54.52 Software engineering
Refine
Document Type
- Doctoral Thesis (3)
- Master's Thesis (1)
Keywords
- model-based (2)
- Data flow analysis (1)
- Datenfluss (1)
- Datenflussanalyse (1)
- Datenflussmodell (1)
- GDPR (1)
- Knowledge (1)
- Mining (1)
- Ontology (1)
- Software Language (1)
Institute
For software engineers, conceptually understanding the tools they are using in the context of their projects is a daily challenge and a prerequisite for complex tasks. Textual explanations and code examples serve as knowledge resources for understanding software languages and software technologies. This thesis describes research on integrating and interconnecting
existing knowledge resources, which can then be used to assist with understanding and comparing software languages and software technologies on a conceptual level. We consider the following broad research questions that we later refine: What knowledge resources can be systematically reused for recovering structured knowledge and how? What vocabulary already exists in literature that is used to express conceptual knowledge? How can we reuse the
online encyclopedia Wikipedia? How can we detect and report on instances of technology usage? How can we assure reproducibility as the central quality factor of any construction process for knowledge artifacts? As qualitative research, we describe methodologies to recover knowledge resources by i.) systematically studying literature, ii.) mining Wikipedia, iii.) mining available textual explanations and code examples of technology usage. The theoretical findings are backed by case studies. As research contributions, we have recovered i.) a reference semantics of vocabulary for describing software technology usage with an emphasis on software languages, ii.) an annotated corpus of Wikipedia articles on software languages, iii.) insights into technology usage on GitHub with regard to a catalog of pattern and iv.) megamodels of technology usage that are interconnected with existing textual explanations and code examples.
Nowadays, almost any IT system involves personal data processing. In
such systems, many privacy risks arise when privacy concerns are not
properly addressed from the early phases of the system design. The
General Data Protection Regulation (GDPR) prescribes the Privacy by
Design (PbD) principle. As its core, PbD obliges protecting personal
data from the onset of the system development, by effectively
integrating appropriate privacy controls into the design. To
operationalize the concept of PbD, a set of challenges emerges: First, we need a basis to define privacy concerns. Without such a basis, we are not able to verify whether personal data processing is authorized. Second, we need to identify where precisely in a system, the controls have to be applied. This calls for system analysis concerning privacy concerns. Third, with a view to selecting and integrating appropriate controls, based on the results of system analysis, a mechanism to identify the privacy risks is required. Mitigating privacy risks is at the core of the PbD principle. Fourth, choosing and integrating appropriate controls into a system are complex tasks that besides risks, have to consider potential interrelations among privacy controls and the costs of the controls.
This thesis introduces a model-based privacy by design methodology to handle the above challenges. Our methodology relies on a precise definition of privacy concerns and comprises three sub-methodologies: model-based privacy analysis, modelbased privacy impact assessment and privacy-enhanced system design modeling. First, we introduce a definition of privacy preferences, which provides a basis to specify privacy concerns and to verify whether personal data processing is authorized. Second, we present a model-based methodology to analyze a system model. The results of this analysis denote a set of privacy design violations. Third, taking into account the results of privacy analysis, we introduce a model-based privacy impact assessment methodology to identify concrete privacy risks in a system model. Fourth, concerning the risks, and taking into account the interrelations and the costs of the controls, we propose a methodology to select appropriate controls and integrate them into a system design. Using various practical case studies, we evaluate our concepts, showing a promising outlook on the applicability of our methodology in real-world settings.
Software systems have an increasing impact on our daily lives. Many systems process sensitive data or control critical infrastructure. Providing secure software is therefore inevitable. Such systems are rarely being renewed regularly due to the high costs and effort. Oftentimes, systems that were planned and implemented to be secure, become insecure because their context evolves. These systems are connected to the Internet and therefore also constantly subject to new types of attacks. The security requirements of these systems remain unchanged, while, for example, discovery of a vulnerability of an encryption algorithm previously assumed to be secure requires a change of the system design. Some security requirements cannot be checked by the system’s design but only at run time. Furthermore, the sudden discovery of a security violation requires an immediate reaction to prevent a system shutdown. Knowledge regarding security best practices, attacks, and mitigations is generally available, yet rarely integrated part of software development or covering evolution.
This thesis examines how the security of long-living software systems can be preserved taking into account the influence of context evolutions. The goal of the proposed approach, S²EC²O, is to recover the security of model-based software systems using co-evolution.
An ontology-based knowledge base is introduced, capable of managing common, as well as system-specific knowledge relevant to security. A transformation achieves the connection of the knowledge base to the UML system model. By using semantic differences, knowledge inference, and the detection of inconsistencies in the knowledge base, context knowledge evolutions are detected.
A catalog containing rules to manage and recover security requirements uses detected context evolutions to propose potential co-evolutions to the system model which reestablish the compliance with security requirements.
S²EC²O uses security annotations to link models and executable code and provides support for run-time monitoring. The adaptation of running systems is being considered as is round-trip engineering, which integrates insights from the run time into the system model.
S²EC²O is amended by prototypical tool support. This tool is used to show S²EC²O’s applicability based on a case study targeting the medical information system iTrust.
This thesis at hand contributes to the development and maintenance of long-living software systems, regarding their security. The proposed approach will aid security experts: It detects security-relevant changes to the system context, determines the impact on the system’s security and facilitates co-evolutions to recover the compliance with the security requirements.
Data flow models in the literature are often very fine-grained, which transfers to the data flow analysis performed on them and thus leads to a decrease in the analysis' understandability. Since a data flow model, which abstracts from the majority of implementation details of the program modeled, allows for potentially easier to understand data flow analyses, this master thesis deals with the specification and construction of a highly abstracted data flow model and the application of data flow analyses on this model. The model and the analyses performed on it have been developed in a test-driven manner, so that a wide range of possible data flow scenarios could be covered. As a concrete data flow analysis, a static security check in the form of a detection of insufficient user input sanitization has been performed. To date, there's no data flow model on a similarly high level of abstraction. The proposed solution is therefore unique and facilitates developers without expertise in data flow analysis to perform such analyses.
