@article{17056,
  author = {Magne J{\o}rgensen},
  title = {Characteristics and generative mechanisms of software development productivity distributions},
  abstract = {Context: There is considerable variation in the productivity of software developers. Better knowledge about this variation may provide valuable inputs for the design of skill tests and recruitment processes. Objective: This paper aims to identify properties of software development productivity distributions and gain insight into mechanisms that potentially explain these productivity differences. Method: Four data sets that contain the results of software developers solving the same programming tasks were collected. The properties of the productivity distributions were analyzed, the fits of different types of distributions to the productivity data were compared, and potential generative mechanisms that would lead to the types of distributions with the best fit to the productivity data were evaluated. Results: The coefficient of variance of the productivity of the software developers was, on average, 0.55, with the top 50\% of developers having average productivity that was 2.44 times higher than the bottom 50\% of developers. All productivity samples were right-skewed, with an average skew of 1.79. About 30\% of the observed productivity variance was explained by non-systematic, i.e., within-developer, variance. The distributions with the best fit to the empirical productivity data were the lognormal and power-law-with-an-exponential-cutoff distributions. The analysis of the mechanisms leading to productivity differences found no support for the "rich-getting-richer" explanation proposed for other disciplines. Instead, it suggests a constant productivity difference with increasing experience. Conclusion: The substantial difference in productivity among software developers solving programming tasks indicates that a thorough evaluation of skill in the recruitment process can be rewarding. In particular, the long tail towards higher productivity values demonstrates the large gains that can be achieved by detecting and recruiting developers with very high productivity. More research is needed to understand the mechanisms leading to the large productivity differences.},
  year = {2023},
  journal = {Information and Software Technology},
  volume = {159},
  pages = {107215},
  publisher = {Elsevier},
  url = {https://doi.org/10.1016/j.infsof.2023.107215},
}