At the core of interpretable machine learning is the question of whether humans are able to make accurate predictions about a model’s behavior. Assumed in this question are three properties of the interpretable output: coverage, precision, and effort. Coverage refers to how often humans think they can predict the model’s behavior, precision to how accurate humans are in those predictions, and e...

If there are many independent, identically distributed observations governed by a smooth, finite-dimensional statistical model, the Bayes estimate and the maximum likelihood estimate will be close. Furthermore, the posterior distribution of the parameter vector around the posterior mean will be close to the distribution of the maximum likelihood estimate around truth. Thus, Bayesian confidence ...

Determining and understanding contributions to a shared code base can be helpful for monitoring developer efficiency, allocating work items and understanding how and why changes were made. To support this task, we introduce a method for deriving contribution using test execution results and code coverage reports. Our visualization tool, Teamline, then presents these metrics in two different vie...

Although there are quite a few approaches to Coverage Directed test Generation aided by Machine Learning which have been applied successfully to small and medium size digital designs, it is not clear how they would scale on more elaborate industrial-level designs. This paper evaluates one of these techniques, called MicroGP, on a fully fledged industrial design. The results indicate relative su...

Forecaster perceptions of major convective outbreaks include the notion that these events occur within relatively large regions of meteorological conditions favorable for the development of significant severe weather, particularly tornadoes. With recent studies developing a rigorous and scientifically repeatable method of identifying these events and distinguishing them from intermediate or mar...

NOTE: This position paper has been coordinated among the software specialists of certification authorities from the United States, Europe, and Canada. However, it does not constitute official policy or guidance from any of the authorities. This document is provided for educational and informational purposes only and should be discussed with the appropriate certification authority when consideri...

One key step in the development of safety-critical applications is the assessment of the quality of the verification strategy. In practice, structural coverage is the methodology used to ascertain the testing campaign well satisfy a given quality criteria. In this paper, we describe the possible strategies to measure structural coverage in a DO-178B context. After evaluating state-ofthe-art app...

We present a sampling-based algorithm that generates a set of locally-optimal paths that differ in visibility.

The purpose of implementation-based testing is to gain a measure of confidence in the correctness of the software by providing adequate coverage of the code. One unit of testing in object-oriented software is a class. However, classes use other classes and if class interactions form a cycle of dependencies then, to test a client class that uses an untested supplier class, stubs must be construc...