Variability provides the ability to adapt and customize a software system's artifacts for a particular context or circumstance. Variability enables code reuse, but its mechanisms are often tangled within a software artifact or scattered over multiple artifacts. This makes the system harder to maintain for developers, and harder to understand for users that configure the software. Feature models provide a centralized source for describing the variability in a software system. A feature model consists of a hierarchy of features—the common and variable system characteristics—with constraints between features. Constructing a feature model, however, is a arduous and time-consuming manual process. We developed two techniques for feature model synthesis. The first, Feature-Graph-Extraction, is an automated algorithm for extracting a feature graph from a propositional formula in either conjunctive normal form (CNF), or disjunctive normal form (DNF). A feature graph describes all feature diagrams that are complete with respect to the input. We evaluated our algorithms against related synthesis algorithms and found that our CNF variant was significantly faster than the previous comparable technique, and the DNF algorithm performed similarly to a comparable, but newer technique, with the exception of several models where our algorithm was faster. The second, Feature-Tree-Synthesis, is a semi-automated technique for building a feature model given a feature graph. This technique uses both logical constraints and text to address the most challenging part of feature model synthesis—constructing the feature hierarchy—by ranking potential parents of a feature with a textual similarity heuristic. We found that the procedure effectively reduced a modeler's choices from thousands, to five or less when synthesizing the Linux and eCos variability models. Our third contribution is the analysis of Kconfig—a language similar to feature modeling used to specify the variability model of the Linux kernel. While large feature models are reportedly used in industry, these models have not been available to the research community for benchmarking feature model analysis and synthesis techniques. We compare Kconfig to feature modeling, reverse engineer formal semantics, and translate 12 open-source Kconfig models—including the Linux model with over 6000 features—to propositional logic.

Many software development projects start with an existing code base that has to be tightly integrated into a new system. In order to make a robust system that will achieve the desired business goals, developers must be able to understand the architecture of the old code base and its rationale. This thesis presents a lightweight approach for the recovery of software architecture. The main goal of the approach is to provide an efficient way for architecture recovery that works on small to mid-sized software systems, and gives a useful starting point on large ones. The emphasis of the process is on the use of well established development techniques and tools, in order to minimize adoption costs and maximize the return on investment.

A goal of runtime monitoring is to observe software execution to determine whether it complies with its intended behavior. Monitoring allows one to analyze and recover from detected faults, providing prevention activities against catastrophic failure. Although runtime monitoring has been in use for so many years, there is renewed interest in its application largely because of the increasing complexity and ubiquitous nature of software systems. To address such a demand for runtime monitoring and behavioral analysis of software systems, we present RUMBA framework. It utilizes a synergy between static and dynamic analyses to evaluate whether a program behavior complies with specified properties during its execution. The framework is comprised of three steps, namely: i) Extracting Architecture where reverse engineering techniques are used to extract two meta-models of a Java system by utilizing UML-compliant and graph representations of the system model, ii) Seeding Objectives in which information required for filtering runtime events is obtained based on properties that are defined in OCL (Object Constraint Language) as specifications for the behavioral analysis, and iii) Runtime Monitoring and Analysis where behavior of the system is monitored according to the output of the previous stages, and then is analyzed based on the objective properties. The first and the second stages are static while the third one is dynamic. A prototype of our framework has been developed in Java programming language. We have performed a set of empirical studies on the proposed framework to assess the techniques introduced in this thesis. We have also evaluated the efficiency of the RUMBA framework in terms of processor and memory utilization for the case study applications.

<p>Software systems have grown larger and more complex in recent years. Generative software development strives to automate software development from a systems family by generating implementations using domain-specific languages. In current practice, specifying domain-specific languages is a manual task requiring expert analysis of multiple information sources. Furthermore, the concepts and relations represented in a language are grown through its usage. Keeping the language consistent with its usage is a time-consuming process requiring manual comparison between the language instances and its language specification. Feature model mining addresses these issues by synthesizing a representative model bottom-up from a sample set of instances called configurations.</p> <p>This thesis presents a mining algorithm that reverse-engineers a probabilistic feature model from a set of individual configurations. A configuration consists of a list of features that are defined as system properties that a stakeholder is interested in. Probabilistic expressions are retrieved from the sample configurations through the use of conjunctive and disjunctive association rule mining. These expressions are used to construct a probabilistic feature model. </p> <p>The mined feature model consists of a hierarchy of features, a set of additional hard constraints and soft constraints. The hierarchy describes the dependencies and alternative relations exhibited among the features. The additional hard constraints are a set of propositional formulas which must be satisfied in a legal configuration. Soft constraints describe likely defaults or common patterns.</p> <p>Systems families are often realized using object-oriented frameworks that provide reusable designs for constructing a family of applications. The mining algorithm is evaluated on a set of applications to retrieve a metamodel of the Java Applet framework. The feature model is then applied to the development of framework-specific modeling languages (FSMLs). FSMLs are domain-specific languages that model the framework-provided concepts and their rules for development.</p> <p>The work presented in this thesis provides the foundation for further research in feature model mining. The strengths and weaknesses of the algorithm are analyzed and the thesis concludes with a discussion of possible extensions.</p>

An object-oriented software system is composed of a collection of communicating objects that co-operate with one another to achieve some desired goals. The object is the basic unit of abstraction in an OO program; objects may model real-world entities or internal abstractions of the system. Similar objects forms classes, which encapsulate the data and operations performed on the data. Therefore, extracting, analyzing, and modelling classes/objects and their relationships is of key importance in understanding and maintaining object-oriented software systems. However, when dealing with large and complex object-oriented systems, maintainers can easily be overwhelmed by the vast number of classes/objects and the high degree of interdependencies among them. In this thesis, we propose a new model, which we call the Hybrid Model, to represent object-oriented systems at a coarse-grained level of abstraction. To promote the comprehensibility of objects as independent units, we group the complete static description of software objects into aggregate components. Each aggregate component logically represents a set of objects, and the components interact with one other through explicitly defined ports. We present and discuss several applications of the Hybrid Model in reverse engineering and software evolution. The Hybrid Model can be used to support a divide-and-conquer comprehension strategy for program comprehension. At a low level of abstraction, maintainers can focus on one aggregate-component at a time, while at a higher level, each aggregate component can be understood as a whole and be mapped to coarse-grained design abstractions, such as subsystems. Based on the new model, we further propose a set of dependency analysis methods. The analysis results reveal the external properties of aggregate components, and lead to better understand the nature of their interdependencies. In addition, we apply the new model in software evolution analysis. We identify a collection of change patterns in terms of changes in aggregate components and their interrelationships. These patterns help to interpret how an evolving system changes at the architectural level, and provides valuable information to understand why the system is designed as the way it is.

Many software development projects start with an existing code base that has to be tightly integrated into a new system. In order to make a robust system that will achieve the desired business goals, developers must be able to understand the architecture of the old code base and its rationale. This thesis presents a lightweight approach for the recovery of software architecture. The main goal of the approach is to provide an efficient way for architecture recovery that works on small to mid-sized software systems, and gives a useful starting point on large ones. The emphasis of the process is on the use of well established development techniques and tools, in order to minimize adoption costs and maximize the return on investment.

<p>Software systems have grown larger and more complex in recent years. Generative software development strives to automate software development from a systems family by generating implementations using domain-specific languages. In current practice, specifying domain-specific languages is a manual task requiring expert analysis of multiple information sources. Furthermore, the concepts and relations represented in a language are grown through its usage. Keeping the language consistent with its usage is a time-consuming process requiring manual comparison between the language instances and its language specification. Feature model mining addresses these issues by synthesizing a representative model bottom-up from a sample set of instances called configurations.</p> <p>This thesis presents a mining algorithm that reverse-engineers a probabilistic feature model from a set of individual configurations. A configuration consists of a list of features that are defined as system properties that a stakeholder is interested in. Probabilistic expressions are retrieved from the sample configurations through the use of conjunctive and disjunctive association rule mining. These expressions are used to construct a probabilistic feature model. </p> <p>The mined feature model consists of a hierarchy of features, a set of additional hard constraints and soft constraints. The hierarchy describes the dependencies and alternative relations exhibited among the features. The additional hard constraints are a set of propositional formulas which must be satisfied in a legal configuration. Soft constraints describe likely defaults or common patterns.</p> <p>Systems families are often realized using object-oriented frameworks that provide reusable designs for constructing a family of applications. The mining algorithm is evaluated on a set of applications to retrieve a metamodel of the Java Applet framework. The feature model is then applied to the development of framework-specific modeling languages (FSMLs). FSMLs are domain-specific languages that model the framework-provided concepts and their rules for development.</p> <p>The work presented in this thesis provides the foundation for further research in feature model mining. The strengths and weaknesses of the algorithm are analyzed and the thesis concludes with a discussion of possible extensions.</p>

An object-oriented software system is composed of a collection of communicating objects that co-operate with one another to achieve some desired goals. The object is the basic unit of abstraction in an OO program; objects may model real-world entities or internal abstractions of the system. Similar objects forms classes, which encapsulate the data and operations performed on the data. Therefore, extracting, analyzing, and modelling classes/objects and their relationships is of key importance in understanding and maintaining object-oriented software systems. However, when dealing with large and complex object-oriented systems, maintainers can easily be overwhelmed by the vast number of classes/objects and the high degree of interdependencies among them. In this thesis, we propose a new model, which we call the Hybrid Model, to represent object-oriented systems at a coarse-grained level of abstraction. To promote the comprehensibility of objects as independent units, we group the complete static description of software objects into aggregate components. Each aggregate component logically represents a set of objects, and the components interact with one other through explicitly defined ports. We present and discuss several applications of the Hybrid Model in reverse engineering and software evolution. The Hybrid Model can be used to support a divide-and-conquer comprehension strategy for program comprehension. At a low level of abstraction, maintainers can focus on one aggregate-component at a time, while at a higher level, each aggregate component can be understood as a whole and be mapped to coarse-grained design abstractions, such as subsystems. Based on the new model, we further propose a set of dependency analysis methods. The analysis results reveal the external properties of aggregate components, and lead to better understand the nature of their interdependencies. In addition, we apply the new model in software evolution analysis. We identify a collection of change patterns in terms of changes in aggregate components and their interrelationships. These patterns help to interpret how an evolving system changes at the architectural level, and provides valuable information to understand why the system is designed as the way it is.

This dissertation describes efficient design of tamper-resistant secure processor and cryptographic memory protection model that will strength security of a computing system. The thesis proposes certain cryptographic and security features integrated into the general purpose processor and computing platform to protect confidentiality and integrity of digital content stored in a computing system's memory. System designers can take advantages of the availability of the proposed security model to build future security systems such as systems with strong anti-reverse engineering capability, digital content protection system, or trusted computing system with strong tamper-proof protection. The thesis explores architecture level optimizations and design trade-offs for supporting high performance tamper-resistant memory model and micro-processor architecture. It expands the research of the previous studies on tamper-resistant processor design on several fronts. It offers some new architecture and design optimization techniques to further reduce the overhead of memory protection over the previous approaches documented in the literature. Those techniques include prediction based memory decryption and efficient memory integrity verification approaches. It compares different encryption modes applicable to memory protection and evaluates their pros and cons. In addition, the thesis tries to solve some of the security issues that have been largely ignored in the prior art. It presents a detailed investigation of how to integrate confidentiality protection and integrity protection into the out-of-order processor architecture both efficiently and securely. Furthermore, the thesis also expands the coverage of protection from single processor to multi-processor.

Understanding the mechanisms of gene regulation in the field of systems biology is a very important issue. With the development of bio-information technology, we can capture large quantities of gene¡¦s expression data from DNA microarray data. In order to discover the relationship of gene regulation, the simulation of gene regulatory networks have been proposed. Among these simulations methods, the S-system model is the most widely used in non-linear differential equations. It can simulate the dynamic behavior of gene regulatory networks and gene expression, but can¡¦t explain the structure and orientation of gene regulatory networks. Therefore, we propose a Boolean knowledge-based approach to assist the S-system modeling of gene regulatory networks. In this study, we derive the positive and negative regulatory relationships between genes from the regulation of S-system parameters, and use the structure of Boolean networks as our knowledge base. According to the results of the experiment, we can verify our assumptions for the regulation of the S-system parameters, and also has a better understanding of the regulatory relationship between genes.