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Measuring and modeling programming experience

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Abstract

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.

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Notes

  1. ICPC was a workshop until 2005 (IWPC), which we also included.

  2. ESEM first took place in 2007.

  3. http://www.infosun.fim.uni-passau.de/spl/janet/prophet/

  4. The German system allows students to take courses in a flexible order and timing.

  5. Small: ±0.1 to ±0.3; medium: ±0.3 to ±0.5; strong: ±0.5 to ±1 (Cohen 1988).

  6. At German universities, semester starts mid October.

  7. http://www-01.ibm.com/software/analytics/spss/products/statistics/amos/. The script of the analysis is available at the project’s website.

  8. There is a heated ongoing debate about fit indices. Since there is not the best index, it is common to present different indices that focus on different aspects (Bagozzi and Yi 2012; Hu and Bentler 199; Hu and Bentler 1998).

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Acknowledgements

We thank all the reviewers for their constructive feedback. We thank Jana Schumann for her support in the literature study and all experimenters for their support in setting up and conducting the experiment. Thanks to Veit Köppen for his support in the analysis of data. Siegmund’s work is supported by BMBF project 01IM10002B, Kästner’s work by ERC grant #203099, and Apel’s work by DFG projects AP 206/2, AP 206/4, and AP 206/5.

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Authors and Affiliations

  1. University of Passau, Passau, Germany

    Janet Siegmund (born Feigenspan), Jörg Liebig & Sven Apel

  2. Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Christian Kästner

  3. University of Duisburg-Essen, Essen, Germany

    Stefan Hanenberg

Authors
  1. Janet Siegmund
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  2. Christian Kästner
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  3. Jörg Liebig
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  4. Sven Apel
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  5. Stefan Hanenberg
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Corresponding author

Correspondence to Janet Siegmund.

Additional information

Communicated by: Michael Godfrey and Arie van Deursen

Appendix

Appendix

We show the results of an exploratory factor analysis, including question s.PE, which loads on factor experience with mainstream languages.

Table 13 Follow-up study: factor loadings of variables in questionnaire (including s.PE)
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Siegmund, J., Kästner, C., Liebig, J. et al. Measuring and modeling programming experience . Empir Software Eng 19, 1299–1334 (2014). https://doi.org/10.1007/s10664-013-9286-4

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