Static Fault Localization in Model Transformations

As the complexity of model transformations grows, there is an increasing need to count on methods, mechanisms, and tools for checking their correctness, i.e., the alignment between specifications and implementations. In this paper we present a light-weight and static approach for locating the faulty rules in model transformations, based on matching functions that automatically establish these alignments using the metamodel footprints, i.e., the metamodel elements used. The approach is implemented for the combination of Tracts and ATL, both residing in the Eclipse Modeling Framework, and is supported by the corresponding toolkit. An evaluation discussing the accuracy and the limitations of the approach is also provided. Furthermore, we identify the kinds of transformations which are most suitable for validation with the proposed approach and use mutation techniques to evaluate its effectiveness.Ministerio de Ciencia e Innovación TIN2011-23795Austrian Research Promotion Agency (FFG) 832160European Commission ICT Policy Support Programme 31785

Spectrum-Based Fault Localization in Model Transformations

Model transformations play a cornerstone role in Model-Driven Engineering (MDE), as they provide the essential mechanisms for manipulating and transforming models. The correctness of software built using MDE techniques greatly relies on the correctness of model transformations. However, it is challenging and error prone to debug them, and the situation gets more critical as the size and complexity of model transformations grow, where manual debugging is no longer possible. Spectrum-Based Fault Localization (SBFL) uses the results of test cases and their corresponding code coverage information to estimate the likelihood of each program component (e.g., statements) of being faulty. In this article we present an approach to apply SBFL for locating the faulty rules in model transformations. We evaluate the feasibility and accuracy of the approach by comparing the effectiveness of 18 different stateof- the-art SBFL techniques at locating faults in model transformations. Evaluation results revealed that the best techniques, namely Kulcynski2, Mountford, Ochiai, and Zoltar, lead the debugger to inspect a maximum of three rules to locate the bug in around 74% of the cases. Furthermore, we compare our approach with a static approach for fault localization in model transformations, observing a clear superiority of the proposed SBFL-based method.Comisión Interministerial de Ciencia y Tecnología TIN2015-70560-RJunta de Andalucía P12-TIC-186

Systematic engineering of mutation operators

In the context of software engineering, mutation consists in injecting small changes in artefacts – like models, programs, or data – for purposes like (mutation) testing, test data generation, and all sorts of search-based methods. These tasks typically require defining sets of mutation operators, which are often built ad-hoc because there is currently poor support for their development and testing. To improve this situation, we propose a methodology and corresponding tool support for the proper engineering of mutation operators. Our proposal is model-based, representing the artefacts to be mutated as models. It includes a domain-specific language to describe the mutation operators, facilities to synthesize models that can be used to test the operators, different metrics to analyse operator coverage, and services to generate operators when the coverage is insufficient. We show automated support atop the WODEL tool, and illustrate its use by defining mutation operators for UML Class Diagrams.This work has been partially funded by the Spanish Ministry of Science (RTI2018-095255-B-I00), by the R&D programme of the Madrid Region (S2018/TCS-4314) and by the Spanish MINECO-FEDER (grant number FAME RTI2018-093608-BC31

Pattern-based multi-cloud architecture migration

Many organizations migrate on-premise software applications to the cloud. However, current coarse-grained cloud migration solutions have made such migrations a non transparent task, an endeavor based on trial-anderror. This paper presents Variability-based, Pattern-driven Architecture Migration .V-PAM), a migration method based on (i) a catalogue of fine-grained service-based cloud architecture migration patterns that target multi-cloud, (ii) a situational migration process framework to guide pattern selection and composition, and (iii) a variability model to structure system migration into a coherent framework. The proposed migration patterns are based on empirical evidence from several migration projects, best practice for cloud architectures and a systematic literature review of existing research. Variability-based, Pattern-driven Architecture Migration allows an organization to (i) select appropriate migration patterns, (ii) compose them to define a migration plan, and (iii) extend them based on the identification of new patterns in new contexts. The patterns are at the core of our solution, embedded into a process model, with their selection governed by a variability model

A Deductive Approach for Fault Localization in ATL Model Transformations

International audienceIn model-driven engineering, correct model transformation is essential for reliably producing the artifacts that drive software development. While the correctness of a model transformation can be specified and checked via contracts, debugging unverified contracts imposes a heavy cognitive load on transformation developers. To improve this situation, we present an automatic fault localization approach, based on natural deduction, for the ATL model transformation language. We start by designing sound natural deduction rules for the ATL language. Then, we propose an automated proof strategy that applies the designed deduction rules on the postconditions of the model transformation to generate sub-goals: successfully proving the sub-goals implies the satisfaction of the postconditions. When a sub-goal is not verified, we present the user with sliced ATL model transformation and predicates deduced from the postcondition as debugging clues. We provide an automated tool that implements this process. We evaluate its practical applicability using mutation analysis, and identify its limitations

CoqTL: A Coq DSL for Rule-Based Model Transformation

International audienc

Model Transformation Testing and Debugging: A Survey

Model transformations are the key technique in Model-Driven Engineering (MDE) to manipulate and construct models. As a consequence, the correctness of software systems built with MDE approaches relies mainly on the correctness of model transformations, and thus, detecting and locating bugs in model transformations have been popular research topics in recent years. This surge of work has led to a vast literature on model transformation testing and debugging, which makes it challenging to gain a comprehensive view of the current state of the art. This is an obstacle for newcomers to this topic and MDE practitioners to apply these approaches. This paper presents a survey on testing and debugging model transformations based on the analysis of \nPapers~papers on the topics. We explore the trends, advances, and evolution over the years, bringing together previously disparate streams of work and providing a comprehensive view of these thriving areas. In addition, we present a conceptual framework to understand and categorise the different proposals. Finally, we identify several open research challenges and propose specific action points for the model transformation community.This work is partially supported by the European Commission (FEDER) and Junta de Andalucia under projects APOLO (US-1264651) and EKIPMENT-PLUS (P18-FR-2895), by the Spanish Government (FEDER/Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación) under projects HORATIO (RTI2018-101204-B-C21), COSCA (PGC2018-094905-B-I00) and LOCOSS (PID2020-114615RB-I00), by the Austrian Science Fund (P 28519-N31, P 30525-N31), and by the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development (CDG

A Multi-Level Framework for the Detection, Prioritization and Testing of Software Design Defects

Large-scale software systems exhibit high complexity and become difficult to maintain. In fact, it has been reported that software cost dedicated to maintenance and evolution activities is more than 80% of the total software costs. In particular, object-oriented software systems need to follow some traditional design principles such as data abstraction, encapsulation, and modularity. However, some of these non-functional requirements can be violated by developers for many reasons such as inexperience with object-oriented design principles, deadline stress. This high cost of maintenance activities could potentially be greatly reduced by providing automatic or semi-automatic solutions to increase system‟s comprehensibility, adaptability and extensibility to avoid bad-practices. The detection of refactoring opportunities focuses on the detection of bad smells, also called antipatterns, which have been recognized as the design situations that may cause software failures indirectly. The correction of one bad smell may influence other bad smells. Thus, the order of fixing bad smells is important to reduce the effort and maximize the refactoring benefits. However, very few studies addressed the problem of finding the optimal sequence in which the refactoring opportunities, such as bad smells, should be ordered. Few other studies tried to prioritize refactoring opportunities based on the types of bad smells to determine their severity. However, the correction of severe bad smells may require a high effort which should be optimized and the relationships between the different bad smells are not considered during the prioritization process. The main goal of this research is to help software engineers to refactor large-scale systems with a minimum effort and few interactions including the detection, management and testing of refactoring opportunities. We report the results of an empirical study with an implementation of our bi-level approach. The obtained results provide evidence to support the claim that our proposal is more efficient, on average, than existing techniques based on a benchmark of 9 open source systems and 1 industrial project. We have also evaluated the relevance and usefulness of the proposed bi-level framework for software engineers to improve the quality of their systems and support the detection of transformation errors by generating efficient test cases.Ph.D.Information Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136075/1/Dilan_Sahin_Final Dissertation.pdfDescription of Dilan_Sahin_Final Dissertation.pdf : Dissertatio

From examples to knowledge in model-driven engineering : a holistic and pragmatic approach

Le Model-Driven Engineering (MDE) est une approche de développement logiciel qui propose d’élever le niveau d’abstraction des langages afin de déplacer l’effort de conception et de compréhension depuis le point de vue des programmeurs vers celui des décideurs du logiciel. Cependant, la manipulation de ces représentations abstraites, ou modèles, est devenue tellement complexe que les moyens traditionnels ne suffisent plus à automatiser les différentes tâches. De son côté, le Search-Based Software Engineering (SBSE) propose de reformuler l’automatisation des tâches du MDE comme des problèmes d’optimisation. Une fois reformulé, la résolution du problème sera effectuée par des algorithmes métaheuristiques. Face à la pléthore d’études sur le sujet, le pouvoir d’automatisation du SBSE n’est plus à démontrer. C’est en s’appuyant sur ce constat que la communauté du Example-Based MDE (EBMDE) a commencé à utiliser des exemples d’application pour alimenter la reformulation SBSE du problème d’apprentissage de tâche MDE. Dans ce contexte, la concordance de la sortie des solutions avec les exemples devient un baromètre efficace pour évaluer l’aptitude d’une solution à résoudre une tâche. Cette mesure a prouvé être un objectif sémantique de choix pour guider la recherche métaheuristique de solutions. Cependant, s’il est communément admis que la représentativité des exemples a un impact sur la généralisabilité des solutions, l'étude de cet impact souffre d’un manque de considération flagrant. Dans cette thèse, nous proposons une formulation globale du processus d'apprentissage dans un contexte MDE incluant une méthodologie complète pour caractériser et évaluer la relation qui existe entre la généralisabilité des solutions et deux propriétés importantes des exemples, leur taille et leur couverture. Nous effectuons l’analyse empirique de ces deux propriétés et nous proposons un plan détaillé pour une analyse plus approfondie du concept de représentativité, ou d’autres représentativités.Model-Driven Engineering (MDE) is a software development approach that proposes to raise the level of abstraction of languages in order to shift the design and understanding effort from a programmer point of view to the one of decision makers. However, the manipulation of these abstract representations, or models, has become so complex that traditional techniques are not enough to automate its inherent tasks. For its part, the Search-Based Software Engineering (SBSE) proposes to reformulate the automation of MDE tasks as optimization problems. Once reformulated, the problem will be solved by metaheuristic algorithms. With a plethora of studies on the subject, the power of automation of SBSE has been well established. Based on this observation, the Example-Based MDE community (EB-MDE) started using application examples to feed the reformulation into SBSE of the MDE task learning problem. In this context, the concordance of the output of the solutions with the examples becomes an effective barometer for evaluating the ability of a solution to solve a task. This measure has proved to be a semantic goal of choice to guide the metaheuristic search for solutions. However, while it is commonly accepted that the representativeness of the examples has an impact on the generalizability of the solutions, the study of this impact suffers from a flagrant lack of consideration. In this thesis, we propose a thorough formulation of the learning process in an MDE context including a complete methodology to characterize and evaluate the relation that exists between two important properties of the examples, their size and coverage, and the generalizability of the solutions. We perform an empirical analysis, and propose a detailed plan for further investigation of the concept of representativeness, or of other representativities

Formal transformation methods for automated fault tree generation from UML diagrams

With a growing complexity in safety critical systems, engaging Systems Engineering with System Safety Engineering as early as possible in the system life cycle becomes ever more important to ensure system safety during system development. Assessing the safety and reliability of system architectural design at the early stage of the system life cycle can bring value to system design by identifying safety issues earlier and maintaining safety traceability throughout the design phase. However, this is not a trivial task and can require upfront investment. Automated transformation from system architecture models to system safety and reliability models offers a potential solution. However, existing methods lack of formal basis. This can potentially lead to unreliable results. Without a formal basis, Fault Tree Analysis of a system, for example, even if performed concurrently with system design may not ensure all safety critical aspects of the design. [Continues.]</div