MDI4SE 2018 Abstracts

Full Papers Short Papers
Full Papers
Paper Nr: 1
Title:

The Topological Functioning Model as a Reference Model for Software Functional and Non-functional Requirements
Authors:

Erika Nazaruka and Jānis Osis
Abstract: Specification of non-functional requirements in models is a challenge due to extra-functional nature of the requirements. The topological functioning model (TFM) can serve as a reference model for specifying mappings from both functional and non-functional requirements to the functional characteristics and structure of the modelled system. The main principle presented in this paper extends a way of specification of the TFM functional characteristics and causal relationships and provides a specification of mapping types as tuples of TFM functional features extended with requirements and characteristics of these relationships, namely, completeness and overlapping for functional requirements, and scope and dynamic characteristics for non-functional ones. This allows propagating the mappings from requirements to software implementing constructs, that would be useful for further architectural decisions and development of test cases.
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Paper Nr: 3
Title:

Cost-Benefit Analysis at Runtime for Self-adaptive Systems Applied to an Internet of Things Application
Authors:

M. Jeroen Van Der Donckt, Danny Weyns, M. Usman Iftikhar and Ritesh Kumar Singh
Abstract: Ensuring the qualities of modern software systems, such as the Internet of Things, is challenging due to various uncertainties, such as dynamics in availability of resources or changes in the environment. Self-adaptation is an established approach to deal with such uncertainties. Self-adaptation equips a software system with a feedback loop that tracks changes and adapts the system accordingly to ensure its quality goals. Current research in this area has primarily focussed on the benefits that self-adaptation can offer. However, realising adaption can also incur costs. Ignoring these costs may invalidate the expected benefits. We start with demonstrating that the costs for adaptation can be significant. To that end, we apply a state-of-the-art approach for self-adaptation to an Internet of Things (IoT) application. We then present CB@R (Cost-Benefit analysis @ Runtime), a novel model-based approach for runtime decision-making in self-adaptive systems. CB@R is inspired by the Cost-Benefit Analysis Method (CBAM), which is an established approach for analysing costs and benefits of architectural decisions. We evaluate CB@R for a real world deployed IoT application and compare it with the conservative approach applied in practice and a state-of-the-art self-adaptation approach.
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Paper Nr: 4
Title:

Retrieving the Topology from the Knowledge Frame System for Composition of the Topological Functioning Model
Authors:

Vladislavs Nazaruks and Jānis Osis
Abstract: Model-driven software development considers models as a core artefact for generation of software source code. This requires models to be formal and complete enough for further transformations and code generation. It requires clear understanding of such knowledge as functionality, objects and dependencies in the problem domain. In our approach, this knowledge is kept in the frame-based system. The completeness and consistency of the knowledge can be verified by generating and validating the topological functioning model (TFM). The TFM is a model, which elements are linked by the topology, i.e. by cause and effect relations among the functional characteristics of the domain. Automated composition of the TFM requires retrieving appropriate conditions on cause and effect functional characteristics of the system from the knowledge base. The proposed algorithm reads data of functional characteristics kept in the knowledge base, relates those of them, where a cause condition corresponds to an effect condition, and generates data for the corresponding cause-and-effect relation. The difficulty is that conditions can be combined using logical operators AND, OR, XOR, as well as can use negation NOT. The benefit is that any inconsistency in the retrieved topology could be discovered and marked for further analysis. This should force careful analysis of the problem domain before generation of the design model. That could lead to decreasing a number of errors made due to uncertainty in the analysis.
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Paper Nr: 5
Title:

Determination of Natural Language Processing Tasks and Tools for Topological Functioning Modelling
Authors:

Erika Nazaruka and Jānis Osis
Abstract: Topological Functioning Modelling (TFM) is based on analysis of exhaustive verbal descriptions of the domain functionality. Manual acquisition of knowledge about the domain from text in natural language requires a lot of resources. Natural Language Processing (NLP) tools provide automatic analysis of text in natural language and may fasten and make cheaper this process. First, the knowledge, its expressing elements of the English language, and processing tasks that are required for construction of the topological functioning model are identified. The overview of the support of these tasks by the main NLP pipelines is based on the available documentation without performing practical experiments. The results showed that among the selected six NLP pipelines the largest support comes from the Stanford CoreNLP toolkit, FreeLing, and NLTK toolkit. They allow analysing not only the words and sentences, but also dependencies in word groups and between sentences. The obtained results can be used for academics and practitioners that perform research on NLP for composition of domain (business, system, software) models.
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Paper Nr: 7
Title:

Verification of Causality in the Frame System based on the Topological Functioning Modelling
Authors:

Vladislavs Nazaruks and Jānis Osis
Abstract: Causality is universal relations among phenomena (states, facts, elements, functions) in the system. Verification of causality in the knowledge frame system based on principles of the topological functioning modelling can help in discovering inconsistencies such as incompleteness, ambiguity or contradictions in knowledge on system’s functioning. The method for such verification is presented in this paper. It is based on topological and functioning properties of the topological functioning model including the definition of continuous mapping between topological spaces. The method helps in discovering inconsistent combinations of cause-and-effect relations or a lack of them. Functional characteristics of the system involved in these relations are marked as doubtful. The results of verification require additional investigation by a software developer. A use of the proposed method can lead to more thorough system analysis before development of the solution.
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Short Papers
Paper Nr: 6
Title:

Tool Support to Automate Transformations from SBVR to UML Use Case Diagram
Authors:

Imane Essebaa and Salima Chantit
Abstract: Model transformation becomes a very important technique in software engineering as it helps to guarantee traceability between models and develop software applications quickly, In this context, the purpose of this work is an approach that allows generating UML Use Case Diagram (UCD) from Semantic Business Vocabulary and Business Rules (SBVR) which is a standard introduced by OMG that can be used to capture software requirements in structured English. The paper gives a set of rules that map SBVR element into UCD elements. This approach is a part of our works that focus on automating Model Transformations to generate the application code following standards of Model Driven Architecture approach. Particulary we use SBVR standard and UCD to model the Computation Independent Model (CIM) which is the first level of abstraction. The paper describes also an implementation of this approach as an Eclipse plug-in that automates the transformation rules defined using QVT language. In order to well illustrate our approach, we apply it on RentalCarAgency system.
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