Assessing and improving the quality of model transformations

Software is pervading our society more and more and is becoming increasingly complex. At the same time, software quality demands remain at the same, high level. Model-driven engineering (MDE) is a software engineering paradigm that aims at dealing with this increasing software complexity and improving productivity and quality. Models play a pivotal role in MDE. The purpose of using models is to raise the level of abstraction at which software is developed to a level where concepts of the domain in which the software has to be applied, i.e., the target domain, can be expressed e??ectively. For that purpose, domain-speci??c languages (DSLs) are employed. A DSL is a language with a narrow focus, i.e., it is aimed at providing abstractions speci??c to the target domain. This makes that the application of models developed using DSLs is typically restricted to describing concepts existing in that target domain. Reuse of models such that they can be applied for di??erent purposes, e.g., analysis and code generation, is one of the challenges that should be solved by applying MDE. Therefore, model transformations are typically applied to transform domain-speci??c models to other (equivalent) models suitable for di??erent purposes. A model transformation is a mapping from a set of source models to a set of target models de??ned as a set of transformation rules. MDE is gradually being adopted by industry. Since MDE is becoming more and more important, model transformations are becoming more prominent as well. Model transformations are in many ways similar to traditional software artifacts. Therefore, they need to adhere to similar quality standards as well. The central research question discoursed in this thesis is therefore as follows. How can the quality of model transformations be assessed and improved, in particular with respect to development and maintenance? Recall that model transformations facilitate reuse of models in a software development process. We have developed a model transformation that enables reuse of analysis models for code generation. The semantic domains of the source and target language of this model transformation are so far apart that straightforward transformation is impossible, i.e., a semantic gap has to be bridged. To deal with model transformations that have to bridge a semantic gap, the semantics of the source and target language as well as possible additional requirements should be well understood. When bridging a semantic gap is not straightforward, we recommend to address a simpli??ed version of the source metamodel ??rst. Finally, the requirements on the transformation may, if possible, be relaxed to enable automated model transformation. Model transformations that need to transform between models in di??erent semantic domains are expected to be more complex than those that merely transform syntax. The complexity of a model transformation has consequences for its quality. Quality, in general, is a subjective concept. Therefore, quality can be de??ned in di??erent ways. We de??ned it in the context of model transformation. A model transformation can either be considered as a transformation de??nition or as the process of transforming a source model to a target model. Accordingly, model transformation quality can be de??ned in two di??erent ways. The quality of the de??nition is referred to as its internal quality. The quality of the process of transforming a source model to a target model is referred to as its external quality. There are also two ways to assess the quality of a model transformation (both internal and external). It can be assessed directly, i.e., by performing measurements on the transformation de??nition, or indirectly, i.e., by performing measurements in the environment of the model transformation. We mainly focused on direct assessment of internal quality. However, we also addressed external quality and indirect assessment. Given this de??nition of quality in the context of model transformations, techniques can be developed to assess it. Software metrics have been proposed for measuring various kinds of software artifacts. However, hardly any research has been performed on applying metrics for assessing the quality of model transformations. For four model transformation formalisms with di??fferent characteristics, viz., for ASF+SDF, ATL, Xtend, and QVTO, we de??ned sets of metrics for measuring model transformations developed with these formalisms. While these metric sets can be used to indicate bad smells in the code of model transformations, they cannot be used for assessing quality yet. A relation has to be established between the metric sets and attributes of model transformation quality. For two of the aforementioned metric sets, viz., the ones for ASF+SDF and for ATL, we conducted an empirical study aiming at establishing such a relation. From these empirical studies we learned what metrics serve as predictors for di??erent quality attributes of model transformations. Metrics can be used to quickly acquire insights into the characteristics of a model transformation. These insights enable increasing the overall quality of model transformations and thereby also their maintainability. To support maintenance, and also development in a traditional software engineering process, visualization techniques are often employed. For model transformations this appears as a feasible approach as well. Currently, however, there are few visualization techniques available tailored towards analyzing model transformations. One of the most time-consuming processes during software maintenance is acquiring understanding of the software. We expect that this holds for model transformations as well. Therefore, we presented two complementary visualization techniques for facilitating model transformation comprehension. The ??rst-technique is aimed at visualizing the dependencies between the components of a model transformation. The second technique is aimed at analyzing the coverage of the source and target metamodels by a model transformation. The development of the metric sets, and in particular the empirical studies, have led to insights considering the development of model transformations. Also, the proposed visualization techniques are aimed at facilitating the development of model transformations. We applied the insights acquired from the development of the metric sets as well as the visualization techniques in the development of a chain of model transformations that bridges a number of semantic gaps. We chose to solve this transformational problem not with one model transformation, but with a number of smaller model transformations. This should lead to smaller transformations, which are more understandable. The language on which the model transformations are de??ned, was subject to evolution. In particular the coverage visualization proved to be bene??cial for the co-evolution of the model transformations. Summarizing, we de??ned quality in the context of model transformations and addressed the necessity for a methodology to assess it. Therefore, we de??ned metric sets and performed empirical studies to validate whether they serve as predictors for model transformation quality. We also proposed a number of visualizations to increase model transformation comprehension. The acquired insights from developing the metric sets and the empirical studies, as well as the visualization tools, proved to be bene??cial for developing model transformations

Private Military and Security Companies and Gendered Human Rights Challenges: Oversight or Blatant Disregard?

It is well documented that the private military and security industry has the capacity to do great gendered harms to both those it encounters and those it employs.1 Significantly, it is also a sector where a variety of human rights-based approaches, instruments and mechanisms have emerged beyond the United Nations Guiding Principles on Business and Human Rights (UNGPs).2 The International Code of Conduct for Private Security Providers (ICoC) addresses gender, and sexual and gender-based violence (SGBV), and explicitly requires private military and security companies (PMSCs) to integrate a gender perspective in their practices.3 Through an examination of publicly available documents and policies required for PMSCs certified as complying with the ICoC, this piece evaluates whether PMSCs do in fact integrate a gender perspective into their human rights policies and grievance procedures (see Table 1).4 Our study of certified PMSCs demonstrates that despite increased attention to the potential for negative gender impacts in the sector, companies have not developed gender-responsive policies and procedures. It can be said, therefore, that gender is not addressed in any meaningful way by PMSCs. More specifically, we conclude that PMSCs have not yet shown the required holistic understanding of gendered impacts and barriers that is required to respect human rights, and that further efforts are needed in the sector

Optimizing the Post Sandvik Nanoflex material model using inverse optimization and the finite element method

This article describes an inverse optimization method for the Sandvik Nanoflex steel in cold forming\ud processes. The optimization revolves around measured samples and calculations using the Finite Element\ud Method. Sandvik Nanoflex is part of the group of meta-stable stainless steels. These materials are characterized\ud by a good corrosion resistance, high strength, good formability and crack resistance. In addition, Sandvik\ud Nanoflex has a strain-induced transformation and, depending on austenising conditions and chemical composition,\ud a stress-assisted transformation can occur. The martensite phase of this material shows a substantial aging\ud response. The inverse optimization is a sub-category of the optimization techniques. The inverse optimization\ud method uses a top down approach, as the name implies. The starting point is a prototype state where the current\ud state is to converge on. In our experiment the test specimen is used as prototype and a calculation result as\ud current state. The calculation is then adapted so that the result converges towards the test example. An iterative\ud numerical optimization algorithm controls the adaptation. For the inverse optimization method two parameters\ud are defined: shape of the product and martensite profile. These parameters are extracted from both calculation\ud and test specimen, using Fourier analysis and integrals. An optimization parameter is then formulated from\ud the extracted parameters. The method uses this optimization parameter to increase the accuracy of ”The Post”\ud material model for Sandvik Nanoflex. [1] The article will describe a method to optimize material models, using\ud a combination practical experiments, Finite Element Method and parameter extraction

Using metrics for assessing the quality of ATL model transformations

Model transformations play a pivotal role in model-driven engineering. Since they are in many ways similar to traditional software artifacts, they have to be treated similarly. Therefore, it is necessary to assess their quality. We propose to use metrics to assess various quality attributes of model transformations In this paper, we focus on model transformations created using ATL, which ATL is currently one of the most widely used model transformation formalisms. We have collected metrics data from a heterogeneous collection of seven model transformations. The quality of the same transformations has been evaluated manually by nineteen ATL experts. We assess whether the metrics are appropriate predictors for the quality attributes by correlating the metrics data with the expert data. To support or refute the correlations, we also acquired qualitative statements from the ATL experts. Although the study was intended as a first exploration of the relation between metrics and quality attributes, some significant correlations were found that are supported by statements of the participants