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Large-scale software exhibits periods of increased defect discovery when blocks of less thoroughly tested code are introduced into an existing codebase. For example, the mission systems schedule of software intensive government acquisition programs includes multiple overlapping software blocks associated with various capabilities. Software reliability researchers have proposed changepoint models to characterize periods of increased defect discovery. However, these models attempt to identify the location of these changepoints after testing has been performed, which is counter-intuitive because conscious decisions such as testing new functionality drive software changepoints. Existing changepoint models are therefore difficult to employ in a predictive manner. To overcome this limitation, this paper proposes a covariate software defect discovery model capable of explaining changepoints in terms of common software testing activities and metrics such as software size estimation, code coverage, and defect density. The proposed and past changepoint models are compared with respect to their predictive accuracy and computational efficiency. Our results indicate that the proposed approach is more computationally efficient and enables accurate prediction of the time needed to achieve a desired defect discovery intensity or mean time to failure despite the occurrence of changepoints during software testing.
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Metastable structures of cation vacancies in semiconducting oxides
The observed metastable characteristics of cation vacancies in Ga2O3 have prompted a wider search for such systems. In this Perspective, we consider a number of defect systems as candidates for metastability. Some of these are already known to have this property, while for others, this suggestion is new. The examples discussed here are but a sampling of a huge number of systems, and these are used to emphasize that the metastability of defect structures is both common and important; it may yield (for example) split vacancy equilibrium configurations and, hence, should be considered in developing defect models and in analyzing their properties.
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- Award ID(s):
- 1901563
- PAR ID:
- 10514093
- Publisher / Repository:
- AIP Publishing
- Date Published:
- Journal Name:
- Journal of Applied Physics
- Volume:
- 135
- Issue:
- 17
- ISSN:
- 0021-8979
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1063/5.0205665
