic binding and polymorphism are involved. We describe the conflicts using topological class configuration diagrams. An overall architecture is represented as a combination of the initial design and several patterns. We focus on the design patterns as coherent subsets in the architecture, and we explain how their use can provide a way for limiting the complexity of testing for conflicts, and of confining their effects to the classes involved in the pattern.