Understanding the trust of software‐intensive distributed systems

Abstract

Summary An early understanding of the trust concerns while composing a distributed system from independently developed software services saves time and effort. It also allows the developer of such distributed systems to reason about the trust‐related properties of these systems. Although there are prevalent approaches for evaluating the trust of such systems, it is not clear which approach, if any, is the most comprehensive and best suited for a given situation. Moreover, there is no agreement about a unified approach, for quantifying trust, which can be applied to the entire software life‐cycle of distributed systems. This article, first, motivates the need for such a quantification of trust via a case study from the domain of indoor tracking. It then provides a comprehensive survey of current approaches that define trust, in general domains, and then focuses on the relevant approaches from the domain of software‐oriented distributed systems. These prevalent efforts are categorized into groups using existing clustering tools and then are further analyzed for their comprehensiveness. The analysis depicts: (1) many trust‐related efforts and associated models have their own constrained views of trust; (2) different trust models focus on different aspects of trust and life‐cycle details; and (3) it is difficult to interoperate across different trust models. Hence, the paper identifies a set of principles that can assist in quantifying and evaluating the trust throughout the software life‐cycle of distributed systems. These principles, then, are applied to the aforementioned case study to provide an outline of how trustworthy distributed systems can be composed from independent software services. Copyright © 2015 John Wiley & Sons, Ltd.

Disparity between the programmatic views and the user perceptions of mobile apps

“…Hammad et al [16] used FindBugs to determine which categories of bugs occurred more frequently in low rated apps rather than in high rated apps by examining the relationships between each category of bugs in an app and the corresponding app rating. In our work, we have used FindBugs to identify different categories of bugs in terms of bug ranks (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) and bug confidence levels (such as high, medium and low). The bug rank represents the severity of the bug and the confidence level indicates confidence of the tool regarding the bug existences.…”

“…To address these challenges associated with selection and ranking of apps, we need to use a holistic view about the behavior of an app. past, we have defined the trust of an app (or a software service) 1 as its conformance to its specification [3] and proposed two views of a service: an internal view or the programmatic view 2 that uses programmatic artifacts (e.g., system source code, specifications, etc. ), and an external view that uses the nonprogrammatic artifacts (e.g., user ratings and reviews in public marketplaces).…”