What can we learn from surveys on the importance of software development productivity factors?
Resumo
A proper interpretation of survey results on the importance of software development productivity factors requires an understanding of what the responses reflect. To find out more about this, we conducted a survey with 79 experienced software professionals. The strongest connection found was between the high perceived importance of a productivity factor and how frequently the respondents had experienced that a low performance on that factor had caused productivity problems. We argue that it is challenging to interpret survey-based results on the importance of productivity factors. Instead of conducting more surveys on the importance of such factors, we recommend asking directly about previous experiences related to productivity factors and including contextual information that enables proper interpretation of the responses.
Palavras-chave:
Software development productivity, survey, empirical methods
Referências
Blackburn, J. D., Scudder, G. D., & Van Wassenhove, L. N. (1996). Improving speed and productivity of software development: a global survey of software developers. IEEE Transactions on Software Engineering, 22(12), 875-885.
Brown, J. D. (2011). Likert items and scales of measurement. Statistics, 15(1), 10-14.
Chattopadhyay, S., Nelson, N., Au, A., Morales, N., Sanchez, C., Pandita, R., & Sarma, A. (2022). Cognitive biases in software development. Communications of the ACM, 65(4), 115-122.
de Barros Sampaio, S. C., Barros, E. A., de Aquino, G. S., e Silva, M. J. C., & de Lemos Meira, S. R. (2010). A review of productivity factors and strategies on software development. Paper presented at the 2010 fifth international conference on software engineering advances.
Gigerenzer, G. (2008). Why heuristics work. Perspectives on psychological science, 3(1), 20-29.
Jørgensen, M. (2013). Myths and over-simplifications in software engineering. Lecture Notes on Software Engineering, 1(1), 7.
Jørgensen, M., & Papatheocharous, E. (2015). Believing is seeing: Confirmation bias studies in software engineering. Paper presented at the 2015 41st Euromicro Conference on Software Engineering and Advanced Applications.
Machuca-Villegas, L., Gasca-Hurtado, G. P., Puente, S. M., & Tamayo, L. M. R. (2022). Perceptions of the human and social factors that influence the productivity of software development teams in Colombia: A statistical analysis. Journal of systems and software, 192, 111408.
Machuca-Villegas, L., Hurtado, G. G., Puente, S. M., & Tamayo, L. M. R. (2021). An Instrument for Measuring Perception about Social and Human Factors that Influence Software Development Productivity. J. Univers. Comput. Sci., 27(2), 111-134.
Oliveira, E., Conte, T., Cristo, M., & Valentim, N. (2018). Influence factors in software productivity—a tertiary literature review. International Journal of Software Engineering and Knowledge Engineering, 28(11n12), 1795-1810.
Paiva, E., Barbosa, D., Lima Jr, R., & Albuquerque, A. (2010). Factors that influence the productivity of software developers in a developer view. In Innovations in computing sciences and software engineering (pp. 99-104): Springer.
Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive psychology, 5(2), 207-232.
Wagner, S., & Ruhe, M. (2018). A systematic review of productivity factors in software development. arXiv preprint arXiv:1801.06475.
Brown, J. D. (2011). Likert items and scales of measurement. Statistics, 15(1), 10-14.
Chattopadhyay, S., Nelson, N., Au, A., Morales, N., Sanchez, C., Pandita, R., & Sarma, A. (2022). Cognitive biases in software development. Communications of the ACM, 65(4), 115-122.
de Barros Sampaio, S. C., Barros, E. A., de Aquino, G. S., e Silva, M. J. C., & de Lemos Meira, S. R. (2010). A review of productivity factors and strategies on software development. Paper presented at the 2010 fifth international conference on software engineering advances.
Gigerenzer, G. (2008). Why heuristics work. Perspectives on psychological science, 3(1), 20-29.
Jørgensen, M. (2013). Myths and over-simplifications in software engineering. Lecture Notes on Software Engineering, 1(1), 7.
Jørgensen, M., & Papatheocharous, E. (2015). Believing is seeing: Confirmation bias studies in software engineering. Paper presented at the 2015 41st Euromicro Conference on Software Engineering and Advanced Applications.
Machuca-Villegas, L., Gasca-Hurtado, G. P., Puente, S. M., & Tamayo, L. M. R. (2022). Perceptions of the human and social factors that influence the productivity of software development teams in Colombia: A statistical analysis. Journal of systems and software, 192, 111408.
Machuca-Villegas, L., Hurtado, G. G., Puente, S. M., & Tamayo, L. M. R. (2021). An Instrument for Measuring Perception about Social and Human Factors that Influence Software Development Productivity. J. Univers. Comput. Sci., 27(2), 111-134.
Oliveira, E., Conte, T., Cristo, M., & Valentim, N. (2018). Influence factors in software productivity—a tertiary literature review. International Journal of Software Engineering and Knowledge Engineering, 28(11n12), 1795-1810.
Paiva, E., Barbosa, D., Lima Jr, R., & Albuquerque, A. (2010). Factors that influence the productivity of software developers in a developer view. In Innovations in computing sciences and software engineering (pp. 99-104): Springer.
Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive psychology, 5(2), 207-232.
Wagner, S., & Ruhe, M. (2018). A systematic review of productivity factors in software development. arXiv preprint arXiv:1801.06475.
Publicado
24/04/2023
Como Citar
JØRGENSEN, Magne.
What can we learn from surveys on the importance of software development productivity factors?. In: CONGRESSO IBERO-AMERICANO EM ENGENHARIA DE SOFTWARE (CIBSE), 26. , 2023, Montevideo, Uruguai.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 16-30.
DOI: https://doi.org/10.5753/cibse.2023.24690.
