Maintaining requirements for long-living software systems by incorporating security knowledge

Security is an increasingly important quality facet in modern information systems and needs to be retained. Due to a constantly changing environment, long-living software systems “age” not by wearing out, but by failing to keep up-to-date with their environment. The problem is that requirements engineers usually do not have a complete overview of the security-related knowledge necessary to retain security of long-living software systems. This includes security standards, principles and guidelines as well as reported security incidents. In this paper, we focus on the identification of known vulnerabilities (and their variations) in natural-language requirements by leveraging security knowledge. For this purpose, we present an integrative security knowledge model and a heuristic method to detect vulnerabilities in requirements based on reported security incidents. To support knowledge evolution, we further propose a method based on natural language analysis to refine and to adapt security knowledge. Our evaluation indicates that the proposed assessment approach detects vulnerable requirements more reliable than other methods (Bayes, SVM, k-NN). Thus, requirements engineers can react faster and more effectively to a changing environment that has an impact on the desired security level of the information system

Towards adaptation and evolution of domain-specific knowledge for maintaining secure systems

Creating and maintaining secure software require a good understanding of the system and its environment. Knowledge management is therefore one of the key factors to maintain secure software successfully. However, acquiring and modeling knowledge is a labor-intensive and time-consuming task. Thus, knowledge ought to be shared among different projects and must be adapted to their specific needs. In this paper, we present an approach allowing the stepwise adaptation from domain-to project-specific knowledge based on OWL ontologies. For this purpose, we define a basic set of adaptation operators which allows effective and frugal changes. Moreover, we discuss how our approach can be integrated into common software process models in order to adapt knowledge required for maintenance. Since domain-and project-specific knowledge changes over time, we show how our approach copes with changes efficiently, so that the affected knowledge remains consistent. The shared use of knowledge significantly reduces the complexity and effort to model required knowledge in various projects. Our case study and tool implementation shows the benefits for maintaining secure systems

The CoCoME platform: A research note on empirical studies in information system evolution

Methods for supporting evolution of software-intensive systems are a competitive edge in software engineering as software is often operated over decades. Empirical research is useful to validate the effectiveness of these methods. However, empirical studies on software evolution are rarely comprehensive and hardly replicable. Collaboration may prevent these shortcomings. We designed CoCoMEP — a platform for supporting collaboration in empirical research on software evolution by shared knowledge. We report lessons learned from the application of the platform in a large research programme

A Minimal Set of Refactoring Rules for Object-Z

This paper presents a minimal and complete set of structural refactoring rules for the Object-Z specification language that allow for the derivation of arbitrary object-oriented architectures. The rules are equivalence preserving and work in concert with existing class refinement theory, so that any design derived using the rule set can be shown to be equivalent to, or a refinement of, the original specification

Towards Hybrid Array Types in SAC

Array programming is characterised by a formal calculus of (regular, dense) multidimensional arrays that defines the relationships between structural properties like rank and shape as well as data set sizes. Operations in the array calculus often impose certain constraints on the relationships of values or structural properties of argument arrays and guarantee certain relationships of values or structural properties of argument and result arrays. However, in all existing array programming languages these relationships are rather implicit and are neither used for static correctness guarantees nor for compiler optimisations. We propose hybrid array types to make implicit relationships between array values, both value-wise and structural, explicit. We exploit the dual nature of such relations, being requirements as well as evidence at the same time, to insert them either way into intermediate code. Aggressive partial evaluation, code optimisation and auxiliary transformations are used to prove as many explicit constraints as possible at compile time. In particular in the presence of separate compilation, however, it is unrealistic to prove all constraints. To avoid the pitfall of dependent types, where it may be hard to have any program accepted by the type system, we use hybrid types and compile unverified constraints to dynamic checks