Homepage for 17-803 "Empirical Methods" at Carnegie Mellon University
In this lecture we continue to reflect on how the (empirical) methods, regardless of how rigorously the studies are done, are ultimately applied and interpreted by human researchers. And how interpretation of even hard numbers is inherently subjective, subject to the beliefs, values, politics, social norms, culture, etc of the researchers.
Shepperd, M., Bowes, D., & Hall, T. (2014). Researcher bias: The use of machine learning in software defect prediction. IEEE Transactions on Software Engineering, 40(6), 603-616.
Ray, B., Posnett, D., Filkov, V., & Devanbu, P. (2014). A large scale study of programming languages and code quality in GitHub. In Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (pp. 155-165).
Berger, E. D., Hollenbeck, C., Maj, P., Vitek, O., & Vitek, J. (2019). On the impact of programming languages on code quality: a reproduction study. ACM Transactions on Programming Languages and Systems (TOPLAS), 41(4), 1-24.