On Formalizing UML and OCL Features and Their Employment to Runtime Verification

Model-driven development (MDD) has been identified as a promising approach for developing software. By using abstract models of a system and by generating parts of the system out of these models, one tries to improve the efficiency of the overall development process and the quality of the resulting software. In the context of MDD the Unified Modeling Language (UML) and its related textual Object Constraint Language (OCL) have gained a high recognition. To be able to generate systems of high quality and to allow for interoperability between modeling tools, a well-defined semantics for these languages is required. This thesis summarizes published work in this context that employs an endogenous metamodeling approach to define the semantics of newer elements of the UML. While the covered elements are exhaustively used to define relations between elements of the metamodel of the UML, the UML specification leaves out a precise definition of their semantics. Our proposed approach uses models, not only to define the abstract syntax, but also to define the semantics of UML. By using UML and OCL for this, existing modeling tools can be used to validate the definition. The second part of this thesis covers work on the usage of UML and OCL models for runtime verification. It is shown how models can still be used at the end of a software development process, i. e., after an implementation has manually been added to generated parts, even though they are not used as central parts of the development process. This work also influenced the integration of protocol state machines into a modeling tool, which lead to publications about the runtime semantics of state machines and the capabilities to declaratively specify behavior using state machines

Using Codecharts for formally modelling and automating detection of patterns with application to Security Patterns

Software design patterns are solutions for recurring design problems. Many have introduced their catalogues in order to describe those patterns using templates which consist of informal statements as well as UML diagrams. Security patterns are design patterns for specific security problems domains, therefore, they are described in the same manner. However, the current catalogues describing security patterns contain a level of ambiguity and imprecision. These issues might result in incorrect implementations, which will be vital and at high cost security flaw, especially after delivery. In addition, software maintainability will be difficult thereafter, especially for systems with poor documentation. Therefore, it is important to overcome these issues by patterns formalisation in order to allow sharing the same understanding of the patterns to be implemented. The current patterns formalisation approaches aim to translate UML diagrams using different formal methods. However, these diagrams are incomplete or suffer from levels of ambiguity and imprecision. Furthermore, the employed diagrams notations cannot depict the abstraction shown in the patterns descriptions. In addition, the current formalisation approaches cannot formalise some security properties shown the diagrams, such as system boundary. Furthermore, detecting patterns in a source-code improves the overall software maintenance, especially when obsolete or lost system documentation is often the case of large and legacy systems. Current patterns detection approaches rely on translating the diagrams of the patterns. Consequently, the issue of detecting patterns with abstraction is not possible using such approaches. In addition, these approaches lack generality, abstraction detection, and efficiency. This research suggests the use of Codecharts for security patterns formalisation as well as studying relationships among patterns. Besides, it investigates relationships among patterns. Furthermore, it proposes a pattern detection approach which outperforms the current pattern detection approaches in terms of generality, and abstraction detection. The approach competes in performance with the current efficient pattern detection approaches