doc. Ing. Miroslav Bureš, Ph.D.

Development of the Internet of Things (loT) technology brings a number of challenges in the Quality Assurance (QA) area, and these challenges are also interesting research opportunities. Compared with classical web-based information systems, loT systems have their specifics, which strongly justify the development of innovative loT- specific formal model-based test automation methods. A typical example is handling the combinatorial explosion in the number of configurations, testing of lot solutions under a limited network connection or more effective model-based methods of integration testing. After initial analysis, one of these prospective topics will be selected, and innovative formal method developed and experimentally verified during the PhD project.

Size estimation is an essential topic in software and IoT engineering projects. Faults in effort estimation can put these projects to economic difficulties and risks or even cause failure. Currently, conventional methods fail to estimate software size (or development effort) accurately. In the project, a novel hybrid estimate model will be investigated. The research will explore the adaptation of estimation models to changing project features and available project characteristics. As a result, a feature-specific estimation model and adaptive problem domain-specific estimation algorithm will be proposed and evaluated compared to state-of-the-art methods. In this comparison, a multi-criteria approach will be used to be sure that the proposed approach outperforms the current techniques.

Testing and defect fixing typically represents 30-50% of the budget of contemporary large software projects, which is the strong motivation for research and development of more efficient testing methods. Test automation based on formal software models represents a prospective stream here. Current formal techniques as path-based testing or combinatorial / constrained interaction testing provide a solid baseline of these techniques. However, the approaches and algorithms can be improved in a number of points. An example is adding constraints and more metadata to path-based testing techniques, or prioritization mechanism to combinatorial / constrained interaction testing algorithms. The goal of the research is to select one of these prospective areas, develop an innovative formal test case generation strategy in this area and compare the results with state of the art.

Current complex software systems are often composed of various integrated parts and involve a number of legacy components. Well-designed software architecture positively impacts software development and maintenance costs. In contrast, various organic mixtures of different architectonic styles can cause severe cost, performance and reliability-related issues in the software development lifecycle. The current software industry lacks effective methods to streamline sub-optimal software architectures. Instead, state-of-the-art focuses mainly on blueprinting an ideal software architecture or automated visualization of existing system architectures. The goal of PhD project is to research a well-working combination of formal software architecture modelling methods with simulation, static analysis approaches, and audit guidelines to effectively analyze existing architecture and identify the most feasible and useful streamlining suggestions.

Software defect prediction can contribute to better software project planning and evaluation, thus leading to less risky and more economical software projects. The fundamental approaches generally lack the identification of factors correlated with defects. To get better effectiveness, these factors have to be identified more intensively. The next drawback of current methods is that they are often adjusted to a particular project (or set of projects), and it might not be easy to apply them to other projects. In defect prediction models, it is necessary to determine the association between factors causing the defects and defects by using various combinations and subsets of these attributes with various classifiers. In addition, the investigation will determine whether there is a link between analysis factors and defects. The research aims to design a general framework for defect prediction. Compared to the state-of-art defect prediction techniques, the created framework will give better defect prediction results.