Dieser Bericht präsentiert RACR-NET, eine Schnittstelle der Referenzattributgrammatik-gesteuerten Graphersetzungsbibliothek RACR für C#. RACR-NET ermöglicht die Nutzung der deklarativen, dynamischen Sprachspezifikations-, Instanziierungs- und Auswertungsmeachanismen der RACR Scheme-Bibliothek in der objektorientierten Programmierung. Dies umfasst insbesondere die automatische inkrementelle Auswertung attributbasierter semantischer Analysen und somit das automatische Cachen parametrisierter Funktionsmethoden. Graphersetzungen entsprechen hierbei Zustandsänderungen von Objektinstanzen und der Invalidierung abgeleiteter Berechnungen. Schwerpunkt dieses Berichts ist die objektorientierte Programmierschnittstelle von RACR-NET, dessen praktische Anwendung und Implementierung. Der Bericht ist ein Referenzhandbuch für RACR-NET Anwender und Entwickler.:1. Einleitung 1.1. Aufgabenstellung 1.2. Struktur der Arbeit 2. Konzeptionelle und technische Voraussetzungen 2.1. Überblick der RAG-gesteuerten Graphersetzung 2.2. Scheme 2.3. Die RACR Scheme-Bibliothek 2.4. Das .NET-Framework und die Common Language Infrastructure 2.5. IronScheme 3. RACR-NET Implementierung: Prozedurale Schnittstelle 3.1. Scheme in C# 3.2. RACR in C# 3.3. Anforderungsanalyse 3.4. Implementierung der prozeduralen Schnittstelle 4. RACR-NET Implementierung: Objektorientierte Schnittstelle 4.1. Überblick über die objektorientierte Schnittstelle 4.2. Anwendungsbeispiel 4.3. Herausforderungen bei der Implementierung 4.4. Implementierung 5. Evaluation 5.1. Testen der Schnittstelle 5.2. Performance-Messungen und -Vergleiche 6. Zusammenfassung und Ausblick 6.1. Eine objektorientierte Bibliothek für RAG-gesteuerte Graphersetzung 6.2. Zukünftige Arbeiten A. Literaturverzeichnis B. MIT Lizenz

This work focuses on a cognitive science inspired solution to an event detection problem in a video domain. The thesis raises the question whether video sequences that are taken in highway tunnels can be used to create meaningful data in terms of symbolic representation, and whether these symbolic representations can be used as sequences to be parsed by attribute grammars into abnormal and normal events. The main motivation of the research was to develop a novel algorithm that parses sequences of primitive events created by the image processing algorithms. The domain of the research is video detection and the special application purpose is for highway tunnels, which are critical places for abnormality detection. The method used is attribute grammars to parse the sequences. The symbolic sequences are created from a cascade of image processing algorithms such as / background subtracting, shadow reduction and object tracking. The system parses the sequences and creates alarms if a car stops, moves backwards, changes lanes, or if a person walks into the road or is in the vicinity when a car is moving along the road. These critical situations are detected using Earley&rsquo / s parser, and the system achieves real-time performance while processing the video input. This approach substantially lowers the number of false alarms created by the lower level image processing algorithms by preserving the number of detected events at a maximum. The system also achieves a high compression rate from primitive events while keeping the lost information at minimum. The output of the algorithm is measured against SVM and observed to be performing better in terms of detection and false alarm performance.

This thesis deals with fact extraction, which analyzes source code (and sometimes related artifacts) to produce extracted facts about the code. These facts may, for example, record where in the code variables are declared and where they are used, as well as related information. These extracted facts are typically used in software reverse engineering to reconstruct the design of the program. This thesis has two main parts, each of which deals with a formal approach to fact extraction. Part 1 of the thesis deals with the question: How can we demonstrate that a fact extractor actually does its job? That is, does the extractor produce the facts that it is supposed to produce? This thesis builds on the concept of semantic completeness of a fact extractor, as defined by Tom Dean et al, and further defines source, syntax and compiler completeness. One of the contributions of this thesis is to show that in particular important cases (when the extractor is deterministic and its front end is idempotent), there is an efficient algorithm to determine if the extractor is compiler complete. This result is surprising, considering that in general it is undecidable if two programs are semantically equivalent, and it would seem that source code and its corresponding extracted facts are each essentially programs that are to be proved to be equivalent or at least sufficiently similar. The larger part of the thesis, Part 2, presents Algebraic Refers-to Analysis (ARA), a new approach to fact extraction with emphasis on the Refers-to relation. ARA provides a framework for specifying fact extraction, based on a three-step pipeline: (1) basic (lexical and syntactic) extraction, (2) a normalization step and (3) a binding step. For practical programming languages, these three steps are repeated, in stages and phases, until the Refers-to relation is computed. During the writing of this thesis, ARA pipelines for C, Java, C++, Fortran, Pascal and Ada have been designed. A prototype fact extractor for the C language has been created. Validating ARA means to demonstrate that ARA pipelines satisfy the programming language standards such as ISO C++ standard. In other words, we show that ARA phases (stages and formulas) are correctly transcribed from the rules in the language standard. Comparing with the existing approaches such as Attribute Grammar, ARA has the following advantages. First, ARA formulas are concise, elegant and more importantly, insightful. As a result, we have some interesting discovery about the programming languages. Second, ARA is validated based on set theory and relational algebra, which is more reliable than exhaustive testing. Finally, ARA formulas are supported by existing software tools such as database management systems and relational calculators. Overall, the contributions of this thesis include 1) the invention of the concept of hierarchy of completeness and the automatic testing of completeness, 2) the use of the relational data model in fact extraction, 3) the invention of Algebraic Refers-to Relation Analysis (ARA) and 4) the discovery of some interesting facts of programming languages.

This thesis deals with fact extraction, which analyzes source code (and sometimes related artifacts) to produce extracted facts about the code. These facts may, for example, record where in the code variables are declared and where they are used, as well as related information. These extracted facts are typically used in software reverse engineering to reconstruct the design of the program. This thesis has two main parts, each of which deals with a formal approach to fact extraction. Part 1 of the thesis deals with the question: How can we demonstrate that a fact extractor actually does its job? That is, does the extractor produce the facts that it is supposed to produce? This thesis builds on the concept of semantic completeness of a fact extractor, as defined by Tom Dean et al, and further defines source, syntax and compiler completeness. One of the contributions of this thesis is to show that in particular important cases (when the extractor is deterministic and its front end is idempotent), there is an efficient algorithm to determine if the extractor is compiler complete. This result is surprising, considering that in general it is undecidable if two programs are semantically equivalent, and it would seem that source code and its corresponding extracted facts are each essentially programs that are to be proved to be equivalent or at least sufficiently similar. The larger part of the thesis, Part 2, presents Algebraic Refers-to Analysis (ARA), a new approach to fact extraction with emphasis on the Refers-to relation. ARA provides a framework for specifying fact extraction, based on a three-step pipeline: (1) basic (lexical and syntactic) extraction, (2) a normalization step and (3) a binding step. For practical programming languages, these three steps are repeated, in stages and phases, until the Refers-to relation is computed. During the writing of this thesis, ARA pipelines for C, Java, C++, Fortran, Pascal and Ada have been designed. A prototype fact extractor for the C language has been created. Validating ARA means to demonstrate that ARA pipelines satisfy the programming language standards such as ISO C++ standard. In other words, we show that ARA phases (stages and formulas) are correctly transcribed from the rules in the language standard. Comparing with the existing approaches such as Attribute Grammar, ARA has the following advantages. First, ARA formulas are concise, elegant and more importantly, insightful. As a result, we have some interesting discovery about the programming languages. Second, ARA is validated based on set theory and relational algebra, which is more reliable than exhaustive testing. Finally, ARA formulas are supported by existing software tools such as database management systems and relational calculators. Overall, the contributions of this thesis include 1) the invention of the concept of hierarchy of completeness and the automatic testing of completeness, 2) the use of the relational data model in fact extraction, 3) the invention of Algebraic Refers-to Relation Analysis (ARA) and 4) the discovery of some interesting facts of programming languages.

This report presents RACR, a reference attribute grammar library for the programming language Scheme. RACR supports incremental attribute evaluation in the presence of abstract syntax tree rewrites. It provides a set of functions that can be used to specify abstract syntax tree schemes and their attribution and construct respective trees, query their attributes and node information and annotate and rewrite them. Thereby, both, reference attribute grammars and rewriting, are seamlessly integrated, such that rewrites can reuse attributes and attribute values change depending on performed rewrites – a technique we call Reference Attribute Grammar Controlled Rewriting. To reevaluate attributes influenced by abstract syntax tree rewrites, a demand-driven, incremental evaluation strategy, which incorporates the actual execution paths selected at runtime for control-flows within attribute equations, is used. To realize this strategy, a dynamic attribute dependency graph is constructed throughout attribute evaluation – a technique we call Dynamic Attribute Dependency Analyses. The report illustrates RACR's motivation, features, instantiation and usage. In particular its application programming interface is documented and exemplified. The report is a reference manual for RACR developers. Further, it presents RACR’s complete implementation and therefore provides a good foundation for readers interested into the details of reference attribute grammar controlled rewriting and dynamic attribute dependency analyses.:1. Introduction 1.1. RACR is Expressive, Elegant, Ecient, Flexible and Reliable 1.2. Structure of the Manual 2. Library Overview 2.1. Architecture 2.2. Instantiation 2.3. API 3. Abstract Syntax Trees 3.1. Specification 3.2. Construction 3.3. Traversal 3.4. Node Information 4. Attribution 4.1. Specification 4.2. Evaluation and Querying 5. Rewriting 5.1. Primitive Rewrite Functions 5.2. Rewrite Strategies 6. AST Annotations 6.1. Attachment 6.2. Querying 7. Support API A. RACR Source Code B. MIT License API Index

Adaptation enables a reaction to a changing environment. For traditional software development, that means changing the design and implementation of the software in a potentially complex and expensive process. If requirements are not known until the runtime of a software system, this system must be able to cope with changes during its runtime. For this, self-adaptive systems (SAS) were created. They have internal knowledge about themselves and their environment to reason about changes and take appropriate actions. Many approaches aiming to build such systems have been published since the start of the research area at the beginning of the 21st century. However, it is difficult to find an appropriate approach, even when all requirements of a scenario the system should be built for are known. If no suitable approach can be found, software developers have to built a new system leading to high development costs and potentially inefficient solutions due to the complexity of the system. This thesis follows two goals: (1) To make approaches building SAS more comparable through a feature model describing features of SAS, and (2) to provide a novel way of specifying SAS concisely using reference attribute grammars (RAGs) providing efficient systems. RAGs originate from the research field of compiler construction and enable the concise description of parts of the internal knowledge mentioned above as well as of the computation of the actions to cope with recognised changes. To make RAGs fully usable, this thesis presents two extensions: Relational RAGs enable the efficient handling of relations required for knowledge graphs, and Connected RAGs let RAG-based system communicate with other external systems to both recognise changes and execute actions. To evaluate the novel approaches, a classification of 30 approaches for the feature model and several case studies in the areas smart home, robotics, and system orchestration were conducted. It can be shown, that significantly less code is required to specify SAS. To specify the computation, 14.5 % to 28.7 % less code was required, whereas in another case study only 6.3 % of the total code was manually written and the rest was generated. The efficiency is similar to the best comparable approaches for graph queries. Furthermore, using additional optimizations (incremental evaluation), the execution time can be shown to be faster by a factor of 167.88 less albeit being sometimes by 50.0 % slower for very small workloads and specific queries. In a more realistic, extrapolated experiment, using incremental evaluation creates speed-up factors between 6.63 and 44.93. With the contributions in this thesis, existing approaches can be selected more precisely, new approaches can classify themselves within the research area, and the development of self-adaptive systems is possible using RAG-based systems.