Biblio

Research shows that commonly accepted security requirements are not generally applied in practice. Instead of relying on requirements checklists, security experts rely on their expertise and background knowledge to identify security vulnerabilities. To understand the gap between available checklists and practice, we conducted a series of interviews to encode the decision-making process of security experts and novices during security requirements analysis. Participants were asked to analyze two types of artifacts: source code, and network diagrams for vulnerabilities and to apply a requirements checklist to mitigate some of those vulnerabilities. We framed our study using Situation Awareness-a cognitive theory from psychology-to elicit responses that we later analyzed using coding theory and grounded analysis. We report our preliminary results of analyzing two interviews that reveal possible decision-making patterns that could characterize how analysts perceive, comprehend and project future threats which leads them to decide upon requirements and their specifications, in addition, to how experts use assumptions to overcome ambiguity in specifications. Our goal is to build a model that researchers can use to evaluate their security requirements methods against how experts transition through different situation awareness levels in their decision-making process.

Self-adaptive systems have the ability to adapt their behavior to dynamic operation conditions. In reaction to changes in the environment, these systems determine the appropriate corrective actions based in part on information about which action will have the best impact on the system. Existing models used to describe the impact of adaptations are either unable to capture the underlying uncertainty and variability of such dynamic environments, or are not compositional and described at a level of abstraction too low to scale in terms of specification effort required for non-trivial systems. In this paper, we address these shortcomings by describing an approach to the specification of impact models based on architectural system descriptions, which at the same time allows us to represent both variability and uncertainty in the outcome of adaptations, hence improving the selection of the best corrective action. The core of our approach is an impact model language equipped with a formal semantics defined in terms of Discrete Time Markov Chains. To validate our approach, we show how employing our language can improve the accuracy of predictions used for decisionmaking in the Rainbow framework for architecture-based self-adaptation. 

Software architects design systems to achieve quality attributes like security, reliability, and performance. Key to achieving these quality attributes are design constraints governing how components of the system are configured, communicate and access resources. Unfortunately, identifying, specifying, communicating and enforcing important design constraints – achieving architectural control – can be difficult, particularly in large software systems. We argue for the development of architectural frameworks, built to leverage language mechanisms that provide for domain-specific syntax, editor services and explicit control over capabilities, that help increase architectural control. In particular, we argue for concise, centralized architectural descriptions which are responsible for specifying constraints and passing a minimal set of capabilities to downstream system components, or explicitly entrusting them to individuals playing defined roles within a team. By integrating these architectural descriptions directly into the language, the type system can help enforce technical constraints and editor services can help enforce social constraints. We sketch our approach in the context of distributed systems. 

Security requirements patterns represent reusable security practices that software engineers can apply to improve security in their system. Reusing best practices that others have employed could have a number of benefits, such as decreasing the time spent in the requirements elicitation process or improving the quality of the product by reducing product failure risk. Pattern selection can be difficult due to the diversity of applicable patterns from which an analyst has to choose. The challenge is that identifying the most appropriate pattern for a situation can be cumbersome and time-consuming. We propose a new method that combines an inquiry-cycle based approach with the feature diagram notation to review only relevant patterns and quickly select the most appropriate patterns for the situation. Similar to patterns themselves, our approach captures expert knowledge to relate patterns based on decisions made by the pattern user. The resulting pattern hierarchies allow users to be guided through these decisions by questions, which introduce related patterns in order to help the pattern user select the most appropriate patterns for their situation, thus resulting in better requirement generation. We evaluate our approach using access control patterns in a pattern user study.

The use of shared mutable state, commonly seen in object-oriented systems, is often problematic due to the potential conflicting interactions between aliases to the same state. We present a substructural type system outfitted with a novel lightweight interference control mechanism, rely-guarantee protocols, that enables controlled aliasing of shared resources. By assigning each alias separate roles, encoded in a novel protocol abstraction in the spirit of rely-guarantee reasoning, our type system ensures that challenging uses of shared state will never interfere in an unsafe fashion. In particular, rely-guarantee protocols ensure that each alias will never observe an unexpected value, or type, when inspecting shared memory regardless of how the changes to that shared state (originating from potentially unknown program contexts) are interleaved at run-time.

Programming languages often include specialized syntax for common datatypes e.g. lists and some also build in support for specific specialized datatypes e.g. regular expressions, but user-defined types must use general-purpose syntax. Frustration with this causes developers to use strings, rather than structured data, with alarming frequency, leading to correctness, performance, security, and usability issues. Allowing library providers to modularly extend a language with new syntax could help address these issues. Unfortunately, prior mechanisms either limit expressiveness or are not safely composable: individually unambiguous extensions can still cause ambiguities when used together. We introduce type-specific languages TSLs: logic associated with a type that determines how the bodies of generic literals, able to contain arbitrary syntax, are parsed and elaborated, hygienically. The TSL for a type is invoked only when a literal appears where a term of that type is expected, guaranteeing non-interference. We give evidence supporting the applicability of this approach and formally specify it with a bidirectionally typed elaboration semantics for the Wyvern programming language.

To evolve object-oriented code, one must understand both the code structure in terms of classes, and the runtime structure in terms of abstractions of objects that are being created and relations between those objects. To help with this understanding, static program analysis can extract heap abstractions such as object graphs. But the extracted graphs can become too large if they do not sufficiently abstract objects, or too imprecise if they abstract objects excessively to the point of being similar to a class diagram, where one box for a class represents all the instances of that class. One previously proposed solution uses both annotations and abstract interpretation to extract a global, hierarchical, abstract object graph that conveys both abstraction and design intent, but can still be related to the code structure. In this paper, we define metrics that relate nodes and edges in the object graph to elements in the code structure, to measure how they differ, and if the differences are indicative of language or design features such as encapsulation, polymorphism and inheritance. We compute the metrics across eight systems totaling over 100 KLOC, and show a statistically significant difference between the code and the object graph. In several cases, the magnitude of this difference is large.

To evolve object-oriented code, one must understand both the code structure in terms of classes, and the runtime structure in terms of abstractions of objects that are being created and relations between those objects. To help with this understanding, static program analysis can extract heap abstractions such as object graphs. But the extracted graphs can become too large if they do not sufficiently abstract objects, or too imprecise if they abstract objects excessively to the point of being similar to a class diagram that shows one box for a class to represent all the instances of that class. One previously proposed solution uses both annotations and abstract interpretation to extract a global, hierarchical, abstract object graph that conveys both abstraction and design intent, but can still be related to the code structure. In this paper, we define metrics that relate nodes and edges in the object graph to elements in the code structure to measure how they differ, and if the differences are indicative of language or design features such as encapsulation, polymorphism and inheritance. We compute the metrics across eight systems totaling over 100 KLOC, and show a statistically significant difference between the code and the object graph. In several cases, the magnitude of this difference is large.