An engineer at The University of Texas at Arlington is making it easier for software developers to more efficiently test big data analysis systems.

Jeff Lei, a professor in the Computer Science and Engineering Department, was awarded a three-year, $375,000 grant from the National Institute of Standards and Technology to develop new combinatorial testing techniques that will ensure that software used to analyze big data is free of bugs. 

Founded in 1901, NIST is a non-regulatory federal agency within the U.S. Department of Commerce. NIST's mission is to promote U.S. innovation and industrial competitiveness by advancing measurement science, standards and technology in ways that enhance economic security and improve life.

"Our research is among the first efforts to develop combinatorial testing techniques that will ensure successful testing and implementation of big data software. Many data analysis algorithms have been designed to discover useful information from large amounts of data," Lei said. "These algorithms must be implemented correctly in software so people can use them. Often, software engineers are hired to perform the implementation, and they can make mistakes. Our goal is to develop techniques and tools that software engineers can use to detect mistakes made during implementation and thus ensure that the software they develop will actually work."

Combinatorial testing has been widely applied to software testing, although not to big data software. It first identifies the major factors that could impact the behavior of the software being tested. 

Then it uses a systematic approach to test only a small subset of interactions between the factors. That subset is selected so that it can help developers find a significant percentage of bugs that might exist in the software. 

Big data software poses new challenges to combinatorial testing due to its algorithm-intensive nature and also the need to deal with large amounts of data. Lei's research will allow combinatorial testing to be effectively applied to big data software. The goal is to help software developers build more reliable big data software faster. 

Khosrow Behbehani, dean of the College of Engineering, commended Lei on his work and its importance to future research and product development.

"Data-driven discovery is a pillar of UTA's Strategic Plan, and our faculty in the College of Engineering are leading the way in adapting big data for healthcare, security and other applications. Users of these massive amounts of data must be confident that they can count on its reliability," Behbehani said. "Dr. Lei's important work will allow software developers to create tools that researchers can use with confidence, knowing that the testing procedures used during the development of those tools are sound."

This is not Lei's first NIST grant. He received two in 2013 worth about $400,000 that was associated with combinatorial testing in the healthcare information technology sector. That work has the potential to greatly reduce the cost of healthcare while improving the quality.