Skip to main content

Property-Driven Timestamps Encoding for Timeprints-Based Tracing and Monitoring

  • Conference paper
  • First Online:
Formal Modeling and Analysis of Timed Systems (FORMATS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11750))

  • 446 Accesses


Timeprints are temporal regularly-logged signatures, describing a signal’s temporal behavior. They have been recently used in on-chip signals tracing and temporal properties checking. Timeprints are generated by aggregations of encoded timestamps marking where signal changes took place. This paper describes different timestamps encoding mechanisms, and shows how some system’s temporal properties can be used to create more efficient timestamps. The efficiency of a timestamps-encoding is introduced in terms of the number of collisions in the timeprints-reconstruction solution space. We show how using property-based timestamps encoding reduces the number of such collisions, leading to better chances capturing unexpected behaviors.

This work is supported by the DAAD, University of Bremen (SyDe graduate school and CRDF) and the BMBF grant SELFIE (grant no. 01IW16001).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. 1.

    If the signal change rate is known to be below certain limit the number of bits needed to describe the number of changes can still be less than \(log(m-1)\). This \(log(m-1)\) bits already covers the case of m changes as we aggregate timeprints recursively, i.e. the last timeprint of a trace-cycle is the initial value of the timeprint for the new trace-cycle; hence, if m=0 and the timeprint value changed, it means m changes took place, and if m=0 and the timeprint value is the same, then there has been zero changes in that trace-cycle.

  2. 2.

    Inc-Ind-k: means the incremental code Inc-Index with increments of weight k.

  3. 3.

    This set-up was already existing in our research-group within the bachelor’s-project DRIVE, and the data was obtained upon request from the students.


  1. ARM CoreSight and ETM (2018).

  2. (2018).

  3. (2018).

  4. Bartocci, E., et al.: Specification-based monitoring of cyber-physical systems: a survey on theory, tools and applications. In: Bartocci, E., Falcone, Y. (eds.) Lectures on Runtime Verification. LNCS, vol. 10457, pp. 135–175. Springer, Cham (2018).

    Chapter  Google Scholar 

  5. Chini, P., Massoud, R., Meyer, R., Saivasan, P.: Fast witness counting. CoRR abs/1807.05777 (2018).

  6. de Moura, L., Bjørner, N.: Z3: an efficient SMT solver. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 337–340. Springer, Heidelberg (2008).

    Chapter  Google Scholar 

  7. Giantamidis, G., Tripakis, S.: Learning moore machines from input-output traces. In: Fitzgerald, J., Heitmeyer, C., Gnesi, S., Philippou, A. (eds.) FM 2016. LNCS, vol. 9995, pp. 291–309. Springer, Cham (2016).

    Chapter  Google Scholar 

  8. Maler, O., Nickovic, D., Pnueli, A.: Checking temporal properties of discrete, timed and continuous behaviors. In: Avron, A., Dershowitz, N., Rabinovich, A. (eds.) Pillars of Computer Science. LNCS, vol. 4800, pp. 475–505. Springer, Heidelberg (2008).

    Chapter  MATH  Google Scholar 

  9. Massoud, R., Le, H.M., Chini, P., Saivasan, P., Meyer, R., Drechsler, R.: Temporal tracing of on-chip signals using timeprints. In: Design Automation Conference DAC-19 (2019).

  10. Mehrabian, M., et al.: Timestamp temporal logic (TTL) for testing the timing of cyber-physical systems. ACM Trans. Embed. Comput. Syst. 16(5s), 169:1–169:20 (2017).

    Article  Google Scholar 

  11. Schumann, J., Moosbrugger, P., Rozier, K.Y.: R2U2: monitoring and diagnosis of security threats for unmanned aerial systems. In: Bartocci, E., Majumdar, R. (eds.) RV 2015. LNCS, vol. 9333, pp. 233–249. Springer, Cham (2015).

    Chapter  Google Scholar 

  12. Park, S.B., Hong, T., Mitra, S.: Post-silicon bug localization in processors using instruction footprint recording and analysis (ifra). In: TCADIC (2009)

    Google Scholar 

  13. Vazquez-Chanlatte, M., Deshmukh, J.V., Jin, X., Seshia, S.A.: Logical clustering and learning for time-series data. In: Majumdar, R., Kunčak, V. (eds.) CAV 2017. LNCS, vol. 10426, pp. 305–325. Springer, Cham (2017).

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Rehab Massoud .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Massoud, R., Le, H.M., Drechsler, R. (2019). Property-Driven Timestamps Encoding for Timeprints-Based Tracing and Monitoring. In: André, É., Stoelinga, M. (eds) Formal Modeling and Analysis of Timed Systems. FORMATS 2019. Lecture Notes in Computer Science(), vol 11750. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29661-2

  • Online ISBN: 978-3-030-29662-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics