ScAmPER: Generating Test Suites to Maximise Code Coverage in Interactive Fiction Games

  • Martin Mariusz LesterEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12165)


We present ScAmPER, a tool that generates test suites that maximise coverage for a class of interactive fiction computer games from the early 1980s. These games customise a base game engine with scripts written in a simple language. The tool uses a heuristic-guided search to evaluate whether these lines of code can in fact be executed during gameplay and, if so, outputs a sequence of game inputs that achieves this. Equivalently, the tool can be seen as attempting to generate a set of test cases that maximises coverage of the scripted code. The tool also generates a visualisation of the search process.


Reachability Coverage Explicit state Interactive fiction 

Supplementary material


  1. 1.
    Adams, S.: Scott adams grand adventures.
  2. 2.
  3. 3.
    Dietsch, D., Jakobs, M.C.: Tap 2020 virtual machine, April 2020.
  4. 4.
    Dubois, C., Wolff, B. (eds.): TAP 2018. LNCS, vol. 10889. Springer, Cham (2018). Scholar
  5. 5.
    Fuchs, A.: Automated test case generation for Java EE based web applications. In: Dubois and Wolff [4], pp. 167–176.
  6. 6.
    Gibson-Robinson, T., Armstrong, P., Boulgakov, A., Roscoe, A.W.: FDR3—a modern refinement checker for CSP. In: Ábrahám, E., Havelund, K. (eds.) TACAS 2014. LNCS, vol. 8413, pp. 187–201. Springer, Heidelberg (2014). Scholar
  7. 7.
    Holzmann, G.J.: The SPIN Model Checker - Primer and Reference Manual. Addison-Wesley, Boston (2004)Google Scholar
  8. 8.
    Julliand, J., Kouchnarenko, O., Masson, P., Voiron, G.: Under-approximation generation driven by relevance predicates and variants. In: Dubois and Wolff [4], pp. 63–82.
  9. 9.
    Lester, M.M.: ScAmPER: Scott Adams exPlicitly Evaluating Reachability, March 2020.
  10. 10.
    Mnih, V., et al.: Playing atari with deep reinforcement learning. CoRR abs/1312.5602 (2013).
  11. 11.
    Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529 (2015)CrossRefGoogle Scholar
  12. 12.
    Narasimhan, K., Kulkarni, T.D., Barzilay, R.: Language understanding for text-based games using deep reinforcement learning. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, 17–21 September 2015, pp. 1–11 (2015).
  13. 13.
    Pickett, C.J., Verbrugge, C., Martineau, F.: NFG: a language and runtime system for structured computer narratives. In: Proceedings of the 1st Annual North American Game-On Conference (GameOn’NA 2005), pp. 23–32 (2005)Google Scholar
  14. 14.
    Taylor, M.: Scott adams compiler (sac).
  15. 15.
    Verbrugge, C., Zhang, P.: Analyzing computer game narratives. In: Yang, H.S., Malaka, R., Hoshino, J., Han, J.H. (eds.) ICEC 2010. LNCS, vol. 6243, pp. 224–231. Springer, Heidelberg (2010). Scholar
  16. 16.
    Vos, T.E.J., Kruse, P.M., Condori-Fernández, N., Bauersfeld, S., Wegener, J.: TESTAR: tool support for test automation at the user interface level. IJISMD 6(3), 46–83 (2015). Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of ReadingReadingUK

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