Gary R. Wilson

A graph-based approach to API usage adaptation

Reusing existing library components is essential for reducing the cost of software development and maintenance. When library components evolve to accommodate new feature requests, to fix bugs, or to meet new standards, the clients of software libraries often need to make corresponding changes to correctly use the updated libraries. Existing API usage adaptation techniques support simple adaptation such as replacing the target of calls to a deprecated API, however, cannot handle complex adaptations such as creating a new object to be passed to a different API method, or adding an exception handling logic that surrounds the updated API method calls. This paper presents LIBSYNC that guides developers in adapting API usage code by learning complex API usage adaptation patterns from other clients that already migrated to a new library version (and also from the API usages within the librarys test code). LIBSYNC uses several graph-based techniques (1) to identify changes to API declarations by comparing two library versions, (2) to extract associated API usage skeletons before and after library migration, and (3) to compare the extracted API usage skeletons to recover API usage adaptation patterns. Using the learned adaptation patterns, LIBSYNC recommends the locations and edit operations for adapting API usages. The evaluation of LIBSYNC on real-world software systems shows that it is highly correct and useful with a precision of 100% and a recall of 91%.

Nonlinear and non‐Gaussian ocean noise

Bispectral analysis is a statistical tool for detecting and identifying a nonlinear stochastic signal generating mechanism from data containing its output. Bispectral analysis can also be employed to investigate whether the observed data record is consistent with the hypothesis that the underlying stochastic process has Gaussian distribution. From estimates of bispectra of several records of ambient acoustic ocean noise, a newly developed statistical method for testing whether the noise had a Gaussian distribution, and whether it contains evidence of nonlinearity in the underlying mechanisms generating the observed noise is applied. Seven acoustic records from three environments are examined: the Atlantic south of Bermuda, the northeast Pacific, and the Indian Ocean. The collection of time series represents both ambient acoustic noise (no local shipping) and noise dominated by local shipping. The three ambient records appeared to be both linear and Gaussian processes when examined over a period on the ord...

Detection of non-Gaussian signals in non-Gaussian noise using the bispectrum

The problem of detecting a non-Gaussian time series in the presence of additive Gaussian or non-Gaussian noise is cast into a classical hypothesis testing framework, using the sample bispectrum as the test statistic. The power of the test is demonstrated as a function of signal-to-noise ratio, the degree of skewness of the signal, and processing parameters. The results are compared to the power of a classical energy detection test. It is concluded that the bispectrum can be used effectively to detect non-Gaussian signals in the presence of interfering noise and that it may perform better, depending on the degree of non-Gaussianity, than energy detection. >

Vector sensors and vector sensor line arrays: Comments on optimal array gain and detection

This paper examines array gain and detection performance of single vector sensors and vector sensor line arrays, with focus on the impact of nonacoustic self noise and finite spatial coherence of the noise between the vector sensor components. Analytical results based on maximizing the directivity index show that the particle motion channels should always be included in the processing for optimal detection, regardless of self noise level, as long as the self noise levels are taken into account. The vector properties of acoustic intensity can be used to estimate the levels of nonacoustic noise in ocean measurements. Application of conventional, minimum variance distortionless response, and white-noise-constrained adaptive beamforming methods with ocean acoustic data collected by a single vector sensor illustrate an increase in spatial resolution but a corresponding decrease in beamformer output with increasing beamformer adaptivity. Expressions for the spatial coherence of all pairs of vector sensor compon...

Matched mode localization

In this article, a method of passively localizing a narrow‐band source in range and depth in a waveguide is presented based on ‘‘matching’’ predicted normal mode amplitudes to measured mode amplitudes. The modes are measured by using a vertical array of hydrophones and performing mode filtering. Previous studies of mode filtering have considered only the overdetermined case, i.e., where there are more hydrophones than discrete modes present in the waveguide. In this study, mode filtering is considered for the underdetermined case, i.e., where there are fewer hydrophones than the total number of discrete modes in the waveguide, but only a subset of the total number of modes is to be estimated. Previous studies of matched field localization have been based on matching the entire pressure field. In this study, the pressure field is expressed in terms of normal modes, and only a subset of the total number of modes is used for localization. Using a subset of modes allows trade‐offs to be made between localizat...

Time delay estimation using the cross bispectrum

The cross bispectrum phase can be effectively used to estimate the time required for a nonGaussian signal to propagate between a pair of spatially separated sensors in the presence of highly correlated Gaussian noise. The authors present a consistent estimator of the phase of the cross bispectrum, derive the exact distribution of the phase of a complex Gaussian sample bispectrum, and show that in most cases the exact distribution can be approximated by a Gaussian distribution. Using this Gaussian approximation, the authors derive the variance of the time delay estimate computed from the sample cross bispectrum of a signal in additive correlated noise. These results allow the performance of time delay estimators based on the cross bispectrum phase to be quantified as a function of the sample size, the skewness of the signal, the signal-to-noise ratio (SNR), and the noise correlation. >

Identifying and Summarizing Systematic Code Changes via Rule Inference

Programmers often need to reason about how a program evolved between two or more program versions. Reasoning about program changes is challenging as there is a significant gap between how programmers think about changes and how existing program differencing tools represent such changes. For example, even though modification of a locking protocol is conceptually simple and systematic at a code level, diff extracts scattered text additions and deletions per file. To enable programmers to reason about program differences at a high level, this paper proposes a rule-based program differencing approach that automatically discovers and represents systematic changes as logic rules. To demonstrate the viability of this approach, we instantiated this approach at two different abstraction levels in Java: first at the level of application programming interface (API) names and signatures, and second at the level of code elements (e.g., types, methods, and fields) and structural dependences (e.g., method-calls, field-accesses, and subtyping relationships). The benefit of this approach is demonstrated through its application to several open source projects as well as a focus group study with professional software engineers from a large e-commerce company.

CLASS A MODELING OF OCEAN ACOUSTIC NOISE PROCESSES

Previous work has shown that some ocean acoustic noise processes can be represented as Class A noise. Likelihood ratio and threshold detectors have been developed to detect signals in the presence of Class A noise. The performance of these detectors is significantly affected by the accuracy with which the parameters of the Class A noise can be estimated. This paper presents two methods of estimating the Class A parameters, a minimum distance method and a maximum likelihood method. These methods are compared to a previously developed method using estimates of the moments of the noise process and are generally found to be superior estimators.

Probability density estimates of surface and bottom reverberation

Selected surface reverberation and bottom reverberation returns were used to compute estimates of the probability density function of the instantaneous reverberation. To estimate the densities, 6500 samples of surface reverberation and 3078 samples of bottom reverberation were used. The collections of samples were tested for randomness, independence, homogeneity, and normality. Both the surface and bottom reverberation were found to be non‐Gaussian. Kernel density estimation techniques were applied to the collections of samples to provide univariate estimates of the densities. The densities were seen to be nearly Gaussian, but with heavier tails. Heavier tailed densities generally result in higher false alarm rates for detectors designed for a Gaussian noise process.

BISPECTRAL CHARACTERIZATION OF OCEAN ACOUSTIC TIME SERIES; NONLINEARITY AND NON-GAUSSIANITY

Previous research into the Gaussianity of ocean acoustical time series has examined univariate marginal densities. In this paper we present research which examines this issue from a time series point of view. Even series which previously passed univariate tests for normality are shown to be non-Gaussian time series. Additionally, these time series are shown to be nonlinear time series, so that such acoustical series must be modeled in a nonlinear fashion.