Preserving the Quality of Architectural Tactics in Source Code

In any complex software system, strong interdependencies exist between requirements and software architecture. Requirements drive architectural choices while also being constrained by the existing architecture and by what is economically feasible. This makes it advisable to concurrently specify the requirements, to devise and compare alternative architectural design solutions, and ultimately to make a series of design decisions in order to satisfy each of the quality concerns. Unfortunately, anecdotal evidence has shown that architectural knowledge tends to be tacit in nature, stored in the heads of people, and lost over time. Therefore, developers often lack comprehensive knowledge of underlying architectural design decisions and inadvertently degrade the quality of the architecture while performing maintenance activities. In practice, this problem can be addressed through preserving the relationships between the requirements, architectural design decisions and their implementations in the source code, and then using this information to keep developers aware of critical architectural aspects of the code. This dissertation presents a novel approach that utilizes machine learning techniques to recover and preserve the relationships between architecturally significant requirements, architectural decisions and their realizations in the implemented code. Our approach for recovering architectural decisions includes the two primary stages of training and classification. In the first stage, the classifier is trained using code snippets of different architectural decisions collected from various software systems. During this phase, the classifier learns the terms that developers typically use to implement each architectural decision. These ``indicator terms\u27\u27 represent method names, variable names, comments, or the development APIs that developers inevitably use to implement various architectural decisions. A probabilistic weight is then computed for each potential indicator term with respect to each type of architectural decision. The weight estimates how strongly an indicator term represents a specific architectural tactics/decisions. For example, a term such as \emph{pulse} is highly representative of the heartbeat tactic but occurs infrequently in the authentication. After learning the indicator terms, the classifier can compute the likelihood that any given source file implements a specific architectural decision. The classifier was evaluated through several different experiments including classical cross-validation over code snippets of 50 open source projects and on the entire source code of a large scale software system. Results showed that classifier can reliably recognize a wide range of architectural decisions. The technique introduced in this dissertation is used to develop the Archie tool suite. Archie is a plug-in for Eclipse and is designed to detect wide range of architectural design decisions in the code and to protect them from potential degradation during maintenance activities. It has several features for performing change impact analysis of architectural concerns at both the code and design level and proactively keep developers informed of underlying architectural decisions during maintenance activities. Archie is at the stage of technology transfer at the US Department of Homeland Security where it is purely used to detect and monitor security choices. Furthermore, this outcome is integrated into the Department of Homeland Security\u27s Software Assurance Market Place (SWAMP) to advance research and development of secure software systems

An Empirical Study of Security Issues Posted in Open Source Projects

When developers gain thorough understanding and knowledge of software security, they can produce more secure software. This study aims at empirically identifying and understanding the security issues posted on a random sample of GitHub repositories. We tried to understand the presence of security issues and their key themes and topics. We applied a mixed-methods approach, combining topic modeling techniques and qualitative analysis. Our findings have revealed that a) the rate of security-related issues was rather small (approx. 3% of all issues), b) the majority of the security issues were related to identity management and cryptography topics. We present 7 high-level themes of problems that developers face in implementing security features

AN EMERGING THEORY ON THE INTERACTION BETWEEN REQUIREMENTS ENGINEERING AND SYSTEMS ARCHITECTING BASED ON A SUITE OF EXPLORATORY EMPIRICAL STUDIES

Requirements Engineering and Systems Architecting are often considered the most important phases of the software development lifecycle. Because of their close proximity in the software development lifecycle, there is a high degree of interaction between these two processes. While such interaction has been recognized and researched in terms of new technology (particularly methods and tools), there is a distinct lack of empirical understanding regarding the scientific properties of this interaction. Furthermore, in Requirements Engineering and Systems Architecting, not only technical but human aspects are considered critical for the success of these processes due to these processes lying at the front-end of the development cycle and therefore being more aligned with real-world issues. Thus, the scientific properties of the interactions between Requirements Engineering and Systems Architecting can be broken down into these two key aspects. For instance, the following example research questions relate to such scientific properties: What is the impact of an existing system’s architecture on requirements decision-making? What kinds of requirements-oriented problems are encountered during architecting? What is the impact of an existing systems architecture on new requirements being elicited? What is the impact of requirements engineering knowledge on systems architecting? There is little in the literature addressing such questions. This thesis explores such issues through a suite of six exploratory empirical studies that were conducted over the last five years. Based on the observations from these studies, an emerging theory is proposed that describes the impact of human and process factors in the interaction between Requirements Engineering and Systems Architecting. The impact of this emerging body of knowledge is deemed to be on the following: technology development for Requirements Engineering and Software Architecting (methods, tools, processes, etc.); hiring and training personnel for Requirements Engineering and Systems Architecture processes in industry; Requirements Engineering and Systems Architecture project planning; curriculum improvement in academia; and future empirical research in Requirements Engineering and Systems Architecting

Architectural stability of self-adaptive software systems

This thesis studies the notion of stability in software engineering with the aim of understanding its dimensions, facets and aspects, as well as characterising it. The thesis further investigates the aspect of behavioural stability at the architectural level, as a property concerned with the architecture's capability in maintaining the achievement of expected quality of service and accommodating runtime changes, in order to delay the architecture drifting and phasing-out as a consequence of the continuous unsuccessful provision of quality requirements. The research aims to provide a systematic and methodological support for analysing, modelling, designing and evaluating architectural stability. The novelty of this research is the consideration of stability during runtime operation, by focusing on the stable provision of quality of service without violations. As the runtime dimension is associated with adaptations, the research investigates stability in the context of self-adaptive software architectures, where runtime stability is challenged by the quality of adaptation, which in turn affects the quality of service. The research evaluation focuses on the effectiveness, scale and accuracy in handling runtime dynamics, using the self-adaptive cloud architectures

Modeling Deception for Cyber Security

In the era of software-intensive, smart and connected systems, the growing power and so- phistication of cyber attacks poses increasing challenges to software security. The reactive posture of traditional security mechanisms, such as anti-virus and intrusion detection systems, has not been sufficient to combat a wide range of advanced persistent threats that currently jeopardize systems operation. To mitigate these extant threats, more ac- tive defensive approaches are necessary. Such approaches rely on the concept of actively hindering and deceiving attackers. Deceptive techniques allow for additional defense by thwarting attackers’ advances through the manipulation of their perceptions. Manipu- lation is achieved through the use of deceitful responses, feints, misdirection, and other falsehoods in a system. Of course, such deception mechanisms may result in side-effects that must be handled. Current methods for planning deception chiefly portray attempts to bridge military deception to cyber deception, providing only high-level instructions that largely ignore deception as part of the software security development life cycle. Con- sequently, little practical guidance is provided on how to engineering deception-based techniques for defense. This PhD thesis contributes with a systematic approach to specify and design cyber deception requirements, tactics, and strategies. This deception approach consists of (i) a multi-paradigm modeling for representing deception requirements, tac- tics, and strategies, (ii) a reference architecture to support the integration of deception strategies into system operation, and (iii) a method to guide engineers in deception mod- eling. A tool prototype, a case study, and an experimental evaluation show encouraging results for the application of the approach in practice. Finally, a conceptual coverage map- ping was developed to assess the expressivity of the deception modeling language created.Na era digital o crescente poder e sofisticação dos ataques cibernéticos apresenta constan- tes desafios para a segurança do software. A postura reativa dos mecanismos tradicionais de segurança, como os sistemas antivírus e de detecção de intrusão, não têm sido suficien- tes para combater a ampla gama de ameaças que comprometem a operação dos sistemas de software actuais. Para mitigar estas ameaças são necessárias abordagens ativas de defesa. Tais abordagens baseiam-se na ideia de adicionar mecanismos para enganar os adversários (do inglês deception). As técnicas de enganação (em português, "ato ou efeito de enganar, de induzir em erro; artimanha usada para iludir") contribuem para a defesa frustrando o avanço dos atacantes por manipulação das suas perceções. A manipula- ção é conseguida através de respostas enganadoras, de "fintas", ou indicações erróneas e outras falsidades adicionadas intencionalmente num sistema. É claro que esses meca- nismos de enganação podem resultar em efeitos colaterais que devem ser tratados. Os métodos atuais usados para enganar um atacante inspiram-se fundamentalmente nas técnicas da área militar, fornecendo apenas instruções de alto nível que ignoram, em grande parte, a enganação como parte do ciclo de vida do desenvolvimento de software seguro. Consequentemente, há poucas referências práticas em como gerar técnicas de defesa baseadas em enganação. Esta tese de doutoramento contribui com uma aborda- gem sistemática para especificar e desenhar requisitos, táticas e estratégias de enganação cibernéticas. Esta abordagem é composta por (i) uma modelação multi-paradigma para re- presentar requisitos, táticas e estratégias de enganação, (ii) uma arquitetura de referência para apoiar a integração de estratégias de enganação na operação dum sistema, e (iii) um método para orientar os engenheiros na modelação de enganação. Uma ferramenta protó- tipo, um estudo de caso e uma avaliação experimental mostram resultados encorajadores para a aplicação da abordagem na prática. Finalmente, a expressividade da linguagem de modelação de enganação é avaliada por um mapeamento de cobertura de conceitos

Understanding, Analysis, and Handling of Software Architecture Erosion

Architecture erosion occurs when a software system's implemented architecture diverges from the intended architecture over time. Studies show erosion impacts development, maintenance, and evolution since it accumulates imperceptibly. Identifying early symptoms like architectural smells enables managing erosion through refactoring. However, research lacks comprehensive understanding of erosion, unclear which symptoms are most common, and lacks detection methods. This thesis establishes an erosion landscape, investigates symptoms, and proposes identification approaches. A mapping study covers erosion definitions, symptoms, causes, and consequences. Key findings: 1) "Architecture erosion" is the most used term, with four perspectives on definitions and respective symptom types. 2) Technical and non-technical reasons contribute to erosion, negatively impacting quality attributes. Practitioners can advocate addressing erosion to prevent failures. 3) Detection and correction approaches are categorized, with consistency and evolution-based approaches commonly mentioned.An empirical study explores practitioner perspectives through communities, surveys, and interviews. Findings reveal associated practices like code review and tools identify symptoms, while collected measures address erosion during implementation. Studying code review comments analyzes erosion in practice. One study reveals architectural violations, duplicate functionality, and cyclic dependencies are most frequent. Symptoms decreased over time, indicating increased stability. Most were addressed after review. A second study explores violation symptoms in four projects, identifying 10 categories. Refactoring and removing code address most violations, while some are disregarded.Machine learning classifiers using pre-trained word embeddings identify violation symptoms from code reviews. Key findings: 1) SVM with word2vec achieved highest performance. 2) fastText embeddings worked well. 3) 200-dimensional embeddings outperformed 100/300-dimensional. 4) Ensemble classifier improved performance. 5) Practitioners found results valuable, confirming potential.An automated recommendation system identifies qualified reviewers for violations using similarity detection on file paths and comments. Experiments show common methods perform well, outperforming a baseline approach. Sampling techniques impact recommendation performance

Designing Human-Centered Collective Intelligence

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence