A Flexible and Non-instrusive Approach for Computing Complex Structural Coverage Metrics

Software analysis tools and techniques often leverage structural code coverage information to reason about the dynamic behavior of software. Existing techniques instrument the code with the required structural obligations and then monitor the execution of the compiled code to report coverage. Instrumentation based approaches often incur considerable runtime overhead for complex structural coverage metrics such as Modified Condition/Decision (MC/DC). Code instrumentation, in general, has to be approached with great care to ensure it does not modify the behavior of the original code. Furthermore, instrumented code cannot be used in conjunction with other analyses that reason about the structure and semantics of the code under test. In this work, we introduce a non-intrusive preprocessing approach for computing structural coverage information. It uses a static partial evaluation of the decisions in the source code and a source-to-bytecode mapping to generate the information necessary to efficiently track structural coverage metrics during execution. Our technique is flexible; the results of the preprocessing can be used by a variety of coverage-driven software analysis tasks, including automated analyses that are not possible for instrumented code. Experimental results in the context of symbolic execution show the efficiency and flexibility of our nonintrusive approach for computing code coverage informatio

The Parametric Aircraft Noise Analysis Module - status overview and recent applications

The German Aerospace Center (DLR) is investigating aircraft noise prediction and noise reduction capabilities. The Parametric Aircraft Noise Analysis Module (PANAM) is a fast prediction tool by the DLR Institute of Aerodynamics and Flow Technology to address overall aircraft noise. It was initially developed to (1) enable comparative design studies with respect to overall aircraft ground noise and to (2) indentify promising low-noise technologies at early aircraft design stages. A brief survey of available and established fast noise prediction codes is provided in order to rank and classify PANAM among existing tools. PANAM predicts aircraft noise generated during arbitrary 3D approach and take-off flight procedures. Noise generation of an operating aircraft is determined by its design, the relative observer position, configuration settings, and operating condition along the flight path. Feasible noise analysis requires a detailed simulation of all these dominating effects. Major aircraft noise components are simulated with individual models and interactions are neglected. Each component is simulated with a separate semi-empirical and parametric noise source model. These models capture major physical effects and correlations yet allow for fast and accurate noise prediction. Sound propagation and convection effects are applied to the emitting noise source in order to transfer static emission into aircraft ground noise impact with respect to the actual flight operating conditions. Recent developments and process interfaces are presented and prediction results are compared with experimental data recorded during DLR flyover noise campaigns with an Airbus A319 (2006), a VFW-614 (2009), and a Boeing B737-700 (2010). Overall, dominating airframe and engine noise sources are adequately modeled and overall aircraft ground noise levels can sufficiently be predicted. The paper concludes with a brief overview on current code applications towards selected noise reduction technologies

brainlife.io: A decentralized and open source cloud platform to support neuroscience research

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research

How Scale Affects Structure in Java Programs

Many internal software metrics and external quality attributes of Java programs correlate strongly with program size. This knowledge has been used pervasively in quantitative studies of software through practices such as normalization on size metrics. This paper reports size-related super- and sublinear effects that have not been known before. Findings obtained on a very large collection of Java programs -- 30,911 projects hosted at Google Code as of Summer 2011 -- unveils how certain characteristics of programs vary disproportionately with program size, sometimes even non-monotonically. Many of the specific parameters of nonlinear relations are reported. This result gives further insights for the differences of "programming in the small" vs. "programming in the large." The reported findings carry important consequences for OO software metrics, and software research in general: metrics that have been known to correlate with size can now be properly normalized so that all the information that is left in them is size-independent.Comment: ACM Conference on Object-Oriented Programming, Systems, Languages and Applications (OOPSLA), October 2015. (Preprint

Py-Feat: Python Facial Expression Analysis Toolbox

Studying facial expressions is a notoriously difficult endeavor. Recent advances in the field of affective computing have yielded impressive progress in automatically detecting facial expressions from pictures and videos. However, much of this work has yet to be widely disseminated in social science domains such as psychology. Current state of the art models require considerable domain expertise that is not traditionally incorporated into social science training programs. Furthermore, there is a notable absence of user-friendly and open-source software that provides a comprehensive set of tools and functions that support facial expression research. In this paper, we introduce Py-Feat, an open-source Python toolbox that provides support for detecting, preprocessing, analyzing, and visualizing facial expression data. Py-Feat makes it easy for domain experts to disseminate and benchmark computer vision models and also for end users to quickly process, analyze, and visualize face expression data. We hope this platform will facilitate increased use of facial expression data in human behavior research.Comment: 25 pages, 3 figures, 5 table