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Neil Walkinshaw - One of the best experts on this subject based on the ideXlab platform.

  • Inferring extended finite state machine models from software executions
    Empirical Software Engineering, 2016
    Co-Authors: Neil Walkinshaw, Ramsay Taylor, John Derrick
    Abstract:

    The ability to reverse-engineer models of software behaviour is valuable for a wide range of software maintenance, validation and verification tasks. Current reverse-engineering Techniques focus either on control-specific behaviour (e.g., in the form of Finite State Machines), or data-specific behaviour (e.g., as pre / post-conditions or invariants). However, typical software behaviour is usually a product of the two; models must combine both aspects to fully represent the software’s operation. Extended Finite State Machines (EFSMs) provide such a model. Although attempts have been made to infer EFSMs, these have been problematic. The models inferred by these Techniques can be non-deterministic, the Inference algorithms can be inflexible, and only applicable to traces with specific characteristics. This paper presents a novel EFSM Inference Technique that addresses the problems of inflexibility and non-determinism. It also adapts an experimental Technique from the field of Machine Learning to evaluate EFSM Inference Techniques, and applies it to three diverse software systems.

  • inferring extended finite state machine models from software executions
    Working Conference on Reverse Engineering, 2013
    Co-Authors: Neil Walkinshaw, Ramsay Taylor, John Derrick
    Abstract:

    The ability to reverse-engineer models of software behaviour is valuable for a wide range of software maintenance, validation and verification tasks. Current reverse-engineering Techniques focus either on control-specific behaviour (e.g. in the form of Finite State Machines), or data-specific behaviour (e.g. as pre/post-conditions or invariants). However, typical software behaviour is usually a product of the two; models must combine both aspects to fully represent the software's operation. Extended Finite State Machines (EFSMs) provide such a model. Although attempts have been made to infer EFSMs, these have been problematic. The models inferred by these Techniques can be non deterministic, the Inference algorithms can be inflexible, and only applicable to traces with specific characteristics. This paper presents a novel EFSM Inference Technique that addresses the problems of inflexibility and non determinism. It also adapts an experimental Technique from the field of Machine Learning to evaluate EFSM Inference Techniques, and applies it to two open-source software projects.

  • Reverse Engineering State Machines by Interactive Grammar Inference
    14th Working Conference on Reverse Engineering (WCRE 2007), 2007
    Co-Authors: Neil Walkinshaw, Kirill Bogdanov, Mike Holcombe, Sarah Salahuddin
    Abstract:

    Finite state machine-derived specifications such as X-machines, extended finite state machines and abstract state machines, are an established means to model software behaviour. They allow for comprehensive testing of an implementation in terms of its intended behaviour. In practice however they are rarely generated and maintained during software development, hence their benefits can rarely be exploited. We address this problem by using an interactive grammar Inference Technique to infer the underlying state machine representation of an existing software system. The approach is interactive because it generates queries to the user as it constructs a hypothesis machine, which can be interpreted as system tests. This paper describes (1) how an existing grammar Inference Technique (QSM) can be used to reverse-engineer state-based models of software from execution traces at a developer-defined level of abstraction and (2) how the QSM Technique can be improved for a better balance between the number of tests it proposes and the accuracy of the machine it derives. The Technique has been implemented, which has enabled us to present a small case study of its use with respect to a real software system, along with some preliminary performance results.

John Derrick - One of the best experts on this subject based on the ideXlab platform.

  • Inferring extended finite state machine models from software executions
    Empirical Software Engineering, 2016
    Co-Authors: Neil Walkinshaw, Ramsay Taylor, John Derrick
    Abstract:

    The ability to reverse-engineer models of software behaviour is valuable for a wide range of software maintenance, validation and verification tasks. Current reverse-engineering Techniques focus either on control-specific behaviour (e.g., in the form of Finite State Machines), or data-specific behaviour (e.g., as pre / post-conditions or invariants). However, typical software behaviour is usually a product of the two; models must combine both aspects to fully represent the software’s operation. Extended Finite State Machines (EFSMs) provide such a model. Although attempts have been made to infer EFSMs, these have been problematic. The models inferred by these Techniques can be non-deterministic, the Inference algorithms can be inflexible, and only applicable to traces with specific characteristics. This paper presents a novel EFSM Inference Technique that addresses the problems of inflexibility and non-determinism. It also adapts an experimental Technique from the field of Machine Learning to evaluate EFSM Inference Techniques, and applies it to three diverse software systems.

  • inferring extended finite state machine models from software executions
    Working Conference on Reverse Engineering, 2013
    Co-Authors: Neil Walkinshaw, Ramsay Taylor, John Derrick
    Abstract:

    The ability to reverse-engineer models of software behaviour is valuable for a wide range of software maintenance, validation and verification tasks. Current reverse-engineering Techniques focus either on control-specific behaviour (e.g. in the form of Finite State Machines), or data-specific behaviour (e.g. as pre/post-conditions or invariants). However, typical software behaviour is usually a product of the two; models must combine both aspects to fully represent the software's operation. Extended Finite State Machines (EFSMs) provide such a model. Although attempts have been made to infer EFSMs, these have been problematic. The models inferred by these Techniques can be non deterministic, the Inference algorithms can be inflexible, and only applicable to traces with specific characteristics. This paper presents a novel EFSM Inference Technique that addresses the problems of inflexibility and non determinism. It also adapts an experimental Technique from the field of Machine Learning to evaluate EFSM Inference Techniques, and applies it to two open-source software projects.

Sarah Salahuddin - One of the best experts on this subject based on the ideXlab platform.

  • Reverse Engineering State Machines by Interactive Grammar Inference
    14th Working Conference on Reverse Engineering (WCRE 2007), 2007
    Co-Authors: Neil Walkinshaw, Kirill Bogdanov, Mike Holcombe, Sarah Salahuddin
    Abstract:

    Finite state machine-derived specifications such as X-machines, extended finite state machines and abstract state machines, are an established means to model software behaviour. They allow for comprehensive testing of an implementation in terms of its intended behaviour. In practice however they are rarely generated and maintained during software development, hence their benefits can rarely be exploited. We address this problem by using an interactive grammar Inference Technique to infer the underlying state machine representation of an existing software system. The approach is interactive because it generates queries to the user as it constructs a hypothesis machine, which can be interpreted as system tests. This paper describes (1) how an existing grammar Inference Technique (QSM) can be used to reverse-engineer state-based models of software from execution traces at a developer-defined level of abstraction and (2) how the QSM Technique can be improved for a better balance between the number of tests it proposes and the accuracy of the machine it derives. The Technique has been implemented, which has enabled us to present a small case study of its use with respect to a real software system, along with some preliminary performance results.

Ramsay Taylor - One of the best experts on this subject based on the ideXlab platform.

  • Inferring extended finite state machine models from software executions
    Empirical Software Engineering, 2016
    Co-Authors: Neil Walkinshaw, Ramsay Taylor, John Derrick
    Abstract:

    The ability to reverse-engineer models of software behaviour is valuable for a wide range of software maintenance, validation and verification tasks. Current reverse-engineering Techniques focus either on control-specific behaviour (e.g., in the form of Finite State Machines), or data-specific behaviour (e.g., as pre / post-conditions or invariants). However, typical software behaviour is usually a product of the two; models must combine both aspects to fully represent the software’s operation. Extended Finite State Machines (EFSMs) provide such a model. Although attempts have been made to infer EFSMs, these have been problematic. The models inferred by these Techniques can be non-deterministic, the Inference algorithms can be inflexible, and only applicable to traces with specific characteristics. This paper presents a novel EFSM Inference Technique that addresses the problems of inflexibility and non-determinism. It also adapts an experimental Technique from the field of Machine Learning to evaluate EFSM Inference Techniques, and applies it to three diverse software systems.

  • inferring extended finite state machine models from software executions
    Working Conference on Reverse Engineering, 2013
    Co-Authors: Neil Walkinshaw, Ramsay Taylor, John Derrick
    Abstract:

    The ability to reverse-engineer models of software behaviour is valuable for a wide range of software maintenance, validation and verification tasks. Current reverse-engineering Techniques focus either on control-specific behaviour (e.g. in the form of Finite State Machines), or data-specific behaviour (e.g. as pre/post-conditions or invariants). However, typical software behaviour is usually a product of the two; models must combine both aspects to fully represent the software's operation. Extended Finite State Machines (EFSMs) provide such a model. Although attempts have been made to infer EFSMs, these have been problematic. The models inferred by these Techniques can be non deterministic, the Inference algorithms can be inflexible, and only applicable to traces with specific characteristics. This paper presents a novel EFSM Inference Technique that addresses the problems of inflexibility and non determinism. It also adapts an experimental Technique from the field of Machine Learning to evaluate EFSM Inference Techniques, and applies it to two open-source software projects.

Teng Joon Lim - One of the best experts on this subject based on the ideXlab platform.

  • the variational Inference approach to joint data detection and phase noise estimation in ofdm
    IEEE Transactions on Signal Processing, 2007
    Co-Authors: Darryl Dexu Lin, Teng Joon Lim
    Abstract:

    This paper studies the mitigation of phase noise (PHN) in orthogonal frequency-division multiplexing (OFDM) data detection. We present a systematic probabilistic framework that leads to both optimal and near-optimal OFDM detection schemes in the presence of unknown PHN. In contrast to the conventional approach that cancels the common (average) PHN, our aim is to jointly estimate the complete PHN sequence and the data symbol sequence. We derive a family of low-complexity OFDM detectors for this purpose. The theoretical foundation on which these detectors are based is called variational Inference, an approximate probabilistic Inference Technique associated with the minimization of variational free energy. In deriving the proposed schemes, we also point out that the expectation-maximization algorithm is a special case of the variational-Inference-based joint estimator. Further complexity reduction is obtained using the conjugate gradient (CG) method, and only a few CG iterations are needed to closely approach the ideal joint estimator output