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Search Publications by: Raghu N Kacker (Fed)

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Displaying 51 - 75 of 373

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

Measuring Combinatorial Coverage 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. In addition

Using Parameter Mapping to Avoid Forbidden Tuples in a Covering Array

April 22, 2019
Author(s)
Raghu N. Kacker, David R. Kuhn, Yu Lei
This paper addresses an optimization problem that occurs when we try to remove from a covering array (CA) the rows that do not satisfy a given set of constraints. That is, how to minimize the number of rows to be removed? The key observation is that the

Browser Fingerprinting using Combinatorial Sequence Testing

April 1, 2019
Author(s)
Bernhard Garn, Dimitris Simos, Stefan Zimmer, D. Richard Kuhn, Raghu N. Kacker
In this paper, we propose an approach for browser fingerprinting using their behavior during the TLS 1.2 handshake with a server. Using combinatorial methods, we created test sets consisting of TLS server-side messages as sequences that are sent to the

A Method Level Test Generation Framework for Debugging Big Data Applications

January 24, 2019
Author(s)
Huadong Feng, Jagan Chandrasekaran, Yu Lei, Raghu N. Kacker, D. Richard Kuhn
When a failure occurs in a big data application, debugging with the original dataset can be difficult due to the large amount of data being processed. This paper introduces a framework for effectively generating method-level tests to facilitate debugging

A Method-Level Test Generation Framework for Debugging Big Data Applications

January 24, 2019
Author(s)
Raghu N. Kacker, David R. Kuhn, Huadong Feng, Yu J. Lei
Big data applications are now widely used to process massive amounts of data we create every day. When a failure occurs in a big data application, debugging at the system-level input can be expensive due to the large amount of data being processed. This

MCDC-Star – An Open-source MC/DC Measurement Tool

September 22, 2018
Author(s)
Raghu N. Kacker, David R. Kuhn, Eric Wong
Applying MC/DC criterion to real-world projects can be expensive due to not only the cost of commercial tools, but also the difficulty of generating test cases to achieve high coverage. To lower the expense from both aspects, this paper presents an easy-to

True value and uncertainty in measurement

September 3, 2018
Author(s)
Raghu N. Kacker
We will discuss the concept of true value and its connection with the uncertainty in measurement as defined in the Guide to the Expression of Uncertainty in Measurement (GUM), and three subsequent documents from the Joint Committee for Guides in Metrology

Finding Bugs in Cryptographic Hash Function Implementations

July 6, 2018
Author(s)
Nicky W. Mouha, Mohammad Raunak, David R. Kuhn, Raghu N. Kacker
Cryptographic hash function implementations can be particularly difficult to test, and bugs can remain unnoticed for a very long time. We revisit the NIST SHA-3 hash function competition, and apply a new testing strategy to all available reference

Pseudo-exhaustive Verification of Rule Based Systems

July 1, 2018
Author(s)
David R. Kuhn, Dylan J. Yaga, Raghu N. Kacker, Yu Lei, Chung Tong Hu
Rule-based systems are important in application domains such as artificial intelligence and business rule engines, as well as formal methods for software design. When translated into an implementation, simple expressions in rules may map to thousands of

Combinatorial Security Testing Course

April 11, 2018
Author(s)
Dimitris Simos, Yu Lei, D. Richard Kuhn, Raghu N. Kacker
Combinatorial methods have attracted attention as a means of providing strong assurance at reduced cost, but when are these methods practical and cost-effective? This tutorial comprises two parts. The first introductory part will briefly explain the back-