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Social influences on secure development tool adoption: why security tools spread

Published:15 February 2014Publication History

ABSTRACT

Security tools can help developers build more secure software systems by helping developers detect or fix security vulnerabilities in source code. However, developers do not always use these tools. In this paper, we investigate a number of social factors that impact developers' adoption decisions, based on a multidisciplinary field of research called diffusion of innovations. We conducted 42 one-on-one interviews with professional software developers, and our results suggest a number of ways in which security tool adoption depends on developers' social environments and on the channels through which information about tools is communicated. For example, some participants trusted developers with strong reputations on the Internet as much as they trust their colleagues for information about security tools.

References

  1. Ajzen, I. The theory of planned behavior. Organizational Behavior and Human Decision Processes 50, 2 (Dec. 1991), 179--211.Google ScholarGoogle ScholarCross RefCross Ref
  2. Bacchelli, A., and Bird, C. Expectations, outcomes, and challenges of modern code review. In Proceedings of the 35th International Conference on Software Engineering (2013). Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Bessey, A., Block, K., Chelf, B., Chou, A., Fulton, B., Hallem, S., Henri-Gros, C., Kamsky, A., McPeak, S., and Engler, D. A few billion lines of code later: using static analysis to find bugs in the real world. Communications of the ACM 53, 2 (Feb. 2010), 66--75. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Boehm, B. W. Software Engineering Economics, 1st ed. Prentice Hall PTR, Upper Saddle River, NJ, USA, 1981. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Buxton, J., and Malcolm, R. Software technology transfer. Software Engineering Journal 6, 1 (Jan. 1991), 17--23. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. 6. Cornell, D. Remediation statistics: What does fixing application vulnerabilities cost? RSA Conference, 2012.Google ScholarGoogle Scholar
  7. Cowley, S. Code red costs could top $2 billion. PCWorld, August 2001. http: //www.pcworld.com/article/57744/article.html.Google ScholarGoogle Scholar
  8. Davis, F. D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13, 3 (Sept. 1989), 319--340. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Geer, D. Are companies actually using secure development life cycles? IEEE Computer 43, 6 (June 2010), 12--16. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Gorschek, T., Wohlin, C., Carre, P., and Larsson, S. A model for technology transfer in practice. IEEE Software 23, 6 (Nov.-Dec. 2006), 88--95. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Grigoreanu, V., Burnett, M., Wiedenbeck, S., Cao, J., Rector, K., and Kwan, I. End-user debugging strategies: A sensemaking perspective. ACM Transactions on Computer-Human Interaction 19 (2012), 5:1--5:28. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Hardgrave, B. C., Davis, F. D., and Riemenschneider, C. K. Investigating determinants of software developers' intentions to follow methodologies. Journal of Management Information Systems 20, 1 (July 2003), 123--151. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Howard, M., and Lipner, S. The Security Development Lifecycle. Microsoft Press, Redmond, WA, USA, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Iivari, J. From a macro innovation theory of IS diffusion to a micro innovation theory of IS adoption: An application to CASE adoption. In Proc. of the IFIP WG8.2 Working Group on Information Systems Development (1993), 295--320. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Iivari, J. Why are CASE tools not used? Communications of the ACM 39, 10 (Oct. 1996), 94--103. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Kemerer, C. F. How the learning curve affects CASE tool adoption. IEEE Software 9, 3 (May 1992), 23--28. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Koppel, R., Wetterneck, T., Telles, J. L., and Karsh, B.-T. Workarounds to barcode medication administration systems: Their occurrences, causes, and threats to patient safety. Journal of the American Medical Informatics Association 15, 4 (2008), 408--423.Google ScholarGoogle ScholarCross RefCross Ref
  18. McGraw, G. Software security. IEEE Security and Privacy 2, 2 (Mar.-Apr. 2004), 80--83. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Meyerovich, L. A., and Rabkin, A. S. Socio-plt: principles for programming language adoption. In Proc. of Onward!, ACM (2012), 39--54. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Moore, G. C., and Benbasat, I. Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Information Systems Research 2, 3 (Sept. 1991), 192--222.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Morrison, P., and Murphy-Hill, E. Is programming knowledge related to age? Mining Software Repositories (2013), 3--6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Murphy-Hill, E., Jiresal, R., and Murphy, G. C. Improving software developers' fluency by recommending development environment commands. In Proc. of FSE, ACM (2012), 42:1--42:11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Murphy-Hill, E., and Murphy, G. C. Peer interaction effectively, yet infrequently, enables programmers to discover new tools. In Proc. of CSCW (2011), 405--414. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Nahar, N., Kakola, T., and Huda, N. Diffusion of software technology innovations in the global context. In Proc. of the Hawaii International Conference on System Sciences (Jan. 2002), 2749--2757. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Nov, O., and Arazy, O. Personality-targeted design: theory, experimental procedure, and preliminary results. In Proc. of CSCW, ACM (New York, NY, USA, 2013), 977--984. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Parnin, C., Bird, C., and Murphy-Hill, E. Java generics adoption: how new features are introduced, championed, or ignored. In Proc. of MSR, ACM (2011), 3--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Pham, R., Singer, L., Liskin, O., Figueira Filho, F., and Schneider, K. Creating a shared understanding of testing culture on a social coding site. In Proc. of ICSE, IEEE Press (Piscataway, NJ, USA, 2013), 112--121. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Premkumar, G., and Potter, M. Adoption of computer aided software engineering (CASE) technology: an innovation adoption perspective. SIGMIS Database 26, 2-3 (May 1995), 105--124. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Raghavan, S., and Chand, D. Diffusing software-engineering methods. IEEE Software 6, 4 (July 1989), 81--90. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Rai, A., and Howard, G. Propagating case usage for software development: An empirical investigation of key organizational correlates. Omega 22, 2 (Mar. 1994), 133--147.Google ScholarGoogle ScholarCross RefCross Ref
  31. Rai, A., and Patnayakuni, R. A structural model for CASE adoption behavior. Journal of Management Information Systems 13, 2 (Sept. 1996), 205--234. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Riemenschneider, C., Hardgrave, B., and Davis, F. Explaining software developer acceptance of methodologies: a comparison of five theoretical models. IEEE TSE 28, 12 (Dec. 2002), 1135--1145. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Rigby, P. C., and Bird, C. Convergent Software Peer Review Practices. In Proceedings of the the joint meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on The Foundations of Software Engineering (ESEC/FSE), ACM (2013).Google ScholarGoogle Scholar
  34. Rogers, E. M. Diffusion of Innovations. Free Press, 1995.Google ScholarGoogle Scholar
  35. Seaman, C. Qualitative methods in empirical studies of software engineering. IEEE TSE 25, 4 (1999), 557--572. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Singer, L., and Schneider, K. Influencing the adoption of software engineering methods using social software. In Proc. of ICSE (2012), 1325--1328. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Singer, L.-G. Improving the Adoption of Software Engineering Practices Through Persuasive Interventions. PhD thesis, Gottfried Wilhelm Leibniz Universitt Hannover, 2013.Google ScholarGoogle Scholar
  38. Straus, S. G., Bikson, T. K., Balkovich, E., and Pane, J. F. Mobile technology and action teams: Assessing blackberry use in law enforcement units. Proc. of CSCW 19, 1 (2010), 45--71. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Thompson, R. L., Higgins, C. A., and Howell, J. M. Personal Computing: Toward a Conceptual Model of Utilization. MIS Quarterly 15, 1 (Mar. 1991), 125. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Venkatesh, V., and Davis, F. D. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science 46, 2 (Feb. 2000), 186--204. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Xie, J., Lipford, H., and Chu, B. Why do programmers make security errors? In Proc. of Visual Languages and Human-Centric Computing (Sept. 2011), 161--164.Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Conferences
      CSCW '14: Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
      February 2014
      1600 pages
      ISBN:9781450325400
      DOI:10.1145/2531602

      Copyright © 2014 ACM

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      Publication History

      • Published: 15 February 2014

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