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Register a new userMaximizing Error Injection Realism for Chaos Engineering with System Calls
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Added by
Long Zhang
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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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.
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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 during the debugging process when seeking the source of a fault. However, probably none of the known SBFL formulae are guaranteed to produce different scores for all the program elements, hence ties emerge between the code elements. Based on our experiments, ties in SBFL are prevalent: in Defects4J, 54-56% of buggy methods are members of ties, i.e., there is at least one other method with the same score in these cases (but typically much more, on average 6), and this inevitably reduces the effectiveness of any SBFL approach. In this work, we present a technique to break ties in such cases based on the so-called method calls frequencies. This counts the number of different contexts of method calls (both as callees and as callers) in failing test cases. The intuition is that if a method appears in many different calling contexts during a failing test case, it will be more suspicious and get a higher rank position compared to other methods with the same scores. This method can be applied to any underlying SBFL formula, and can favourably break the occurring ranks in the ties in many cases. The experimental results show that our novel tie-breaking strategy achieved a significant reduction in both size and number of critical ties in our benchmark. In 72-73% of the cases, the ties were completely eliminated and the average reduction rate was more than 80%.
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. One of the unique mechanisms of the language is a linguistic support for abstracting typical faults such as unexpected termination of processes and random loss of messages. The Sandal compiler translates a model into a set of NuSMV modules. During the compilation process, faults specified in the model will be woven into the output. One can thus enjoy full-automatic exhaustive fault-injection without writing faulty behaviors explicitly. We demonstrate the advantage of the language by verifying a model of the two-phase commit protocol under faulty environment.
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-oriented language that takes a flexible approach where effects are just method calls: this works well because ordinary methods often model things like I/O operations, access to global state, or primitive language operations such as thread creation. CallE supports both flexible and fine-grained control over such behaviour, in a way designed to minimise the complexity of annotations. CallEs effect system can be used to prevent OO code from performing privileged operations, such as querying a database, modifying GUI widgets, exiting the program, or performing network communication. It can also be used to ensure determinism, by preventing methods from (indirectly) calling non-deterministic primitives like random number generation or file reading.
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 methods commonly used in software engineering. These living documents, which should be continuously revised to reflect evolving consensus around research best practices, will improve research quality and make peer review more effective, reliable, transparent and fair.
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. Self-adaptive software is increasingly expected to meet strict functional and non-functional requirements in applications from areas as diverse as manufacturing, healthcare and finance. To address this need, we introduce a methodology for the systematic ENgineering of TRUstworthy Self-adaptive sofTware (ENTRUST). ENTRUST uses a combination of (1) design-time and runtime modelling and verification, and (2) industry-adopted assurance processes to develop trustworthy self-adaptive software and assurance cases arguing the suitability of the software for its intended application. To evaluate the effectiveness of our methodology, we present a tool-supported instance of ENTRUST and its use to develop proof-of-concept self-adaptive software for embedded and service-based systems from the oceanic monitoring and e-finance domains, respectively. The experimental results show that ENTRUST can be used to engineer self-adaptive software systems in different application domains and to generate dynamic assurance cases for these systems.
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