Combating state explosion in the detection of dynamic properties of distributed computations

In the context of asynchronous distributed systems, many important applications depend on the ability to check that all observations of the execution of a distributed program, or distributed computation, satisfy a desired (or undesired) temporal evolution of states, or dynamic property. Examples include the implementation of distributed algorithms, automated testing via oracles, debugging, and building fault-tolerant applications through exception detection and handling. When a distributed program exhibits a high degree of concurrency, the number of possible observations of an execution can grow exponentially, quickly leading to an explosion in the amount of space and time required to check a dynamic property. In the worst case, detection of such properties may be defeated. This is the run-time counterpart of the well-known state explosion problem studied in model checking. In this thesis, we study the problem of state explosion as it arises in the detection of dynamic properties. In particular, we consider the potential of applying well-known techniques for dealing with state explosion from model checking to the case of dynamic property detection. Significant semantic similarities between the two problems means that there is great potential for deriving techniques for dealing with state explosion in dynamic property detection based on existing model checking techniques. However, differences between the contexts in which model checking and dynamic property detection take place mean that not all approaches to dealing with state explosion in model checking may carryover to the run-time case. We investigate these similarities and differences and provide the development and analysis of two approaches for combating state explosion in dynamic property detection based on model checking methods: on-the-fly automata theoretic model checking, and partial order reduction.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Evidence flow graph methods for validation and verification of expert systems

The results of an investigation into the use of evidence flow graph techniques for performing validation and verification of expert systems are given. A translator to convert horn-clause rule bases into evidence flow graphs, a simulation program, and methods of analysis were developed. These tools were then applied to a simple rule base which contained errors. It was found that the method was capable of identifying a variety of problems, for example that the order of presentation of input data or small changes in critical parameters could affect the output from a set of rules

Global state predicates in rough real-time

Distributed systems are characterized by the fact that the constituent processes have neither common memory nor a common system clock. These processes communicate solely via message passing. While providing a number of benefits such as increased reliability, increased computational power, and geographic dispersion, this architecture significantly complicates many of the tasks of software development and verification, including evaluation of the program state. In the case of distributed systems, the program state is comprised of the local states of the constituent processes, as well as the state of the channels between processes, and is called the global state.;With no common system clock, many distributed system protocols rely on the global ordering of local process events imposed by the message passing that occurs between processes. This leads to a partial global ordering of local process events, which can then be used to determine which process states could (or could not) have occurred simultaneously.;Traditional predicate evaluation protocols evaluate predicates on the global state of a distributed computation using consistent global states. This evaluation is complicated by the fact that the event ordering imposed by message passing is only partial. A complete history of the global states that occurred during an execution cannot always be constructed. This introduces inefficiency into predicate detection protocols and prohibits detection of certain predicates.;This dissertation explores the use of this rough global time base for global state predicate evaluation within distributed systems. By structuring the evaluation on the assumption that a global time base exists, we can develop simple and efficient protocols for both stable and unstable predicate evaluation. Further, we can evaluate certain predicates which are not easily evaluated using consistent global states. We demonstrate these advantages by developing protocols for detection of distributed termination, distributed deadlock detection, and detection of certain unstable predicates as they occur. as the global time base is rough, we can only detect unstable predicates which remain true for a sufficient duration. We additionally develop several formalizations which assist the protocol developer in dealing with the fact that the global time base is not perfect. We demonstrate the application of these formalizations within the protocols that we develop

Enhancing Usability and Explainability of Data Systems

The recent growth of data science expanded its reach to an ever-growing user base of nonexperts, increasing the need for usability, understandability, and explainability in these systems. Enhancing usability makes data systems accessible to people with different skills and backgrounds alike, leading to democratization of data systems. Furthermore, proper understanding of data and data-driven systems is necessary for the users to trust the function of the systems that learn from data. Finally, data systems should be transparent: when a data system behaves unexpectedly or malfunctions, the users deserve proper explanation of what caused the observed incident. Unfortunately, most existing data systems offer limited usability and support for explanations: these systems are usable only by experts with sound technical skills, and even expert users are hindered by the lack of transparency into the systems\u27 inner workings and functions. The aim of my thesis is to bridge the usability gap between nonexpert users and complex data systems, aid all sort of users, including the expert ones, in data and system understanding, and provide explanations that help reason about unexpected outcomes involving data systems. Specifically, my thesis has the following three goals: (1) enhancing usability of data systems for nonexperts, (2) enable data understanding that can assist users in a variety of tasks such as achieving trust in data-driven machine learning, gaining data understanding, and data cleaning, and (3) explaining causes of unexpected outcomes involving data and data systems. For enhancing usability, we focus on example-driven user intent discovery. We develop systems based on example-driven interactions in two different settings: querying relational databases and personalized document summarization. Towards data understanding, we develop a new data-profiling primitive that can characterize tuples for which a machine-learned model is likely to produce untrustworthy predictions. We also develop an explanation framework to explain causes of such untrustworthy predictions. Additionally, this new data-profiling primitive enables interactive data cleaning. Finally, we develop two explanation frameworks, tailored to provide explanations in debugging data system components, including the data itself. The explanation frameworks focus on explaining the root cause of a concurrent application\u27s intermittent failure and exposing issues in the data that cause a data-driven system to malfunction

Towards the design of efficient error detection mechanisms

The pervasive nature of modern computer systems has led to an increase in our reliance on such systems to provide correct and timely services. Moreover, as the functionality of computer systems is being increasingly defined in software, it is imperative that software be dependable. It has previously been shown that a fault intolerant software system can be made fault tolerant through the design and deployment of software mechanisms implementing abstract artefacts known as error detection mechanisms (EDMs) and error recovery mechanisms (ERMs), hence the design of these components is central to the design of dependable software systems. The EDM design problem, which relates to the construction of a boolean predicate over a set of program variables, is inherently difficult, with current approaches relying on system specifications and the experience of software engineers. As this process necessarily entails the identification and incorporation of program variables by an error detection predicate, this thesis seeks to address the EDM design problem from a novel variable-centric perspective, with the research presented supporting the thesis that, where it exists under the assumed system model, an efficient EDM consists of a set of critical variables. In particular, this research proposes (i) a metric suite that can be used to generate a relative ranking of the program variables in a software with respect to their criticality, (ii) a systematic approach for the generation of highly-efficient error detection predicates for EDMs, and (iii) an approach for dependability enhancement based on the protection of critical variables using software wrappers that implement error detection and correction predicates that are known to be efficient. This research substantiates the thesis that an efficient EDM contains a set of critical variables on the basis that (i) the proposed metric suite is able, through application of an appropriate threshold, to identify critical variables, (ii) efficient EDMs can be constructed based only on the critical variables identified by the metric suite, and (iii) the criticality of the identified variables can be shown to extend across a software module such that an efficient EDM designed for that software module should seek to determine the correctness of the identified variables