Today, discrete event simulation is the only reliable tool for detailed analysis of complex behaviors of modern manufacturing systems. However, building high fidelity simulation models is expensive. Hence, it is important to improve the simulation modeling productivity. In this research, we explore two approaches for the improvement of simulation modeling productivity. One approach is the Virtual Factory Approach, using a general-purpose model for a system to achieve various simulation objectives with a single high fidelity model through abstraction. The other approach is the Reference Model Approach, which is to build fundamental building blocks for simulation models of any system in a domain with formal descriptions and domain knowledge. In the Virtual Factory Approach, the challenge is to show the validity of the methodology. We develop a formal framework for the relationships between higher fidelity and lower fidelity models, and provide justification that the models abstracted from a higher fidelity model are interchangeable with various abstract simulation models for a target system. For the Reference Model Approach, we attempt to overcome the weak points inherited from ad-hoc modeling and develop a formal reference model and a model generation procedure for discrete part manufacturing systems, which covers most modern manufacturing systems.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-04-10T20:00:05Z No. of bitstreams: 1 FRANCISCO GOMES DE OLIVEIRA NETO - TESE PPGCC 2014..pdf: 5163454 bytes, checksum: 228c1fc4f2dc9aad01698011238cfde1 (MD5) / Made available in DSpace on 2018-04-10T20:00:05Z (GMT). No. of bitstreams: 1 FRANCISCO GOMES DE OLIVEIRA NETO - TESE PPGCC 2014..pdf: 5163454 bytes, checksum: 228c1fc4f2dc9aad01698011238cfde1 (MD5) Previous issue date: 2014-07-30 / uring software maintenance, several modifications can be performed in a specification model in order to satisfy new requirements. Perform regression testing on modified software is known to be a costly and laborious task. Test case selection, test case prioritization, test suite minimisation,among other methods,aim to reduce these costs by selecting or prioritizing a subset of test cases so that less time, effort and thus money are involved in performing regression testing. In this doctorate research, we explore the general problem of automatically selecting test cases in a model-based testing (MBT) process where specification models were modified. Our technique, named Similarity Approach for Regression Testing (SART), selects subset of test cases traversing modified regions of a software system’s specification model. That strategy relies on similarity-based test case selection where similarities between test cases from different software versions are analysed to identify modified elements in a model. In addition, we propose an evaluation approach named Search Based Model Generation for Technology Evaluation (SBMTE) that is based on stochastic model generation and search-based techniques to generate large samples of realistic models to allow experiments with model-based techniques. Based on SBMTE,researchers are able to develop model generator tools to create a space of models based on statistics from real industrial models, and eventually generate samples from that space in order to perform experiments. Here we developed a generator to create instances of Annotated Labelled Transitions Systems (ALTS), to be used as input for our MBT process and then perform an experiment with SART.In this experiment, we were able to conclude that SART’s percentage of test suite size reduction is robust and able to select a sub set with an average of 92% less test cases, while ensuring coverage of all model modification and revealing defects linked to model modifications. Both SART and our experiment are executable through the LTS-BT tool, enabling researchers to use our selections trategy andr eproduce our experiment. / During software maintenance, several modifications can be performed in a specification model in order to satisfy new requirements. Perform regression testing on modified software is known to be a costly and laborious task. Test case selection, test case prioritization, test suite minimisation,among other methods,aim to reduce these costs by selecting or prioritizing a subset of test cases so that less time, effort and thus money are involved in performing regression testing. In this doctorate research, we explore the general problem of automatically selecting test cases in a model-based testing (MBT) process where specification models were modified. Our technique, named Similarity Approach for Regression Testing (SART), selects subset of test cases traversing modified regions of a software system’s specification model. That strategy relies on similarity-based test case selection where similarities between test cases from different software versions are analysed to identify modified elements in a model. In addition, we propose an evaluation approach named Search Based Model Generation for Technology Evaluation (SBMTE) that is based on stochastic model generation and search-based techniques to generate large samples of realistic models to allow experiments with model-based techniques. Based on SBMTE,researchers are able to develop model generator tools to create a space of models based on statistics from real industrial models, and eventually generate samples from that space in order to perform experiments. Here we developed a generator to create instances of Annotated Labelled Transitions Systems (ALTS), to be used as input for our MBT process and then perform an experiment with SART.In this experiment, we were able to conclude that SART’s percentage of test suite size reduction is robust and able to select a sub set with an average of 92% less test cases, while ensuring coverage of all model modification and revealing defects linked to model modifications. Both SART and our experiment are executable through the LTS-BT tool, enabling researchers to use our selections trategy andr eproduce our experiment.