Measuring and Modeling Programming Experience

kaestner@cs.cmu.edu (J. Feigenspan, C. Kästner, J. Liebig, S. Apel, S. Hanenberg)
Sun, 13 Mar 2016 14:06:02 -0400

Janet Feigenspan, Christian Kästner, Jörg Liebig, Sven Apel, and Stefan Hanenberg. Measuring and Modeling Programming Experience. Empirical Software Engineering (EMSE), 19(5):1299--1334, October 2014.

Programming experience is an important confounding parameter in controlled experiments regarding program comprehension. In literature, ways to measure or control programming experience vary. Often, researchers neglect it or do not specify how they controlled for it. We set out to find a well-defined understanding of programming experience and a way to measure it. From published comprehension experiments, we extracted questions that assess programming experience. In a controlled experiment, we compare the answers of computer-science students to these questions with their performance in solving program-comprehension tasks. We found that self estimation seems to be a reliable way to measure programming experience. Furthermore, we applied exploratory and confirmatory factor analyses to extract and evaluate a model of programming experience. With our analysis, we initiate a path toward validly and reliably measuring and describing programming experience to better understand and control its influence in program-comprehension experiments.