Automated Software Engineering

, Volume 22, Issue 2, pp 159–197 | Cite as

Building high assurance human-centric decision systems

  • Constance L. Heitmeyer
  • Marc Pickett
  • Elizabeth I. Leonard
  • Myla M. Archer
  • Indrakshi Ray
  • David W. Aha
  • J. Gregory Trafton
Article

Abstract

Many future decision support systems will be human-centric, i.e., require substantial human oversight and control. Because these systems often provide critical services, high assurance is needed that they satisfy their requirements. This paper, the product of an interdisciplinary research team of experts in formal methods, adaptive agents, and cognitive science, addresses this problem by proposing a new process for developing high assurance human-centric decision systems. This process uses AI (artificial intelligence) methods—i.e., a cognitive model to predict human behavior and an adaptive agent to assist the human—to improve system performance, and software engineering methods—i.e., formal modeling and analysis—to obtain high assurance that the system behaves as intended. The paper describes a new method for synthesizing a formal system model from Event Sequence Charts, a variant of Message Sequence Charts, and a Mode Diagram, a specification of system modes and mode transitions. It also presents results of a new pilot study investigating the optimal level of agent assistance for different users in which the agent design was evaluated using synthesized user models. Finally, it reviews a cognitive model for predicting human overload in complex human-centric systems. To illustrate the development process and our new techniques, we describe a human-centric decision system for controlling unmanned vehicles.

Keywords

High assurance Formal models Formal methods Adaptive agents Cognitive models Formal model synthesis from scenarios User model synthesis User scenarios System and software requirements 

References

  1. Alspaugh, T.A., Faulk, S.R., Britton, K.H., Parker, R.A., Parnas, D.L., Shore, J.E.: Software requirements for the A-7E aircraft. Tech. Rep. NRL-9194, Naval Research Laboratory, Washington, DC (1992)Google Scholar
  2. Alur, R., Yannakakis, M.: Model checking of Message Sequence Charts. Proceedings of the 10th International Conference on Concurrency Theory (CONCUR), pp. 114–129. Eindhoven, The Netherlands (1999)Google Scholar
  3. Archer, M.: TAME: Using PVS strategies for special-purpose theorem proving. Ann Math Artif Intell 29(1–4), 131–189 (2001)MathSciNetGoogle Scholar
  4. Bharadwaj, R., Heitmeyer, C.: Developing high assurance avionics systems with the SCR requirements method. In: Proceedings of the 19th Digital Avionics Systems Conference (DASC), Philadelphia, Pennsylvania (2000)Google Scholar
  5. Boussemart, Y., Cummings, M.: Behavioral recognition and prediction of an operator supervising multiple heterogeneous unmanned vehicles. In: Proceedings of the 1st International Conference on Humans Operating Unmanned Systems (HUMOUS), Brest, France (2008)Google Scholar
  6. Breslow, L.A., Gartenberg, D., McCurry, J.M., Trafton, J.G.: Dynamic operator overload: A model for predicting workload during supervisory control. IEEE Trans Hum Mach Syst 44(1), 30–40 (2014)CrossRefGoogle Scholar
  7. Bumiller, E., Shanker, T.: War evolves with drones, some tiny as bugs. New York Times, (2011)Google Scholar
  8. Crandall, J.W., Goodrich, M.A., D R Olsen, J., Nielsen, C.W.: Validating human-robot systems in multi-tasking environments. IEEE Transactions on Systems, Man, and Cybernetics 35(4), 438–449 (2005)Google Scholar
  9. Cummings, M.L., Mitchell, P.J.: Predicting controller capacity in supervisory control of multiple UAVs. IEEE Trans Syst Man Cybern 38(2), 451–460 (2008)CrossRefGoogle Scholar
  10. Damas, C., Lambeau, B., Dupont, P., van Lamsweerde, A.: Generating annotated behavior models from end-user scenarios. IEEE Trans Softw Eng 31(12), 1056–1073 (2005)CrossRefGoogle Scholar
  11. Damas, C., Lambeau, B., Roucoux, F., van Lamsweerde, A.: Analyzing critical process models through behavior model synthesis. In: Proceedings of the 31st International Conference on Software Engineering (ICSE), pp. 241–251. Vancouver, Canada (2009)Google Scholar
  12. DSB: The role of autonomy in DoD systems. Tech. rep., Defense Science Board, Office of the Under Secretary of Defense for Acquisition, Technology and Logistics, Washington, DC (2012)Google Scholar
  13. Fawcett, T.: An introduction to ROC analysis. Pattern Recognit Lett 27(8), 861–874 (2006)CrossRefMathSciNetGoogle Scholar
  14. Gargantini, A., Heitmeyer, C.: Using model checking to generate tests from requirements specifications. In: Proceedings of the 7th European Software Engineering Conference and 7th ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE), pp. 146–162. Toulouse, France (1999)Google Scholar
  15. Gartenberg, D., Breslow, L., Park, J., McCurry, J., Trafton, J.: Adaptive automation and cue invocation: The effect of cue timing on operator error. In: Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 3121–3130. France, Paris (2013)Google Scholar
  16. Giannakopoulou, D., Magee, J.: Fluent model checking for event-based systems. ACM SIGSOFT Softw Eng Notes 28, 257–266 (2003)CrossRefGoogle Scholar
  17. Gray, W.D., Boehm-Davis, D.A.: Milliseconds matter: An introduction to microstrategies and to their use in describing and predicting interactive behavior. J Exp Psychol 6(4), 322 (2000)Google Scholar
  18. Hanke, M., Halchenko, Y.O., Sederberg, P.B., Olivetti, E., Fründ, I., Rieger, J.W., Herrmann, C.S., Haxby, J.V., Hanson, S.J., Pollmann, S.: PyMVPA: a Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics 7(1), 37–53 (2009)CrossRefGoogle Scholar
  19. Heitmeyer, C., Jeffords, R.: Applying a formal requirements method to three NASA systems: Lessons learned. In: Proceedings of the IEEE Aerospace Conference, Big Sky, Montana, p 84 (2007)Google Scholar
  20. Heitmeyer, C., Kirby, J., Labaw, B., Archer, M., Bharadwaj, R.: Using abstraction and model checking to detect safety violations in requirements specifications. IEEE Trans Softw Eng 24(11), 927–948 (1998)CrossRefGoogle Scholar
  21. Heitmeyer, C., Archer, M., Bharadwaj, R., Jeffords, R.: Tools for constructing requirements specifications: The SCR toolset at the age of ten. Comput Syst Sci Eng 20(1), 19–35 (2005)Google Scholar
  22. Heitmeyer, C., Pickett, M., Breslow, L., Aha, D., Trafton, J.G., Leonard, E.: High assurance human-centric decision systems. In: Proc of the 2nd International NSF-Sponsored Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE) (2013a)Google Scholar
  23. Heitmeyer, C.L., Jeffords, R.D., Labaw, B.G.: Automated consistency checking of requirements specifications. ACM Trans Softw Eng Methodol 5(3), 231–261 (1996)CrossRefGoogle Scholar
  24. Heitmeyer, C.L., Archer, M.M., Leonard, E.I., McLean, J.D.: Applying formal methods to a certifiably secure software system. IEEE Trans Softw Eng 34(1), 82–98 (2008)CrossRefGoogle Scholar
  25. Heitmeyer, C.L., Shukla, S., Archer, M.M., Leonard, E.I.: On model-based software development. In: Munch, J., Schmid, K. (eds) Perspectives on the Future of Software Engineering, Springer, Berlin, Germany, pp 49–60 (2013b)Google Scholar
  26. Heninger, K.L.: Specifying software requirements for complex systems: New techniques and their application. IEEE Trans Softw Eng 6(1), 2–13 (1980)CrossRefGoogle Scholar
  27. ITU: Message Sequence Charts. Recommendation Z.120, Intern. Telecomm. Union, Telecomm. Standardization Sector (1999)Google Scholar
  28. Jeffords, R., Heitmeyer, C.: Automatic generation of state invariants from requirements specifications. In: Proceedings of the 6th ACM SIGSOFT Symposium on Foundations of Software Engineering (FSE), pp. 56–69. Lake Buena Vista, Florida (1998)Google Scholar
  29. Jeffords, R.D., Heitmeyer, C.L.: A strategy for efficiently verifying requirements. ACM SIGSOFT Softw Eng Notes 28, 28–37 (2003)CrossRefGoogle Scholar
  30. Just, M.A., Carpenter, P.A.: Eye fixations and cognitive processes. Cogn Psychol 8(4), 441–480 (1976)CrossRefGoogle Scholar
  31. Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Proceedings of the 9th International Workshop on Machine Learning (ML), pp. 249–256. Aberdeen, Scotland (1992)Google Scholar
  32. Leonard, E.I., Heitmeyer, C.L.: Program synthesis from formal requirements specifications using APTS. High Order Symb Comput 16(1–2), 63–92 (2003)CrossRefMATHGoogle Scholar
  33. Leonard, E.I., Archer, M., Heitmeyer, C.L., Jeffords, R.D.: Direct generation of invariants for reactive models. In: Proc. 10th ACM/IEEE Conference on Formal Methods and Models for Co-Design (MEMOCODE), pp 119–130 (2012)Google Scholar
  34. Pickett, M., Aha, D.W., Trafton, J.G.: Acquiring user models to test automated assistants. In: Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference (FLAIRS), pp 112–117 (2013)Google Scholar
  35. Ratwani, R., Trafton, J.G.: A real-time eye tracking system for predicting postcompletion errors. Hum Comput Interact 26(3), 205–245 (2011)Google Scholar
  36. Rayner, K.: Eye movements in reading and information processing: 20 years of research. Psychol Bull 124(3), 372 (1998)CrossRefGoogle Scholar
  37. Rayner, K., Morris, R.K.: Do eye movements reflect higher order processes in reading? In: From Eye to Mind. Information Acquisition in Perception, Search, and Reading, North-Holland, pp 191–204 (1990)Google Scholar
  38. Rothamel, T., Heitmeyer, C., Leonard, E., Liu, A.: Generating optimized code from SCR specifications. In: Proceedings of the ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES 2006) (2006)Google Scholar
  39. Sammut, C., Hurst, S., Kedzier, D., Michie, D.: Learning to fly. In: Sleeman, D.H., Edwards, P. (eds.) Proceedings of the 9th International Workshop on Machine Learning (ML), pp. 385–393. Morgan Kaufmann, Aberdeen, Scotland (1992)Google Scholar
  40. Selic, B.: The pragmatics of model-driven development. IEEE Softw 20(5), 19–25 (2003)CrossRefGoogle Scholar
  41. Sengupta, S.: U.S. border agency allows others to use its drones. New York Times, (2013)Google Scholar
  42. Šuc, D., Bratko, I., Sammut, C.: Learning to fly simple and robust. In: Proceedings of the 15th European Conference on Machine Learning (ECML), pp. 407–418. Pisa, Italy (2004)Google Scholar
  43. Swets, J.A.: Signal detection theory and ROC analysis in psychology and diagnostics: Collected Papers. Lawrence Erlbaum Associates, Mahawa (1996)MATHGoogle Scholar
  44. Uchitel, S., Kramer, J., Magee, J.: Synthesis of behavioral models from scenarios. IEEE Trans Softw Eng 29(2), 99–115 (2003)CrossRefGoogle Scholar
  45. Uchitel, S., Brunet, G., Chechik, M.: Synthesis of partial behaviour model synthesis from properties and scenarios. IEEE Trans Softw Eng 35(3), 384–406 (2009)CrossRefGoogle Scholar
  46. US Senate: The future of drones in America: law enforcement and privacy considerations, hearing before the Committee on the Judiciary. Tech. Rep. J-113-10, Washington, DC (2013)Google Scholar
  47. Whittle, J., Schumann, J.: Generating statechart designs from scenarios. In: Proceedings of the 22nd International Conference on Software Engineering (ICSE), pp. 314–323. Limerick, Ireland (2000)Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Constance L. Heitmeyer
    • 1
  • Marc Pickett
    • 2
  • Elizabeth I. Leonard
    • 1
  • Myla M. Archer
    • 1
  • Indrakshi Ray
    • 3
  • David W. Aha
    • 1
  • J. Gregory Trafton
    • 1
  1. 1.Naval Research LaboratoryWashingtonUSA
  2. 2.Google, Inc.Mountain ViewUSA
  3. 3.Colorado State UniversityFort CollinsUSA

Personalised recommendations