Metamodel Instance Generation: A systematic literature review

Modelling and thus metamodelling have become increasingly important in Software Engineering through the use of Model Driven Engineering. In this paper we present a systematic literature review of instance generation techniques for metamodels, i.e. the process of automatically generating models from a given metamodel. We start by presenting a set of research questions that our review is intended to answer. We then identify the main topics that are related to metamodel instance generation techniques, and use these to initiate our literature search. This search resulted in the identification of 34 key papers in the area, and each of these is reviewed here and discussed in detail. The outcome is that we are able to identify a knowledge gap in this field, and we offer suggestions as to some potential directions for future research.Comment: 25 page

Conflict Detection for Edits on Extended Feature Models using Symbolic Graph Transformation

Feature models are used to specify variability of user-configurable systems as appearing, e.g., in software product lines. Software product lines are supposed to be long-living and, therefore, have to continuously evolve over time to meet ever-changing requirements. Evolution imposes changes to feature models in terms of edit operations. Ensuring consistency of concurrent edits requires appropriate conflict detection techniques. However, recent approaches fail to handle crucial subtleties of extended feature models, namely constraints mixing feature-tree patterns with first-order logic formulas over non-Boolean feature attributes with potentially infinite value domains. In this paper, we propose a novel conflict detection approach based on symbolic graph transformation to facilitate concurrent edits on extended feature models. We describe extended feature models formally with symbolic graphs and edit operations with symbolic graph transformation rules combining graph patterns with first-order logic formulas. The approach is implemented by combining eMoflon with an SMT solver, and evaluated with respect to applicability.Comment: In Proceedings FMSPLE 2016, arXiv:1603.0857

Automated Generation of Unit Tests from UML Activity Diagrams using the AMPL Interface for Constraint Solvers

I, Felix Kurth, declare that I have authored this thesis independently, that I have not used other than the declared sources / resources, and that I have explicitly marked all material which has been quoted either literally or by content from the used sources. Neither this thesis nor any other similar work has been previously submitted to any examination board

Correct composition of dephased behavioural models

This research is supported by EPSRC grant EP/M014290/1.Scenarios of execution are commonly used to specify partial behaviour and interactions between different objects and components in a system. To avoid overall inconsistency in specifications, various automated methods have emerged in the literature to compose (behavioural) models. In recent work, we have shown how the theorem prover Isabelle can be combined with the constraint solver Z3 to efficiently detect inconsistencies in two or more behavioural models and, in their absence, generate the composition. Here, we extend our approach further and show how to generate the correct composition (as a set of valid traces) of dephased models. This work has been inspired by a problem from a medical domain where different care pathways (for chronic conditions) may be applied to the same patient with different starting points.Postprin

A formal approach to finding inconsistencies in a metamodel

Checking the consistency of a metamodel involves finding a valid metamodel instance that provably meets the set of constraints that are defined over the metamodel. These constraints are often specified in Object Constraint Language. Often, a metamodel is inconsistent due to conflicts among the constraints. Existing approaches and tools are typically incapable of pinpointing the conflicting constraints, and this makes it difficult for users to debug and fix their metamodels. In this paper, we present a formal approach for locating conflicting constraints in inconsistent metamodels. Our approach has four distinct features: (1) users can rank individual metamodel features using their own domain-specific knowledge, (2) we transform these ranked features to a weighted maximum satisfiability modulo theories problem and solve it to compute the set of maximum achievable features, (3) we pinpoint the conflicting constraints by solving the set cover problem using a novel algorithm, and (4) we have implemented our approach into a fully automated tool called MaxUSE. Our evaluation results, using our assembled set of benchmarks, demonstrate the scalability of our work and that it is capable of efficiently finding conflicting constraints

Modeling and Analysis of Software Product Line Variability in Clafer

Both feature and class modeling are used in Software Product Line (SPL) engineering to model variability. Feature models are used primarily to represent user-visible characteristics (i.e., features) of products; whereas class models are often used to model types of components and connectors in a product-line architecture. Previous works have explored the approach of using a single language to express both configurations of features and components. Their goal was to simplify the definition and analysis of feature-to-component mappings and to allow modeling component options as features. A prominent example of this approach is cardinality-based feature modeling, which extends feature models with multiple instantiation and references to express component-like, replicated features. Another example is to support feature modeling in a class modeling language, such as UML or MOF, using their profiling mechanisms and a stylized use of composition. Both examples have notable drawbacks: cardinality-based feature modeling lacks a constraint language and a well-defined semantics; encoding feature models as class models and their evolution bring extra complexity. This dissertation presents Clafer (class, feature, reference), a class modeling language with first-class support for feature modeling. Clafer can express rich structural models augmented with complex constraints, i.e., domain, variability, component models, and meta-models. Clafer supports: (i) class-based meta-models, (ii) object models (with uncertainty, if needed), (iii) feature models with attributes and multiple instantiation, (iv) configurations of feature models, (v) mixtures of meta- and feature models and model templates, and (vi) first-order logic constraints. Clafer also makes it possible to arrange models into multiple specialization and extension layers via constraints and inheritance. On the other hand, in designing Clafer we wanted to create a language that builds upon as few concepts as possible, and is easy to learn. The language is supported by tools for SPL verification and optimization. We propose to unify basic modeling constructs into a single concept, called clafer. In other words, Clafer is not a hybrid language. We identify several key mechanisms allowing a class modeling language to express feature models concisely. We provide Clafer with a formal semantics built in a novel, structurally explicit way. As Clafer subsumes cardinality-based feature modeling with attributes, references, and constraints, we are the first to precisely define semantics of such models. We also explore the notion of partial instantiation that allows for modeling with uncertainty and variability. We show that Object-Oriented Modeling (OOM) languages with no direct support for partial instances can support them via class modeling, using subclassing and strengthening multiplicity constraints. We make the encoding of partial instances via subclassing precise and general. Clafer uses this encoding and pushes the idea even further: it provides a syntactic unification of types and (partial) instances via subclassing and redefinition. We evaluate Clafer analytically and experimentally. The analytical evaluation shows that Clafer can concisely express feature and meta-models via a uniform syntax and unified semantics. The experimental evaluation shows that: 1) Clafer can express a variety of realistic rich structural models with complex constraints, such as variability models, meta-models, model templates, and domain models; and 2) that useful analyses can be performed within seconds

Extending Artemis With a Rule-Based Approach for Automatically Assessing Modeling Tasks

The Technische Universität Dresden has multiple e-learning projects in use. The Chair of Software Technology uses Inloop to teach students object-oriented programming through automatic feedback. In the last years, interest has grown in giving students automated feedback on modeling tasks. This is why there was an extension developed by Hamann to automate the assessment of modeling tasks in 2020. The TU Dresden currently has plans to replace Inloop with Artemis, a comparable system. Artemis currently supports the semi-automatic assessment of modeling exercises. In contrast, the system proposed by Hamann, called Inloom, is based on a rule-based approach and provides instant feedback. A rule-based system has certain advantages over a similarity-based system. One advantage is the mostly better feedback that these systems generate. To give instructors more flexibility and choice, this work tries to identify possible ways of extending Artemis with the rule-based approach Inloom. In the second step, this thesis will provide a proof of concept implementation. Furthermore, a comparison between different systems is developed to help instructors choose the best suitable system for their usecase.:Introduction, Background, Related Work, Analysis, System Design, Implementation, Evaluation, Conclusion and Future Work, Bibliography, Appendi

Formal verification of automotive embedded UML designs

Software applications are increasingly dominating safety critical domains. Safety critical domains are domains where the failure of any application could impact human lives. Software application safety has been overlooked for quite some time but more focus and attention is currently directed to this area due to the exponential growth of software embedded applications. Software systems have continuously faced challenges in managing complexity associated with functional growth, flexibility of systems so that they can be easily modified, scalability of solutions across several product lines, quality and reliability of systems, and finally the ability to detect defects early in design phases. AUTOSAR was established to develop open standards to address these challenges. ISO-26262, automotive functional safety standard, aims to ensure functional safety of automotive systems by providing requirements and processes to govern software lifecycle to ensure safety. Each functional system needs to be classified in terms of safety goals, risks and Automotive Safety Integrity Level (ASIL: A, B, C and D) with ASIL D denoting the most stringent safety level. As risk of the system increases, ASIL level increases and the standard mandates more stringent methods to ensure safety. ISO-26262 mandates that ASILs C and D classified systems utilize walkthrough, semi-formal verification, inspection, control flow analysis, data flow analysis, static code analysis and semantic code analysis techniques to verify software unit design and implementation. Ensuring software specification compliance via formal methods has remained an academic endeavor for quite some time. Several factors discourage formal methods adoption in the industry. One major factor is the complexity of using formal methods. Software specification compliance in automotive remains in the bulk heavily dependent on traceability matrix, human based reviews, and testing activities conducted on either actual production software level or simulation level. ISO26262 automotive safety standard recommends, although not strongly, using formal notations in automotive systems that exhibit high risk in case of failure yet the industry still heavily relies on semi-formal notations such as UML. The use of semi-formal notations makes specification compliance still heavily dependent on manual processes and testing efforts. In this research, we propose a framework where UML finite state machines are compiled into formal notations, specification requirements are mapped into formal model theorems and SAT/SMT solvers are utilized to validate implementation compliance to specification. The framework will allow semi-formal verification of AUTOSAR UML designs via an automated formal framework backbone. This semi-formal verification framework will allow automotive software to comply with ISO-26262 ASIL C and D unit design and implementation formal verification guideline. Semi-formal UML finite state machines are automatically compiled into formal notations based on Symbolic Analysis Laboratory formal notation. Requirements are captured in the UML design and compiled automatically into theorems. Model Checkers are run against the compiled formal model and theorems to detect counterexamples that violate the requirements in the UML model. Semi-formal verification of the design allows us to uncover issues that were previously detected in testing and production stages. The methodology is applied on several automotive systems to show how the framework automates the verification of UML based designs, the de-facto standard for automotive systems design, based on an implicit formal methodology while hiding the cons that discouraged the industry from using it. Additionally, the framework automates ISO-26262 system design verification guideline which would otherwise be verified via human error prone approaches