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In this paper, we present a novel fault injection framework for system call invocation errors, called Phoebe. Phoebe is unique as follows. First, Phoebe enables developers to have full observability of system call invocations. Second, Phoebe generates error models that are realistic in the sense that they mimic errors that naturally happen in production. Third, Phoebe is able to automatically conduct experiments to systematically assess the reliability of applications with respect to system call invocation errors in production. We evaluate the effectiveness and runtime overhead of Phoebe on two real-world applications in a production environment. The results show that Phoebe successfully generates realistic error models and is able to detect important reliability weaknesses with respect to system call invocation errors. To our knowledge, this novel concept of realistic error injection, which consists of grounding fault injection on production errors, has never been studied before.
In Spectrum-Based Fault Localization (SBFL), a suspiciousness score is assigned to each code element based on test coverage and test outcomes. The scores are then used to rank the code elements relative to each other in order to aid the programmer du
This paper presents an approach towards specifying and verifying adaptive distributed systems. We here take fault-handling as an example of adaptive behavior and propose a modeling language Sandal for describing fault-prone message-passing systems. O
Effect systems are used to statically reason about the effects an expression may have when evaluated. In the literature, such effects include various behaviours as diverse as memory accesses and exception throwing. Here we present CallE, an object-or
Empirical Standards are natural-language models of a scientific communitys expectations for a specific kind of study (e.g. a questionnaire survey). The ACM SIGSOFT Paper and Peer Review Quality Initiative generated empirical standards for research me
Building on concepts drawn from control theory, self-adaptive software handles environmental and internal uncertainties by dynamically adjusting its architecture and parameters in response to events such as workload changes and component failures. Se