Abstract The ever-growing need for customization creates a need to maintain software systems in many different variants. To avoid having to maintain different copies of the same model, developers of modeling languages and tools have recently started to provide implementation techniques for such variant-rich systems, notably variability mechanisms, which support implementing the differences between model variants. Available mechanisms either follow the annotative or the compositional paradigm, each of which have dedicated benefits and drawbacks. Currently, language and tool designers select the used variability mechanism often solely based on intuition. A better empirical understanding of the comprehension of variability mechanisms would help them in improving support for effective modeling. In this article, we present an empirical assessment of annotative and compositional variability mechanisms for three popular types of models. We report and discuss findings from a family of three experiments with 164 participants in total, in which we studied the impact of different variability mechanisms during model comprehension tasks. We experimented with three model types commonly found in modeling languages: class diagrams, state machine diagrams, and activity diagrams. We find that, in two out of three experiments, annotative technique lead to better developer performance. Use of the compositional mechanism correlated with impaired performance. For all three considered tasks, the annotative mechanism was preferred over the compositional one in all experiments. We present actionable recommendations concerning support of flexible, tasks-specific solutions, and the transfer of established best practices from the code domain to models.

Abstract In the field of Model-Driven Engineering, Triple Graph Grammars (TGGs) play an important role as a rule-based means of implementing consistency management. From a declarative specification of a consistency relation, several operations including forward and backward transformations, (concurrent) synchronisation, and consistency checks can be automatically derived. For TGGs to be applicable in realistic application scenarios, expressiveness in terms of supported language features is very important. A TGG tool is schema compliant if it can take domain constraints, such as multiplicity constraints in a meta-model, into account when performing consistency management tasks. To guarantee schema compliance, most TGG tools allow application conditions to be attached as necessary to relevant rules. This strategy is problematic for at least two reasons: First, ensuring compliance to a sufficiently expressive schema for all previously mentioned derived operations is still an open challenge; to the best of our knowledge, all existing TGG tools only support a very restricted subset of application conditions. Second, it is conceptually demanding for the user to indirectly specify domain constraints as application conditions, especially because this has to be completely revisited every time the TGG or domain constraint is changed. While domain constraints can in theory be automatically transformed to obtain the required set of application conditions, this has only been successfully transferred to TGGs for a very limited subset of domain constraints. To address these limitations, this paper proposes a search-based strategy for achieving schema compliance. We show that all correctness and completeness properties, previously proven in a setting without domain constraints, still hold when schema compliance is to be additionally guaranteed. An implementation and experimental evaluation are provided to support our claim of practical applicability.

Triple Graph Grammars (TGGs) are a declarative and rule-based approach to bidirectional model transformation. The key feature of TGGs is the automatic derivation of various operations such as unidirectional transformation, model synchronisation, and consistency checking. Application conditions can be used to increase the expressiveness of TGGs by guaranteeing schema compliance, i.e., that domain constraints are respected by the TGG. In recent years, a series of new TGG-based operations has been introduced leveraging Integer Linear Programming (ILP) solvers to flexible consistency maintenance even in cases where no strict solution exists. Schema compliance is not guaranteed, however, as application conditions from the original TGG cannot be directly transferred to these ILP-based operations. In this paper, we extend ILP-based TGG operations so as to guarantee schema compliance. We implement and evaluate the practical feasibility of our approach.

Owing to the ever-growing need for customization, software systems often exist in many different variants. To avoid the need to maintain many different copies of the same model, developers of modeling languages and tools have recently started to provide representations for such variant-rich systems, notably variability mechanisms that support the implementation of differences between model variants. Available mechanisms either follow the annotative or the compositional paradigm, each of them having unique benefits and drawbacks. Language and tool designers select the used variability mechanism often solely based on intuition. A better empirical understanding of the comprehension of variability mechanisms would help them in improving support for effective modeling.

Solving scheduling problems is important for a wide range of application domains including home care in the health care domain, allocation engineering in the automotive domain, and virtual network embedding in the network virtualisation domain. Standard solution approaches assume that an initially given problem definition (e.g. a set of constraints and an objective function) can be fixed, and does not have to be constantly changed and validated by domain experts. In this paper, we investigate an application where this is not the case: at dSPACE GmbH, a developer of software and hardware for mechatronic control systems, recurring manual tests must be executed in every development and release cycle. To allocate human resources (developers and testers) to perform these tests, a test schedule must be created and maintained during the testing process. Prior to our work, test scheduling at dSPACE was performed manually by a test manager, requiring more than one working day to create the initial schedule, and several hours of tedious, error-prone work every week to maintain the schedule. The novel challenge here is that an acceptable automation must be highly configurable by the test manager (the domain expert), who should be able to easily adapt and validate the problem definition on a regular basis. We demonstrate that techniques and results from consistency maintenance via triple graph grammars, and constraint solving via linear programming can be synergetically combined to yield a highly configurable and fully automated approach to test schedule generation. We evaluate our solution at dSPACE and show that it not only reduces the effort required to create and maintain schedules of acceptable quality, but that it can also be understood, configured, and validated by the test manager.

In collaboration scenarios, we often encounter situations in which semantically interrelated models are changed concurrently. Concurrent model synchronization denotes the task of keeping these models consistent by propagating changes between them. This is challenging as changes can contradict each other and thus be in conflict. A problem with current synchronisation approaches is that they are often nondeterministic, i.e., the order in which changes are propagated is essential for the result. Furthermore, a common limitation is that the involved models must have been in a consistent state at some point, and that the applied changes are at least valid for the domain in which they were made. We propose a hybrid approach based on Triple Graph Grammars (TGGs) and Integer Linear Programming (ILP) to overcome these issues: TGGs are a grammar-based means that supplies us with a superset of possible synchronization solutions, forming a search space from which an optimum solution incorporating user-defined preferences can be chosen by ILP. Therefore, the proposed method combines configurability by comprising expert knowledge via TGGs with the flexible input handling of search-based techniques: By accepting arbitrary graph structures as input models, the approach is tolerant towards errors induced during the modelling process, i.e., it can cope with input models which do not conform to their metamodel or which cannot be generated by the TGG at hand. The approach is implemented in the model transformation tool eMoflon and evaluated regarding scalability for growing model sizes and an increasing number of changes.

Traceability is crucial for many activities in software and systems engineering including monitoring the development progress, and proving compliance with standards. In practice, the use and maintenance of trace links are challenging as artifacts undergo constant change, and development takes place in distributed scenarios with multiple collaborating stakeholders. Although traceability management in general has been addressed in previous studies, there is a need for empirical insights into the collaborative aspects of traceability management and how it is situated in existing development contexts. The study reported in this paper aims to close this gap by investigating the relation of collaboration and traceability management, based on an understanding of characteristics of the development effort. In our multiple exploratory case study, we conducted semi-structured interviews with 24 individuals from 15 industrial projects. We explored which challenges arise, how traceability management can support collaboration, how collaboration relates to traceability management approaches, and what characteristics of the development effort influence traceability management and collaboration. We found that practitioners struggle with the following challenges: (1) collaboration across team and tool boundaries, (2) conveying the benefits of traceability, and (3) traceability maintenance. If these challenges are addressed, we found that traceability can facilitate communication and knowledge management in distributed contexts. Moreover, there exist multiple approaches to traceability management with diverse collaboration approaches, i.e., requirements-centered, developer-driven, and mixed approaches. While traceability can be leveraged in software development with both agile and plan-driven paradigms, a certain level of rigor is needed to realize its benefits and overcome challenges. To support practitioners, we provide principles of collaborative traceability management. The main contribution of this paper is empirical evidence of how culture, processes, and organization impact traceability management and collaboration, and principles to support practitioners with collaborative traceability management. We show that collaboration and traceability management have the potential to be mutually beneficial—when investing in one, also the other one is positively affected.

Consistency management is an important task in the context of Domain-Specific Language (DSL) development. It involves operations such as program (model) transformation, synchronisation, integration, and consistency checking, which are all tasks required to enable concurrent engineering using multiple DSLs. Even though consistency management is a well-researched topic, existing approaches either implement a fixed strategy for each consistency management operation, or do not scale for large models. This has been criticised in the literature, as practical applications require not only reasonable scalability with model size, but also unite multiple consistency management tasks within one tool. To raise the adaptability of such a tool to an appropriate level, a uniform way of performing these tasks is a desirable goal. In this paper, we propose an approach to consistency management that leverages a synergetic combination of Triple Graph Grammars and Integer Linear Programming. By modelling consistency management as an optimisation problem with a configurable objective function, we are able to uniformly address a wide range of consistency management operations. We show that our approach scales acceptably in practice, while still guaranteeing that a consistent solution is found if and only if one exists.

Modern User Interfaces (UIs) are increasingly expected to be plastic, in the sense that they retain a constant level of usability, even when subjected to context (platform, user, and environment) changes at runtime. Adaptive UIs have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. However, evaluating end-user satisfaction of adaptive UIs is a challenging task, because the UI and the context-of-use are both constantly changing. Thus, an acceptance analysis of UI adaptation features should consider the context-of-use when adaptations are triggered. Classical usability evaluation methods like usability tests mostly focus on a posteriori analysis techniques and do not fully exploit the potential of collecting implicit and explicit user feedback at runtime. To address this challenge, we present an on-the-fly usability testing solution that combines continuous context monitoring together with collection of instant user feedback to assess end-user satisfaction of UI adaptation features. The solution was applied to a mobile Android mail application, which served as basis for a usability study with 23 participants. A data-driven end-user satisfaction analysis based on the collected context information and user feedback was conducted. The main results show that most of the triggered UI adaptation features were positively rated.

Formalising requirements has the potential to solve problems arising from deficiencies in natural language descriptions. While behavioural requirements are rarely described formally in industry, increasing complexity and new safety standards have renewed the interest in formal specifications. The goal of this paper is to explore how behavioural requirements for embedded systems can be formalised and aligned with verification tasks. Over the course of a 2.5-year project with industry, we modelled existing requirements from a safety-critical automotive software function in several iterations. Taking practical limitations and stakeholder preferences into account, we explored the use of models on different abstraction levels. The final model was used to generate test cases and was evaluated in three interviews with relevant industry practitioners. We conclude that models on a high level of abstraction are most suitable for industrial requirements engineering, especially when they need to be interpreted by other stakeholders.

Traceability is an important concern for numerous software engineering activities. Establishing traceability links is a challenging and cost-intensive task, which is uneconomical without suitable strategies for maintaining high link quality. Current approaches to Traceability Management (TM), however, often make important assumptions and choices without ensuring that the consequences and implications for traceability maintenance are feasible and desirable in practice.

Traceability and trace link management are important for various reasons, including managing knowledge about a complex software system, monitoring the progress of its development, and proving that it is developed in accordance to regulations. However, it is difficult to maintain and use trace links in real-world projects where artifacts undergo constant change and multiple stakeholders are involved. In this paper, we extend the current body of knowledge on traceability management by regarding its collaborative aspects in an industrial setting. Based on 15 industrial cases and semi-structured interviews with 24 practitioners, we identify challenges involved in collaborative traceability management, and how traceability management can be used to enable collaboration. Our findings show that main challenges are boundaries between organizations and tools, a lack of common goals and responsibilities, and the difficulty of collaboratively maintaining trace links. We also identify traceability as an important facilitator for communication and knowledge management across these boundaries.

Domain Specific Languages (DSLs) are an established means of reducing the gap between problem and solution domains. DSLs increase productivity and improve quality as they can be tailored to exactly fit the needs of the problem to be solved. A DSL can have multiple notations including textual and graphical notations. In some cases, one of these notations for a DSL is enough but there are many cases where a single notation does not suffice and there is a demand to support multiple notations for the same DSL. UML profile is one of several approaches used to define a DSL, however most UML tools only come with graphical editors. In this paper, we present our approach and industrial experience on integrating textual and graphical editors for a UML profile-based DSL. This work was conducted as part of an explorative study at Ericsson. The main aim of the study was to investigate how to introduce a textual editor to an already existing UML profile-based DSL in an Eclipse environment. We report on the challenges of integrating textual and graphical editors for UML profile-based DSLs in practice, our chosen approach, specific constraints and requirements of the study.