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Search Publications by: D. Richard Kuhn (Assoc)

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Displaying 26 - 50 of 374

A Pseudo Exhaustive Software Testing Framework for Embedded Digital Devices in Nuclear Power

June 14, 2021
Author(s)
Athira Jayakumar, D. Richard Kuhn, Brandon Simons, Aidan Collins, Smitha Gautham, Richard Hite, Raghu N. Kacker, Abhi Rajagopala, Carl Elks
The major challenge faced by the nuclear industry related to software testing of digital embedded devices is the identification of practical software (SW) testing solutions that provide a strong technical basis and is at the same time effective in

Combinatorially XSSing Web Application Firewalls

April 12, 2021
Author(s)
Bernhard Garn, Daniel S. Lang, Manuel Leithner, D. Richard Kuhn, Raghu N. Kacker, Dimitris Simos
Cross-Site scripting (XSS) is a common class of vulnerabilities in the domain of web applications. As it remains prevalent despite continued efforts by practitioners and researchers, site operators often seek to protect their assets using web application

Combinatorial Testing Metrics for Machine Learning

April 12, 2021
Author(s)
Erin Lanus, Laura Freeman, D. Richard Kuhn, Raghu N. Kacker
This short paper defines a combinatorial coverage metric for comparing machine learning (ML) data sets and proposes the differences between data sets as a function of combinatorial coverage. The paper illustrates its utility for evaluating and predicting

Combinatorial Test Generation for Multiple Input Models with Shared Parameters

March 17, 2021
Author(s)
Chang Rao, Nan Li, Yu Lei, Jin Guo, YaDong Zhang, Raghu N. Kacker, D. Richard Kuhn
Combinatorial testing typically considers a single input model and creates a single test set that achieves t-way coverage. This paper addresses the problem of combinatorial test generation for multiple input models with shared parameters. We formally

A Trusted Federated System to Share Granular Data Among Disparate Database Resources

March 15, 2021
Author(s)
Joanna DeFranco, David F. Ferraiolo, D. Richard Kuhn, Joshua D. Roberts
Sharing data between different organizations is a challenge primarily due to database management systems (DBMSs) being different types that impose different schemas to represent and retrieve data. In addition, maintaining security and privacy is a concern

Combinatorial Methods for Explainable AI

October 24, 2020
Author(s)
David R. Kuhn, Raghu N. Kacker, Yu Lei, Dimitris Simos
This paper introduces an approach to producing explanations or justifications of decisions made by artificial intelligence and machine learning (AI/ML) systems, using methods derived from fault location in combinatorial testing. We use a conceptually

Vulnerability Trends in Web Servers and Browsers

September 11, 2020
Author(s)
M S Raunak, D. Richard Kuhn, Raghu N. Kacker, Richard Kogut
In previous work we have looked at trends in vulnerabilities due to ordinary programming errors [2, 3]. This analysis focuses on two of the most widely used types of software in today's internet, web browsers and web servers. In addition to reports of

Effectiveness of dataset reduction in testing machine learning algorithms

August 25, 2020
Author(s)
Raghu N. Kacker, David R. Kuhn
Abstract— Many machine learning algorithms examine large amounts of data to discover insights from hidden patterns. Testing these algorithms can be expensive and time-consuming. There is a need to speed up the testing process, especially in an agile

Aggregating Atomic Clocks for Time-Stamping

August 3, 2020
Author(s)
Temur Saidkhodjaev, Jeff Voas, D. Richard Kuhn, Joanna DeFranco, Phil Laplante
A timestamp is a critical component in many applications, such as proof of transaction ordering or analyzing algorithm performance. This paper reports on a method called Verified Timestamping (VT) that improves the standard timestamp protocol. VT was

Narrow Spectrum Software Testing Addressing Complexity and Trust

April 9, 2020
Author(s)
David R. Kuhn, Mohammad Raunak
Combination coverage based testing supplements basic structural coverage based test selection. This provides a sound test engineering method with defensible, quantitative measures of test completeness.
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