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Raghu N Kacker

Raghu Kacker is a scientist in the Applied and Computational Mathematics Division (ACMD) of the Information Technology Laboratory (ITL) of the National Institute of Standards and Technology (NIST). His current interests include combinatorial methods for black box testing and their applications; mathematics of metrology; quantification of uncertainty in measurements from physical experiments and computational models.

Current Projects:

Additional Data:


  • Fellow, American Society for Quality ASQ (2000)
  • Fellow, American Statistical Association ASA (1994)
  • U.S. Department of Commerce Silver Medal (2014)
  • U.S. Department of Commerce Bronze Medal (1994)
  • Distinguished Technical Staff Award, AT&T-Bell Laboratories (1985)
  • Federal Laboratory Consortium Award for combinatorial testing tool (2009)R&D 100 Award, Team Member for developing the software S-check (1997)
  • PATENT:  United States Patent number: US 10,552,300 B2. Date: February 4, 2020. Title: Oracle-free match testing of a program using covering arrays and equivalence classes. Owner: U.S. Department of Commerce. Inventors: D. Richard Kuhn and Raghu N. Kacker.

Selected Publications

Laleh S. Ghandehari, Yu Lei, Raghu N. Kacker, D. Richard Kuhn, Tao Xie, and David Kung. A Combinatorial Testing-Based Approach to Fault Localization, IEEE Transactions on Software Engineering, 46 (June 2020) pp. 616-645.

Jin Chu Wu, Alvin F. Martin and Raghu N. Kacker. Bootstrap variability studies in ROC analysis on large datasets, Communications in Statistics - Simulation and Computation, 43 (2014) pp. 225-236.

Yu Lei, Raghu N. Kacker, D. Richard Kuhn, Vadim Okun, and James F. Lawrence. IPOG/IPOG-D: Efficient test generation for multi-way combinatorial testing, Software Testing, Verification and Reliability, 18 (2007) pp. 125-148.

Raghu N. Kacker and James F. Lawrence. Trapezoidal and triangular distributions for Type B evaluation of standard uncertainty, Metrologia, 44 (2007) pp. 117-127.

Karl K. Irikura, Russell D. Johnson, and Raghu N. Kacker. Uncertainties in scaling factors for ab-initio vibrational frequencies, Journal of Physical Chemistry A, 109 (2005) pp. 8430-8437.

Raghu N. Kacker and Albert T. Jones. On use of Bayesian statistics to make Guide to the expression of uncertainty in measurement consistent, Metrologia, 40 (2003), pp. 235-248.

Raghu N. Kackar. Off-line Quality Control, Parameter Design, and the Taguchi Method, with discussion and response, Journal of Quality Technology, 17 (1985), pp. 176-209.

Raghu N. Kackar and David A. Harville. Approximations for Standard Errors of Estimators of Fixed and Random Effects in mixed Linear Models” Journal of American Statistical Association, 79 (1984), pp. 853-862.


Combinatorial Methods for Explainable AI

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

Input Space Coverage Matters

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
Created October 9, 2019, Updated August 26, 2020