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

Metrics for model transformations

One of the key concepts of model-driven engineering (MDE) is model transformation. Because of the crucial role of model transformations in MDE, they have to be treated in a similar way as traditional software artifacts. It is therefore necessary to define and assess their quality. In this paper we present three metric sets for assessing the quality of ATL, QVTO, and Xtend model transformations. We show the overlap between the metric sets, as well as their differences

Metrics for analyzing the quality of model transformations

Model transformations become increasingly important with the emergence of model driven engineering of, amongst others, objectoriented software systems. It is therefore necessary to define and evaluate the quality of model transformations. The goal of our research is to make the quality of model transformations measurable. This position paper presents the first results of this ongoing research. We present the quality attributes we have identified thus far and a set of metrics to assess these quality attributes

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Performance analysis of a palletizing system

When designing the layout of the material handling system for a warehouse there is a need for the analysis of overall system performance. Since warehouses are typically very large and complex systems it is infeasible to build a simulation model for the entire system. Our approach is to divide the system into subsystems that are small enough to be captured in simulation models. These models can then later be assembled to acquire a simulation model of the entire system. In this case study we assess the feasibility of this approach by creating a simulation model of a part of a warehouse and verify whether it can be used to embed it in a larger simulation model. The subsystem we use for our case study is a container unloading and automatic palletizing system. This system is chosen because it has already been studied extensively using another simulation tool. We also do a performance analysis of this system in order to come to an optimal layout for this subsystem as well as to reproduce the results of the earlier study for validation. For our performance analysis we created a chi model of the unloading and palletizing area. The process algebra chi has been extensively used for modeling and simulation of real-time manufacturing systems. Our case study is also used as a means to assess the suitability of chi for modeling and simulation in a logistics environment. Our experiments resulted in roughly the same outcomes as the earlier study. It turns out that for the required throughput the layout chosen in that study is optimal. We also concluded that chi is perfectly suitable for modeling logistic systems. Considering the extensive time it takes to run simulations of a rather small part of a warehouse using chi, we conclude that it is infeasible to perform simulations of entire warehousing systems by integrating the simulation models of all subsystems into one simulation model. To overcome this problem, aggregate modeling can be used

Prototyping the Semantics of a DSL using ASF+SDF: Link to Formal Verification of DSL Models

A formal definition of the semantics of a domain-specific language (DSL) is a key prerequisite for the verification of the correctness of models specified using such a DSL and of transformations applied to these models. For this reason, we implemented a prototype of the semantics of a DSL for the specification of systems consisting of concurrent, communicating objects. Using this prototype, models specified in the DSL can be transformed to labeled transition systems (LTS). This approach of transforming models to LTSs allows us to apply existing tools for visualization and verification to models with little or no further effort. The prototype is implemented using the ASF+SDF Meta-Environment, an IDE for the algebraic specification language ASF+SDF, which offers efficient execution of the transformation as well as the ability to read models and produce LTSs without any additional pre or post processing.Comment: In Proceedings AMMSE 2011, arXiv:1106.596

Efficiency of stress-adaptive traits chlorophyll fluorescence and membrane thermo- stability in wheat under high temperature

Despite developments in targeted gene sequencing and whole-genome analysis techniques, the robust detection of all genetic variation, including structural variants, in and around genes of interest and in an allele-specific manner remains a challenge. Here we present targeted locus amplification (TLA), a strategy to selectively amplify and sequence entire genes on the basis of the crosslinking of physically proximal sequences. We show that, unlike other targeted re-sequencing methods, TLA works without detailed prior locus information, as one or a few primer pairs are sufficient for sequencing tens to hundreds of kilobases of surrounding DNA. This enables robust detection of single nucleotide variants, structural variants and gene fusions in clinically relevant genes, including BRCA1 and BRCA2, and enables haplotyping. We show that TLA can also be used to uncover insertion sites and sequences of integrated transgenes and viruses. TLA therefore promises to be a useful method in genetic research and diagnostics when comprehensive or allele-specific genetic information is needed

The right tool for the right job : assessing model transformation quality

Model-Driven Engineering (MDE) is a software engineering discipline in which models play a central role. One of the key concepts of MDE is model transformations. Because of the crucial role of model transformations in MDE, they have to be treated in a similar way as traditional software artifacts. They have to be used by multiple developers, they have to be maintained according to changing requirements and they should preferably be reused. It is therefore necessary to define and assess their quality. In this paper, we give two definitions for two different views on the quality of model transformations. We will also give some examples of quality assessment techniques for model transformations. The paper concludes with an argument about which type of quality assessment technique is most suitable for either of the views on model transformation quality