Does Visualization Speed Up the Safety Analysis Process?

  • Ragaad AlTarawneh
  • Max Steiner
  • Davide Taibi
  • Shah Rukh Humayoun
  • Peter Liggesmeyer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8696)

Abstract

The goal of this paper is to present our experience in utilizing the power of the information visualization (InfoVis) field to accelerate the safety analysis process of Component Fault Trees (CFT) in embedded systems. For this, we designed and implemented an interactive visual tool called ESSAVis, which takes the CFT model as input and then calculates the required safety information (e.g., the information on minimal cut sets and their probabilities) that is needed to measure the safety criticality of the underlying system. ESSAVis uses this information to visualize the CFT model and allows users to interact with the produced visualization in order to extract the relevant information in a visual form. We compared ESSAVis with ESSaRel, a tool that models the CFT and represents the analysis results in textual form. We conducted a controlled user evaluation study where we invited 25 participants from different backgrounds, including 6 safety experts, to perform a set of tasks to analyze the safety aspects of a given system in both tools. We compared the results in terms of accuracy, efficiency, and level of user acceptance. The results of our study show a high acceptance ratio and higher accuracy with better performance for ESSAVis compared to the text-based tool ESSaRel. Based on the study results, we conclude that visual-based tools really help in analyzing the CFT model more accurately and efficiently. Moreover, the study opens the door to thoughts about how the power of visualization can be utilized in such domains to accelerate the safety assurance process in embedded systems.

Keywords

Embedded Systems Safety Analysis Information Visuali- zation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lee, E.A., Seshia, S.A.: Introduction to Embedded Systems - A Cyber-Physical Systems Approach, 1 edn. Lee and Seshia (2010)Google Scholar
  2. 2.
    Kaiser, B., Liggesmeyer, P., Mäckel, O.: A new component concept for fault trees. Reproduction 33, 37–46 (2003)Google Scholar
  3. 3.
    Bozzano, M., Villafiorita, A.: Design and Safety Assessment of Critical Systems. CRC Press (Taylor and Francis), an Auerbach Book (2010)Google Scholar
  4. 4.
    Kaiser, B., Gramlich, C., Förster, M.: State/event fault trees - a safety analysis model for software-controlled systems. Reliability Engineering System Safety 92, 1521–1537 (2007)CrossRefGoogle Scholar
  5. 5.
    Weber, M.: A survey of semantic annotations for knowledge management. DFKI GmbH, p. 1 (2008)Google Scholar
  6. 6.
    AlTarawneh, R., Bauer, J., Keller, P., Ebert, A.: Essavis: A 2Dplus3D visual platform for speeding up the maintenance process of embedded systems. In: BCS HCI 2013 (2013)Google Scholar
  7. 7.
    AlTarawneh, R., Bauer, J., Humayoun, S.R., Ebert, A., Liggesmeyer, P.: Enhancing understanding of safety aspects in embedded systems through an interactive visual tool. In: IUI Companion 2014, pp. 9–12. ACM (2013)Google Scholar
  8. 8.
    Software Engineering Research Group: Dependability Kaiserslautern University, Essarel Tool: Embedded systems safety and reliability analyser (2014), http://essarel.de
  9. 9.
    CESAR Project: cesar project report (2010), http://www.cesarproject.eu
  10. 10.
    Bieber, P., Bougnol, C., Castel, C., Heckmann, J.-L., Kehren, C., Seguin, C.: Safety assessment with altarica - lessons learnt based on two aircraft system studies. In: 18th IFIP World Computer Congress, Topical Day on New Methods for Avionics Certification, p. 26 (2004)Google Scholar
  11. 11.
    Gelfand, N., Tamassia, R.: Algorithmic patterns for orthogonal graph drawing. In: Whitesides, S.H. (ed.) GD 1998. LNCS, vol. 1547, pp. 138–152. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  12. 12.
    AlTarawneh, R., Johannes, S., Humayoun, S.R.: Clue: An algorithm for expanding clustered graphs. In: 7th IEEE Pacific Visualization Symposium (PacificVis 2014), Yokohama, Japan (2014)Google Scholar
  13. 13.
    Basili, V.R., Caldiera, G., Rombach, H.D.: The goal question metric approach. In: Encyclopedia of Software Engineering. Wiley (1994)Google Scholar
  14. 14.
    Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: Toward a unified view. MIS Q. 27, 425–478 (2003)Google Scholar
  15. 15.
    Dix, A., Finlay, J.E., Abowd, G.D., Beale, R.: Human-Computer Interaction, 3rd edn. Prentice-Hall, Inc., Upper Saddle River (2003)Google Scholar
  16. 16.
    Proetzsch, M.: Development Process for Complex Behavior-Based Robot Control Systems. RRLab Dissertations. Verlag Dr. Hut (2010) ISBN: 978-3-86853-626-3Google Scholar
  17. 17.
    Vesely, W.: Fault Tree Handbook with Aerospace Applications. NASA (2002)Google Scholar
  18. 18.
    Shapiro, S.S., Wilk, M.B.: An analysis of variance test for normality (complete samples). Biometrika 52, 591–611 (1965)CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ragaad AlTarawneh
    • 1
  • Max Steiner
    • 2
  • Davide Taibi
    • 3
  • Shah Rukh Humayoun
    • 1
  • Peter Liggesmeyer
    • 2
  1. 1.Computer Graphics and HCIUniversity of KaiserslauternKaiserslauternGermany
  2. 2.Software Engineering: DependabilityUniversity of KaiserslauternKaiserslauternGermany
  3. 3.Software Engineering: Processes and MeasurementUniversity of KaiserslauternKaiserslauternGermany

Personalised recommendations