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This paper presents a dynamic technique for statistically estimating three p r ogram characteristics that a ect a program's computational behavior: 1 the probability that a particular section of a program is executed, 2 the probability that the particular section a ects the data state, and 3 the probability that a data state produced by that section has an e ect on program output. These three characteristics can be used to predict whether faults are likely to be uncovered by software testing.

Blockchain-enabled e-voting (BEV) could reduce voter fraud and increase voter access. Eligible voters cast a ballot anonymously using a computer or smartphone. BEV uses an encrypted key and tamper-proof personal IDs. This article highlights some BEV implementations and the approach's potential benefits and challenges

Software veri cation encompasses a wide range of techniques and activities that are geared towards demonstrating that software is reliable. Veri cation techniques such as testing provide a way t o assess the likelihood that software will fail during use. This paper introduces a di erent t y p e o f v eri cation that shows how l i k ely it is that an incorrect program will not fail. Our veri cation applies fault-injection methods to predict where actual faults are more likely to hide. This veri cation can be combined with software testing to assess a con dence that the code is not hiding faults. Code that hides faults is di cult to test. In order to minimize the problem of hidden faults, we seek methods for identifying and isolating source code that is likely to hide faults. We also introduce the notion of \information loss," a characteristic that can be measured during the early phases of design to suggest where the planned software is likely to harbor faults that will be di cult to uncover during testing.

Security diagnostics expose vulnerabilities and privacy threats that exist in commercial Intelligent Virtual Assistants (IVA) – diagnostics offer the possibility of securer IVA ecosystems.