Deep learning based simulation for automotive software development

The automotive industry is in the midst of a new reality where software is increasingly becoming the primary tool for delivering value to customers. While this has vastly improved their product offerings, vehicle manufacturers are increasingly facing the need to continuously develop, test, and deliver functionality, while maintaining high levels of quality. One important tool for achieving this is simulation-based testing where the external operating environment of a software system is simulated, enabling incremental development with rapid test feedback. However, the traditional practice of manually specifying simulation models for complex external environments involves immense engineering effort, while remaining vulnerable to inevitable assumptions and simplifications. Exploiting the increased availability of data that captures operational environments and scenarios from the field, this work takes a deep learning approach to train models that realistically simulate external environments, significantly increasing the credibility of simulation-driven software development.\ua0First, focusing on simulating the input dependencies of automotive software functions, this work uses techniques of deep generative modeling to develop a framework for realistic test stimulus generation. Such models are trained self-supervised using recorded time-series field data and simulate the input environment much more credibly than manually specified models. With the credibility of stimulus generation being an important concern, an important concept of similarity as plausibility is introduced to evaluate the quality of generation during model training. Second, this work develops new techniques for sampling generative models that enable the controlled generation of test stimulus. Allowing testers to limit the range of scenarios considered for testing, the Metric-based Linear Interpolation (MLERP) sampling algorithm automatically chooses test stimuli that are verifiably similar to a user-supplied reference, and therefore measurably credible. While controllability eases the design of tests, credibility increases trust in the testing process. Third, recognizing that sampling may be an inefficient process for stimulus generation, this work develops a technique that extracts properties from actual code under test in order to automatically search for appropriate test stimuli within the specified range of test scenarios. Fourth, further addressing the question of credible stimulus generation, this work introduces techniques that examine training data for biases in sample representation. Overall, by taking a data-driven deep learning approach, techniques and tools developed in this work vastly expands the credibility of incremental automotive software development under simulated conditions

Corporate Governance and the Shareholder: Asymmetry, Confidence, and Decision-Making

In the decade following the ten-plus percent stockmarket collapse of 2000, regulators enacted a myriad of regulations in response to increasing angst experienced by U.S. capital market retail investors. Systemic asymmetric disclosures have fractured investor confidence prompting many commentators to characterize the relationship between Wall Street and the investment community on main street as dire. Though copious works exist on the phenomenon of corporate behaviors, especially matters of shareholder welfare, weak boards, pervious governance mechanisms, and managerial excess, current literature has revealed a dearth in corporate governance praxis specific to the question and effects of asymmetric disseminations and its principal impact on the retail/noninstitutional accredited investor\u27s (NIAI) confidence and decision-making propensities. This phenomenological study is purposed to bridging the gap between the effects of governance disclosure and the confidence and decision-making inclinations of NIAIs. Conceptual frameworks of Akerlof\u27s information theory and Verstegen Ryan and Buchholtz\u27s trust/risk decision making model undergirded the study. A nonrandom purposive sampling method was used to select 21 NIAI informants. Analysis of interview data revealed epistemological patterns/themes confirming the deleterious effects of asymmetrical disseminations on participants\u27 investment decision-making and trust behaviors. Findings may help academicians, investors, policy makers, and practitioners better comprehend the phenomenon and possibly contribute to operating efficiencies in the capital markets. Proaction and greater assertiveness in the investor/activist community may provide an impetus for continued regulatory reforms, improved transparency, and a revitalization of public trust as positive social change outcomes