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Search Publications by: Raghu N Kacker (Fed)

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Displaying 26 - 50 of 373

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

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

Operational Measurement Uncertainty and Bayesian Probability Distribution

June 25, 2020
Author(s)
Raghu N. Kacker
The JCGM documents have undermined the operational concept of uncertainty in measurement established by the GUM and restored the pre-GUM practice of stating possible error relative to the true value, supposedly to align with Bayesian interpretation. It is

Input Space Coverage Matters

January 15, 2020
Author(s)
David R. Kuhn, Raghu N. Kacker, Yu Lei, Dimitris Simos
Testing is the most commonly used approach for software assurance, yet it remains as much judgement and art as science. Structural coverage adds some rigor to the process by establishing formally defined criteria for some notion of test completeness, but

Low-Power Wide Area Networks (LPWAN) for Communications of Mobile Sensor Data

September 10, 2019
Author(s)
Sebastian Barillaro, Sokwoo Rhee, Raghu N. Kacker, Mark L. Badger, David R. Kuhn, Gustavo Escudero
There are multiple options for communication of data to and from mobile sensors. For tracking systems, Global Navigation Satellite System (GNSS) is often used for localization and mobile- phone technologies are used for transmission of data. Low-power wide

Monte Carlo studies of bootstrap variability in ROC analysis with data dependency

August 1, 2019
Author(s)
Jin Chu Wu, Alvin F. Martin, Raghu N. Kacker
ROC analysis involving two large datasets is an important method for analyzing statistics of interest for decision making of a classifier in many disciplines. And data dependency due to multiple use of the same subjects exists ubiquitously in order to

Improving MC/DC and Fault Detection Strength Using Combinatorial Testing

July 25, 2019
Author(s)
D. Richard Kuhn, Raghu N. Kacker
Software, in many different fields and tasks, has played a critical role and even replaced humans to improve efficiency and safety. However, catastrophic consequences can be caused by implementation bugs and design defects. MC/DC (Modified Condition

An Approach to T-way Test Sequence Generation With Constraints

April 22, 2019
Author(s)
Raghu N. Kacker, David R. Kuhn
In this paper we address the problem of constraint handling in t-way test sequence generation. We develop a notation for specifying sequencing constraints and present a t-way test sequence generation that handles the constraints specified in this notation

Applying Combinatorial Testing to Large-scale Data Processing at Adobe

April 22, 2019
Author(s)
Raghu N. Kacker, David R. Kuhn, Riley Smith
Adobe offers an analytics product as part of the Marketing Cloud software with which customers can track many details about users across various digital platforms. For the most part, customers define the amount and type of data to track. This high