CVSM Bibliography, Entry [ Sh2015Diss ]


Shariat Yazdi, Hamed: Statistical Analysis and Simulation of Design Models Evolution; U.Siegen, UB, URN: urn:nbn:de:hbz:467-9589; 2015
Download: Volltext URN
Download: Volltext dokumentix U.Siegen
Library Entries: U.Siegen, UB
Deskriptoren: CVSM, PI

Abstract: Tools, algorithms and methods in the context of Model-Driven Engineering (MDE) have to be assessed, evaluated and tested with regard to different aspects such as correctness, quality, scalability and efficiency. Unfortunately, appropriate test models are scarcely available and those which are accessible often lack desired properties. Therefore, one needs to resort to artificially generated test models in practice.
Many services and features of model versioning systems are motivated from the collaborative development paradigm. Testing such services does not require single models, but rather pairs of models, one being derived from the other one by applying a known sequence of edit steps. The edit operations used to modify the models should be the same as in usual development environments, e.g. adding, deleting and changing of model elements in visual model editors. Existing model generators are motivated from the testing of model transformation engines, they do not consider the true nature of evolution in which models are evolved through iterative editing steps. They provide no or very little control over the generation process and they can generate only single models rather than model histories. Moreover, the generation of stochastic and other properties of interest also are not supported in the existing approaches.
Furthermore, blindly generating models through random application of edit operations does not yield useful models, since the generated models are not (stochastically) realistic and do not reflect true properties of evolution in real software systems. Unfortunately, little is known about how models of real software systems evolve over time, what are the properties and characteristics of evolution, how one can mathematically formulate the evolution and simulate it.
To address the previous problems, we introduce a new general approach which facilitates generating (stochastically) realistic test models for model differencing tools and tools for analyzing model histories. We propose a model generator which addresses the above deficiencies and generates or modifies models by applying proper edit operations. Fine control mechanisms for the generation process are devised and the generator supports stochastic and other properties of interest in the generated models. It also can generate histories, i.e. related sequences, of models. Moreover, in our approach we provide a methodological framework for capturing, mathematically representing and simulating the evolution of real design models. The proposed framework is able to capture the evolution in terms of edit operations applied between revisions. Mathematically, the representation of evolution is based on different statistical distributions as well as different time series models. Forecasting, simulation and generation of stochastically realistic test models are discussed in detail. As an application, the framework is applied to the evolution of design models obtained from sample a set of carefully selected Java systems.
In order to study the the evolution of design models, we analyzed 9 major Java projects which have at least 100 revisions. We reverse engineered the design models from the Java source code and compared consecutive revisions of the design models. The observed changes were expressed in terms of two sets of edit operations. The first set consists of 75 low-level graph edit operations, e.g. add, delete, etc. of nodes and edges of the abstract syntax graph of the models. The second set consists of 188 high-level (user-level) edit operations which are more meaningful from a developer's point of view and are frequently found in visual model editors. A high-level operation typically comprises several low-level operations and is considered as one user action.
In our approach, we mathematically formulated the pairwise evolution, i.e. changes between each two subsequent revisions, using statistical models (distributions). In this regard, we initially considered many distributions which could be promising in modeling the frequencies of the observed low-level and high-level changes. Six distributions were very successful in modeling the changes and able to model the evolution with very good rates of success. To simulate the pairwise evolution, we studied random variate generation algorithms of our successful distributions in detail. For four of our distributions which no tailored algorithms existed, we indirectly generated their random variates.
The chronological (historical) evolution of design models was modeled using three kinds of time series models, namely ARMA, GARCH and mixed ARMA-GARCH. The comparative performance of the time series models for handling the dynamics of evolution as well as accuracies of their forecasts was deeply studied. Roughly speaking, our studies show that mixed ARMA-GARCH models are superior to other models. Moreover, we discuss the simulation aspects of our proposed time series models in detail.
The knowledge gained through statistical analysis of the evolution was then used in our test model generator in order to generate more realistic test models for model differencing, model versioning, history analysis tools, etc.