Beatriz Marín

Using UML as a Domain-Specific Modeling Language: A Proposal for Automatic Generation of UML Profiles

Nowadays, there are several MDD approaches that have defined Domain-Specific Modeling Languages (DSML) that are oriented to representing their particular semantics. However, since UML is the standard language for software modeling, many of these MDD approaches are trying to integrate their semantics into UML in order to use UML as DSML. The use of UML profiles is a recommended strategy to perform this integration allowing, among other benefits, the use of the existent UML modeling tools. However, in the literature related to UML profile construction; it is not possible to find a standardized UML profile generation process. Therefore, a process that integrates a DSML into UML through the automatic generation of a UML profile is presented in this paper. This process facilitates the correct use of UML in a MDD context and provides a solution to take advantage of the benefits of UML and DSMLs.

Measurement of Functional Size in Conceptual Models: A Survey of Measurement Procedures Based on COSMIC

Many functional size measurement procedures have been developed for applying the COSMIC measurement method to particular methods of software production. A subset of these measurement procedures is centered on the measurement of the functional size of the applications from their conceptual models, allowing the generation of indicators in early stages of the development cycle of a software product. This paper presents a survey of these functional size measurement procedures in order to provide a guide for practitioners and researchers. Finally, a general analysis focused on the results obtained in the survey is performed to obtain important lessons that must be considered in the development of correct measurement procedures.

Automating the Measurement of Functional Size of Conceptual Models in an MDA Environment

The manual measurement of functional size is generally very time-consuming and has many precision errors. For this reason, it is necessary to automate the measurement process to obtain a solution that can be applied in a MDA industrial development. The OO-Method COSMIC Function Points (OOmCFP) is a measurement procedure that has been designed to measure the functional size of object-oriented applications generated from their conceptual models by means of model transformations. This work presents the definition of the mechanisms that are necessary to automate the OOmCFP procedure. This work also presents the OOmCFP tool that implements the OOmCFP procedure. Since this tool measures the functional size of industrial applications generated in MDA environments from their conceptual models, it is not necessary to perform the measurement task on the final code. The OOmCFP tool incorporates the benefits that the COSMIC measurement method provides. These benefits are demonstrated through a comparative analysis.

From i* Requirements Models to Conceptual Models of a Model Driven Development Process

A good understanding of the systems requirements has a high impact in the successful development of software products. Therefore, an appropriate requirements model must provide a comprehensive structure for what must be elicited, evaluated, specified, consolidated, and modified, instead of just providing facilities for software specifications. Since there is a well-known gap between requirements specifications and final software products, we propose the integration of Goal-Oriented Requirements Engineering (GORE) and Model-Driven Development (MDD) to solve this gap. The core of our proposal is comprised by a set of guidelines to automate the process of going from an initial i* model to a final software product by means of a precise model transformation process. Finally, we use a case study that is based on a photographic agency system in order to illustrate our approach.

A Methodological Framework for Evaluating Software Testing Techniques and Tools

There exists a real need in industry to have guidelines on what testing techniques use for different testing objectives, and how usable (effective, efficient, satisfactory) these techniques are. Up to date, these guidelines do not exist. Such guidelines could be obtained by doing secondary studies on a body of evidence consisting of case studies evaluating and comparing testing techniques and tools. However, such a body of evidence is also lacking. In this paper, we will make a first step towards creating such body of evidence by defining a general methodological evaluation framework that can simplify the design of case studies for comparing software testing tools, and make the results more precise, reliable, and easy to compare. Using this framework, (1) software testing practitioners can more easily define case studies through an instantiation of the framework, (2) results can be better compared since they are all executed according to a similar design, (3) the gap in existing work on methodological evaluation frameworks will be narrowed, and (4) a body of evidence will be initiated. By means of validating the framework, we will present successful applications of this methodological framework to various case studies for evaluating testing tools in an industrial environment with real objects and real subjects.

Towards testing future Web applications

The current Web applications are in continuous evolution to provide new and more complex functionalities, which can improve the user experience by means of adaptivity and dynamic changes. Since testing is the most frequently used technique to evaluate the quality of software applications in industry, novel testing approaches will be necessary to evaluate the quality of future (and more complex) web applications. In this paper, we investigate the testing challenges of future web applications and propose a testing methodology that addresses these challenges by the integration of search-based testing, model-based testing, oracle learning, concurrency testing, combinatorial testing, regression testing, and coverage analysis. This paper also presents a testing metamodel that states testing concepts and their relationships, which are used as the theoretical basis of the proposed testing methodology.

A quality model for conceptual models of MDD environments

In Model-Driven Development (MDD) processes, models are key artifacts that are used as input for code generation. Therefore, it is very important to evaluate the quality of these input models in order to obtain high-quality software products. The detection of defects is a promising technique to evaluate software quality, which is emerging as a suitable alternative for MDD processes. The detection of defects in conceptual models is usually manually performed. However, since current MDD standards and technologies allow both the specification of metamodels to represent conceptual models and the implementation of model transformations to automate the generation of final software products, it is possible to automate defect detection from the defined conceptual models. This paper presents a quality model that not only encapsulates defect types that are related to conceptual models but also takes advantage of current standards in order to automate defect detection in MDD environments.

Using UML profiles to interchange DSML and UML models

A key requirement for MDD solutions is to have a modeling language that allows the correct representation of conceptual models. Nowadays, there are two options that are the most widely used for the definition of these modeling languages: 1) the specification of a domain-specific modeling language (DSML) or 2) the customization of UML. In practice, these two modeling alternatives are viewed as opposite solutions. However, since both alternatives provide benefits for the application of MDD solutions, in this paper, we present a proposal that uses UML profile extension mechanisms to interchange modeling information between DSML-based models and UML models. This proposal shows how these two modeling alternatives can be integrated in a unique MDD solution.

Applying a Functional Size Measurement Procedure for Defect Detection in MDD Environments

Nowadays, is widely accepted that functional size measurement is essential to manage and control software projects. In order to obtain early indicators for software projects, many functional size measurement procedures have been developed to measure the functional size of conceptual models. To do this, the measurement procedures assume that models do not present defects. However, this is an unreal assumption because, in practice, the conceptual models can have defects that may affect the implementation of final applications. This is especially important for software production processes based on MDD technology, where the conceptual models are key artifacts used as inputs in the process of code generation. Therefore, this paper presents how a functional size measurement procedure (which has been developed for the measurement of conceptual models of a specific MDD environment) can help in the detection of defects in conceptual models.

A Tool for Automatic Defect Detection in Models Used in Model-Driven Engineering

In the Model-Driven Engineering (MDE) field, the quality assurance of the involved models is fundamental for performing correct model transformations and generating final software applications. To evaluate the quality of models, defect detection is usually performed by means of reading techniques that are manually applied. Thus, new approaches to automate the defect detection in models are needed. To fulfill this need, this paper presents a tool that implements a novel approach for automatic defect detection, which is based on a model-based functional size measurement procedure. This tool detects defects related to the correctness and the consistency of the models. Thus, our contribution lays in the new approach presented and its automation for the detection of defects in MDE environments.