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Deconstructing Agile Survey to Identify Agile Skeptics

  • Received : 2024.03.05
  • Published : 2024.03.30

Abstract

In empirical software engineering research, there is an increased use of questionnaires and surveys to collect information from practitioners. Typically, such data is then analyzed based on overall, descriptive statistics. Overall, they consider the whole survey population as a single group with some sampling techniques to extract varieties. In some cases, the population is also partitioned into sub-groups based on some background information. However, this does not reveal opinion diversity properly as similar opinions can exist in different segments of the population, whereas people within the same group might have different opinions. Even though existing approach can capture the general trends there is a risk that the opinions of different sub-groups are lost. The problem becomes more complex in case of longitudinal studies where minority opinions might fade or resolute over time. Survey based longitudinal data may have some potential patterns which can be extracted through a clustering process. It may reveal new information and attract attention to alternative perspectives. We suggest using a data mining approach to finding the diversity among the different groups in longitudinal studies (agile skeptics). In our study, we show that diversity can be revealed and tracked over time with the use of clustering approach, and the minorities have an opportunity to be heard.

Keywords

References

  1. R. C. Henry and J. D. Zivick, "Principles of survey research.," Fam. Pract. Res. J., vol. 5, no. 3, pp. 145-157, 1986. 
  2. T. Xie, J. Pei, and A. E. Hassan, "Mining software engineering data," Proc. - Int. Conf. Softw. Eng., no. May, pp. 172-173, 2007. 
  3. M. Blom, "Applying clustering to analyze opinion diversity," 2015. 
  4. B. Kitchenham and S. L. Pfleeger, "Principles of survey research part 6," ACM SIGSOFT Softw. Eng. Notes, vol. 28, no. 2, pp. 24-27, 2003.  https://doi.org/10.1145/638750.638758
  5. B. Kitchenham and S. L. Pfleeger, "Principles of Survey Research Part 5: Populations and Samples," ACM SIGSOFT Softw. Eng. Notes, vol. 27, no. 5, p. 17, 2002. 
  6. B. A. Kitchenham et al., "Preliminary guidelines for empirical research in software engineering," IEEE Trans. Softw. Eng., vol. 28, no. 8, pp. 721-734, 2002.  https://doi.org/10.1109/TSE.2002.1027796
  7. M. Mendonca and N. L. Sunderhaft, Mining Software Engineering Data: A Survey A DACS State-of-the-Art Report, vol. 4000. . 
  8. S. Wagner, D. M. Fernandez, M. Felderer, D. Graziotin, and M. Kalinowski, "Challenges in survey research," arXiv, 2019. 
  9. J. Moses, "Benchmarking quality measurement," Softw. Qual. J., vol. 15, no. 4, pp. 449-462, 2007.  https://doi.org/10.1007/s11219-007-9025-4
  10. J. Moses and M. Farrow, "Tests for consistent measurement of external subjective software quality attributes," Empir. Softw. Eng., vol. 13, no. 3, pp. 261-287, 2008.  https://doi.org/10.1007/s10664-007-9058-0
  11. J. Moses, "Should we try to measure software quality attributes directly?," Softw. Qual. J., vol. 17, no. 2, pp. 203-213, 2009.  https://doi.org/10.1007/s11219-008-9071-6
  12. T. Gorschek, E. Tempero, and L. Angelis, "On the use of software design models in software development practice: An empirical investigation," J. Syst. Softw., vol. 95, pp. 176-193, 2014.  https://doi.org/10.1016/j.jss.2014.03.082
  13. E. M. Maximilien and L. Williams, "Assessing Test-Driven Development at IBM 5505 Six Forks Road Department of Computer Science," Proc. 25th Int. Conf. Softw. Eng., vol. 6, 2003. 
  14. M. Alqudah and R. Razali, "A review of scaling agile methods in large software development," Int. J. Adv. Sci. Eng. Inf. Technol., vol. 6, no. 6, pp. 828-837, 2016.  https://doi.org/10.18517/ijaseit.6.6.1374
  15. J. Barlow et al., "Overview and Guidance on Agile Development in Large Organizations," SSRN Electron. J., 2012. 
  16. "2008 IT Project Success Rates Survey Results: December 2008." [Online]. Available: http://www.ambysoft.com/surveys/success2008.html. [Accessed: 25-Mar-2021]. 
  17. "Agile Adoption Rate Survey Results: February 2008." [Online]. Available: http://www.ambysoft.com/surveys/agileFebruary2008.html. [Accessed: 26-Mar-2021]. 
  18. "Software Development at Scale: Results from the Spring 2014 DDJ State of the IT Union Survey." [Online]. Available: http://www.ambysoft.com/surveys/stateOfITUnion2014Q2.html. [Accessed: 26-Mar-2021]. 
  19. T. Hall, "Longitudinal studies in evidence-based software engineering," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 4336 LNCS, no. 3, p. 41+, 2007.