nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

Trust in Automated Software Repair

The Effects of Repair Source, Transparency, and Programmer Experience on Perceived Trustworthiness and Trust

verfasst von : Tyler J. Ryan, Gene M. Alarcon, Charles Walter, Rose Gamble, Sarah A. Jessup, August Capiola, Marc D. Pfahler

Erschienen in: HCI for Cybersecurity, Privacy and Trust

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Automation and autonomous systems are becoming increasingly pervasive in society, as are the software systems that control them. There is a need for safe and secure software systems. Automated code repair provides a promising solution. The present research investigates programmers’ perceptions of trustworthiness and trust in automated code repair, how those perceptions and intentions differed from code ostensibly repaired by a human, and the effects of repair transparency. The present research comprises two studies, each with a unique sample. The first sample included inexperienced developers (N = 24), and the second sample included experienced developers (N = 24). Participants were presented with five different pieces of code before and after being repaired by an automated code repair program, and were asked to rate the trustworthiness of the repairs and whether they would endorse using the code. Each study was a 2 × 2 between-subjects design with repeated measures. The first factor manipulated the purported source of the repairs (human vs automated code repair program). The second factor manipulated the transparency of the repairs (deleted vs commented out). Results suggest that inexperienced developers find automated code repair more trustworthy than repairs made by a human. Both experienced and inexperienced developers trusted the human repairer less after reviewing the repairs, but did not significantly differ in their intentions to trust the automated code repair program after reviewing the repairs.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Privacy and Power Implications of Web Location of Personal Data Authenticators

Nächstes Kapitel Measuring Network User Trust via Mouse Behavior Characteristics Under Different Emotions

Details of the multiple imputation procedure are available from the first author.

Fung, B.: 145 million social security numbers, 99 million addresses and more: every type of personal data Equifax lost to hackers, by the numbers. The Washington Post (2018). https://www.washingtonpost.com/news/the-switch/wp/2018/05/08/every-type-of-personal-data-equifax-lost-to-hackers-by-the-numbers/?noredirect=on&utm_term=.aa92dfb95b08. Accessed 20 Dec 2018

Reuters: Lion Air reviewing Boeing jet purchases after crash that killed 189: report. Business Standard (2018). https://www.business-standard.com/article/international/lion-air-reviewing-boeing-jet-purchases-after-crash-that-killed-189-report-118120301164_1.html. Accessed 20 Dec 2018

CBS Interactive Inc.: Wells Fargo computer glitch blamed as hundreds lose their homes. CBS News (2018). https://www.cbsnews.com/news/wells-fargo-loan-modification-error-homeowners-who-went-into-foreclosure-seek-answers/. Accessed 20 Dec 2018

Perkins, J.H., et al.: Automatically patching errors in deployed software. In: 22nd Symposium on Operating Systems Principles, SOSP, Big Sky, pp. 87–102 (2009). https://doi.org/10.1145/1629575.1629585

Carzaniga, A., Gorla, A., Mattavelli, A., Perino, N., Pezzè, M.: Automatic recovery from runtime failures. In: Proceedings of the 2013 International Conference on Software Engineering, pp. 782–791. IEEE Press, Piscataway (2013)

Le Goues, C., Nguyen, T., Forrest, S., Weimer, W.: GenProg: a generic method for automatic software repair. J. IEEE Trans. Soft. Eng. 38, 54–72 (2012). https://doi.org/10.1109/TSE.2011.104CrossRef

Lee, J.D., See, K.A.: Trust in automation: designing for appropriate reliance. Hum. Factors J. Hum. Fac. Erg. Soc. 46, 50–80 (2004). https://doi.org/10.1518/hfes.46.1.50_30392CrossRef

Anvik, J., Hiew, L., Murphy, G.C.: Coping with an open bug repository. In: Proceedings of the 2005 OOPSLA Workshop on Eclipse Technology Exchange, pp. 35–39. ACM, New York (2005). https://doi.org/10.1145/1117696.1117704

Ball, T., Rajamani, S.K.: Automatically validating temporal safety properties of interfaces. In: Dwyer, M. (ed.) SPIN 2001. LNCS, vol. 2057, pp. 102–122. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45139-0_7CrossRef

10.

Hovemeyer, D., Pugh, W.: Finding bugs is easy. ACM SIGPLAN Notices 39, 92–106 (2004). https://doi.org/10.1145/1052883.1052895CrossRef

11.

Denning, D.E.: An intrusion-detection model. J. IEEE Trans. Soft. Eng. SE-13, 222–232 (1987). https://doi.org/10.1109/tse.1987.232894CrossRef

12.

Cox, B., et al.: N-variant systems: a secretless framework for security through diversity. In: Proceedings of the 15th Conference on USENIX Security Symposium, pp. 105–120. USENIX Association, Berkeley (2006)

13.

Arcuri, A.: Evolutionary repair of faulty software. J. App. Soft Comput. 11, 3494–3514 (2011). https://doi.org/10.1016/j.asoc.2011.01.023CrossRef

14.

Le Goues, C., Dewey-Vogt, M., Forrest, S., Weimer, W.: A systematic study of automated program repair: fixing 55 out of 105 bugs. In: 34th International Conference on Software Engineering, pp. 3–13. IEEE Press, Zurich (2012). https://doi.org/10.1109/icse.2012.6227211

15.

Yuan, Y., Banzhaf, W.: ARJA: automated repair of java programs via multi-objective genetic programming. J. LaTex Class Files 14, 1–30 (2015). https://doi.org/10.1109/TSE.2018.2874648CrossRef

16.

Mayer, R.C., Davis, H.C., Schoorman, F.D.: An integrative model of organizational trust. J. Acad. Manag. Rev. 20, 709–734 (1995). https://doi.org/10.5465/amr.1995.9508080335CrossRef

17.

Sheridan, T.B.: Humans and Automation: System Design and Research Issues. Wiley, New York (2002)

18.

Lambert, F.: Tesla driver was pulled over by cops after allegedly sleeping drunk on autopilot for 7 miles. Electrek (2018). https://electrek.co/2018/12/01/tesla-pulled-over-cops-sleeping-drunk-autopilot/. Accessed 20 Dec 2018

19.

Alarcon, G.M., Ryan, T.J.: Trustworthiness perceptions of computer code: a heuristic-systematic processing model. In: Proceedings of the 51st Hawaii International Conference on System Sciences, pp. 5384–5393 (2018). https://doi.org/10.24251/hicss.2018.671

20.

Alarcon, G.M., Militello, L.G., Ryan, P., Jessup, S.A., Calhoun, C.S., Lyons, J.B.: A descriptive model of computer code trustworthiness. J. Cog. Eng. Dec. Making 11, 107–121 (2016). https://doi.org/10.1177/1555343416657236CrossRef

21.

Alarcon, G.M., et al.: Application of the heuristic-systematic model to computer code trustworthiness: the influence of reputation and transparency. J. Cogent Psych. 4, 1–22 (2017). https://doi.org/10.1080/23311908.2017.1389640CrossRef

22.

Alarcon, G.M., et al.: The influence of commenting validity, placement, and style on perceptions of computer code trustworthiness: a heuristic-systematic processing approach. J. App. Erg. 70, 182–193 (2018). https://doi.org/10.1016/j.apergo.2018.02.027CrossRef

23.

Chaiken, S.: Heuristic versus systematic information processing and the use of source versus message cues in persuasion. J. Pers. Soc. Psych. 39, 752–766 (1980). https://doi.org/10.1037/0022-3514.39.5.752CrossRef

24.

Hoff, K.A., Bashir, M.: Trust in automation: integrating empirical evidence on factors that influence trust. J. Hum. Fact. 57, 407–434 (2015). https://doi.org/10.1177/0018720814547570CrossRef

25.

Merritt, S.M., Ilgen, D.R.: Not all trust is created equal: dispositional and history-based trust in human-automation interactions. J. Hum. Fact. 50, 194–210 (2008). https://doi.org/10.1518/001872008X288574CrossRef

26.

Ryan, T.J., Walter, C., Alarcon, G.M., Gamble, R.F., Jessup, S.A., Capiola, A.: The influence of personality on code reuse. In: Proceedings of the 52nd Hawaii International Conference on System Sciences, University of Hawaii, Manoa, 8–11 January 2019

27.

Lyons, J.B., et al.: Trust-based analysis of an air force collision avoidance system. J. Erg. Des. 24, 9–12 (2016). https://doi.org/10.1177/1064804615611274CrossRef

28.

Dzindoleta, M.T., Peterson, S.A., Pomranky, R.A., Pierce, L.G., Beck, H.P.: The role of trust in automation reliance. J. Hum. Comput. Stud. 58, 697–718 (2003). https://doi.org/10.1016/S1071-5819(03)00038-7CrossRef

29.

Merritt, S.M., Heimbaugh, H., LaChapell, J., Lee, D.: I trust it, but I don’t know why: effects of implicit attitudes toward automation on trust in an automated system. J. Hum. Fact. 55, 520–534 (2013). https://doi.org/10.1177/0018720812465081CrossRef

30.

Dzindoler, M.T., Beck, H.P., Pierce, L.G., Dawe, L.A.: A framework of automation use. Technical report, Army Research Laboratory (2001)

31.

Lyons, J.B., et al.: Trust of an automatic ground collision avoidance technology: a fighter pilot perspective. J. Mil. Psych. 28, 271–277 (2016). https://doi.org/10.1037/mil0000124CrossRef

32.

Ryan, T.J., Walter, C., Alarcon, G.M., Gamble, R.F., Jessup, S.A., Capiola, A.A.: Individual differences in trust in code: the moderating effects of personality on the trustworthiness-trust relationship. In: Stephanidis, C. (ed.) HCI 2018. CCIS, vol. 850, pp. 370–376. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92270-6_53CrossRef

33.

Chen, J.Y.C., Barnes, M.J.: Human-agent teaming for multirobot control: a review of human factors issues. IEEE Trans. Hum. Mach. Syst. 44, 13–29 (2014). https://doi.org/10.1109/THMS.2013.2293535CrossRef

34.

Arkin, R.C., Ulam, P., Wagner, A.R.: Moral decision making in autonomous systems: enforcement, moral emotions, dignity, trust, and deception. In: Proceedings of IEEE, pp. 571–589. IEEE Press, New York (2012). https://doi.org/10.1109/jproc.2011.2173265CrossRef

35.

Lewandowsky, S., Mundy, M., Tan, G.P.A.: The dynamic of trust: comparing humans to automation. J. Exp. Psych. 6, 104–123 (2000). https://doi.org/10.1037//1076-898X.6.2.104CrossRef

36.

Jian, J., Bisantz, A.M., Drury, C.G.: Foundations for an empirically determined scale of trust in automated systems. Int. J. Cog. Erg. 4, 53–71 (2000). https://doi.org/10.1207/S15327566IJCE0401_04CrossRef

37.

Wanous, J.P., Reichers, A.E., Hudy, M.J.: Overall job satisfaction: how good are single-item measures? J. App. Psych. 82, 247–252 (1997). https://doi.org/10.1037/0021-9010.82.2.247CrossRef

38.

Mayer, R.C., Davis, J.H.: The effect of the performance appraisal system on trust for management: a field quasi-experiment. J. App. Psych. 84, 123–136 (1999). https://doi.org/10.1037/0021-9010.84.1.123CrossRef

39.

Honaker, J., King, G., Blackwell, M.: Amelia II: a program for missing data. J. Stat. Soft. 45, 1–47 (2011). https://doi.org/10.18637/jss.v045.i07CrossRef

40.

Epstein, S., Pacini, R., Denes-Raj, V., Heier, H.: Individual differences in intuitive-experiential and analytical-rational thinking styles. J. Pers. Soc. Psych. 71, 390–405 (1996). https://doi.org/10.1037/0022-3514.71.2.390CrossRef

41.

Cacioppo, J.T., Petty, R.E.: The need for cognition. J. Pers. Soc. Psych. 42, 116–131 (1982). https://doi.org/10.1037/0022-3514.42.1.116CrossRef

42.

Henrich, J., Heine, S.J., Norenzayan, A.: The weirdest people in the world. J. Behav. Brain Sci. 33, 61–83 (2010). https://doi.org/10.1017/S0140525X0999152XCrossRef

43.

Adelson, B.: When novices surpass experts: the difficulty of a task may increase with expertise. J. Exp. Psych: Learn. Mem. Cogn. 10, 483–495 (1984). https://doi.org/10.1037/0278-7393.10.3.483CrossRef

44.

Dror, I.E., Basola, B., Busemeyer, J.R.: Decision making under time pressure: an independent test of sequential sampling models. J. Mem. Cog. 27, 713–725 (1999). https://doi.org/10.3758/BF03211564CrossRef

45.

Brooks, P.B.: No Silver Bullet. Elsevier Science Publishers B.V, North-Holland (1986)

46.

Gibson, J.J.: The Senses Considered as Perceptual Systems. Houghton Mifflin, Oxford (1966)

47.

Storey, M.: Theories, methods and tools in program comprehension: past, present and future. In: 13th International Workshop on Program Comprehension, pp. 181–191. IEEE Press, New York (2005). https://doi.org/10.1109/wpc.2005.38

48.

Shneiderman, B.: Exploratory experiments in programmer behavior. J. Comp. Info. Sci. 5, 123–143 (1976). https://doi.org/10.1007/BF00975629MathSciNetCrossRefMATH

49.

Tait, P., Vessey, I.: The effect of user involvement on system success: a contingency approach. MIS Q. 12, 91–108 (1988). https://doi.org/10.2307/248809CrossRef

50.

Ichino, M., Kelleher, C.: The need for improved support for interacting with block examples. In: IEEE Blocks and Beyond Workshop, pp. 69–70. IEEE Press, Raleigh (2017). https://doi.org/10.1109/blocks.2017.8120415

51.

Altadmri, A., Brown, N.C.C.: 37 million compilations: investigating novice programming mistakes in large-scale student data. In: 46th ACM Technical Symposium on Computer Science Education, pp. 522–527. ACM, New York (2015). https://doi.org/10.1145/2676723.2677258

52.

Nakajima, H., Higo, Y., Yokoyama, H., Kusumoto, S.: Toward developer-like automated program repair – modification comparisons between GenProg and developers. In: 23rd Asia-Pacific Software Engineering Conference, pp. 241–248, IEEE Press, Hamilton (2016). https://doi.org/10.1109/apsec.2016.042

Titel: Trust in Automated Software Repair
verfasst von: Tyler J. Ryan
Gene M. Alarcon
Charles Walter
Rose Gamble
Sarah A. Jessup
August Capiola
Marc D. Pfahler
Verlag: Springer International Publishing
Buch: HCI for Cybersecurity, Privacy and Trust
Print ISBN: 978-3-030-22350-2

Electronic ISBN: 978-3-030-22351-9

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-22351-9_31

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"