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arxiv: 2501.05792 · v1 · submitted 2025-01-10 · 💻 cs.SE

Test Case Generation for Simulink Models: An Experience from the E-Bike Domain

Michael Marzella , Andrea Bombarda , Marcello Minervini , Nunzio Marco Bisceglia , Angelo Gargantini , Claudio Menghi This is my paper

Pith reviewed 2026-05-23 05:31 UTC · model grok-4.3

classification 💻 cs.SE
keywords search-based software testingSimulinktest case generationcyber-physical systemse-Bikemodel-based testingfailure detection
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The pith

An SBST tool located failure-revealing test cases for 83 percent of experiments on e-Bike Simulink controllers.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper reports an industrial-style assessment of search-based software testing on Simulink models that implement the motor controllers of electric bikes. It applies the HECATE framework to two controllers and measures how often the generated tests expose violations of functional, regulatory, and safety requirements. In 30 of 36 runs the tool produced such tests, the average run took roughly 77 minutes, and the model developer verified that the violations were real defects. The results supply concrete data on effectiveness and runtime that industries need before adopting these techniques more widely.

Core claim

HECATE successfully identified failure-revealing test cases for 83% (30 out of 36) of the experiments. It required, on average, 1 hour 17 minutes 26 seconds to compute the failure-revealing test cases. The developer of the e-Bike model confirmed the failures identified by HECATE.

What carries the argument

HECATE, a search-based software testing framework that searches the input space of a Simulink model to find test inputs that violate given requirements.

If this is right

  • Search-based testing can locate defects in industrial Simulink models drawn from the e-Bike domain.
  • The reported runtimes supply a concrete baseline for the computational cost of applying SBST to controller models of this complexity.
  • Developer confirmation of the detected failures indicates that the generated tests align with real acceptance criteria.
  • The lessons learned can guide adjustments to testing practice when moving SBST from research prototypes to production models.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar controller models in other vehicle or robotics domains could be tested with the same framework to check whether the 83 percent success rate holds.
  • The wide spread in computation time (12 minutes to over 8 hours) points to possible gains from adding early termination or model-size heuristics.
  • The approach could be extended to compare HECATE against other Simulink testing tools on the same e-Bike benchmarks to isolate the contribution of its search algorithm.

Load-bearing premise

The functional, regulatory, and safety requirements used in the experiments accurately represent the intended behavior of the e-bike controllers, and the failures detected correspond to genuine defects rather than modeling artifacts or requirement misinterpretations.

What would settle it

An independent re-execution of the same test cases on the original Simulink models that shows they do not actually violate the stated requirements or that the violations are not treated as defects by the model developer.

Figures

Figures reproduced from arXiv: 2501.05792 by Andrea Bombarda, Angelo Gargantini, Claudio Menghi, Marcello Minervini, Michael Marzella, Nunzio Marco Bisceglia.

Figure 1
Figure 1. Figure 1: Simulink® model for the e-Bike [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Two software controllers for the e-Bike. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Test Blocks for our e-Bike model. 𝑡𝑖𝑚𝑒 [𝑠] 𝑠𝑝𝑒𝑒𝑑 [𝑟𝑝𝑚] 10 5 10 15 20 25 30 35 (a) Truncated pyramid input with Hecate_sp equal to 10. 𝑡𝑖𝑚𝑒 [𝑠] 𝑠𝑝𝑒𝑒𝑑 [𝑟𝑝𝑚] 10 5 10 15 20 25 30 35 (b) Rectangular pulse input with Hecate_sp equal to 10 [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Signal types generated as input by HECATE for our e-Bike models. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Parameterized Test Sequences. 1 Test Sequence Generation 2 System Execution 3 Fitness Assessment 𝑇 TS 𝑆 𝑓 (𝑇𝐴) 𝑆 (TS) TS/NFF 𝑇𝐴 [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Overview of the HECATE framework. HECATE stops the search procedure if a failure-revealing Test Sequence is found or the maximum time 𝑇 allotted for the search is reached. The main advantage of HECATE over the existing framework is that it relies on Test Sequences and Test Assessments, which are part of Simulink®. Therefore, engineers can start from manually defined test cases, parameterize them, and use t… view at source ↗
Figure 7
Figure 7. Figure 7: Desired (orange) and actual (blue) speeds of the e-Bike for the PWM controller and requirement (R1). [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Desired (orange) and actual speeds for the PWM (blue) and Buck (green) controllers and requirement (R2). [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Desired (orange) and actual speeds for the PWM (blue) and Buck (green) controllers and requirement (R3). [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Time required to generate the failure-revealing [PITH_FULL_IMAGE:figures/full_fig_p009_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Physical test bench. L3 (Comparison of Solutions). HECATE (and SBST in general) was beneficial for assessing the benefits and limitations of different controller implementations. Specifically, in our case, based on the feedback provided by HECATE, we were able to assess when the PWM and Buck worked properly and select in which scenario using each controller can be beneficial. Considering these lessons, th… view at source ↗
read the original abstract

Cyber-physical systems development often requires engineers to search for defects in their Simulink models. Search-based software testing (SBST) is a standard technology that supports this activity. To increase practical adaption, industries need empirical evidence of the effectiveness and efficiency of (existing) SBST techniques on benchmarks from different domains and of varying complexity. To address this industrial need, this paper presents our experience assessing the effectiveness and efficiency of SBST in generating failure-revealing test cases for cyber-physical systems requirements. Our study subject is within the electric bike (e-Bike) domain and concerns the software controller of an e-Bike motor, particularly its functional, regulatory, and safety requirements. We assessed the effectiveness and efficiency of HECATE, an SBST framework for Simulink models, to analyze two software controllers. HECATE successfully identified failure-revealing test cases for 83% (30 out of 36) of our experiments. It required, on average, 1 h 17 min 26 s (min = 11 min 56 s, max = 8 h 16 min 22 s, std = 1 h 50 min 34 s) to compute the failure-revealing test cases. The developer of the e-Bike model confirmed the failures identified by HECATE. We present the lessons learned and discuss the relevance of our results for industrial applications, the state of practice improvement, and the results' generalizability.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript is an experience report assessing the HECATE SBST framework for generating failure-revealing test cases for Simulink models of e-Bike motor controllers. It evaluates two controllers against functional, regulatory, and safety requirements, reporting that HECATE succeeded in 30 out of 36 experiments with an average runtime of 1 hour 17 minutes 26 seconds. Developer confirmation of the failures is provided, along with lessons learned and discussion of industrial relevance.

Significance. This study offers empirical data from a practical industrial domain on the performance of SBST for cyber-physical systems. The success rate and runtime statistics, if verified, contribute to evidence-based adoption of such tools. The inclusion of developer validation strengthens the practical implications for improving state of practice in model-based testing.

major comments (2)
  1. [Abstract] Abstract: The reported 83% success rate (30/36) and runtime statistics (including min/max/std) are presented without reference to the underlying experimental design, including how the 36 experiments were constructed, the distribution of requirements across the two controllers, or measures taken to control for stochasticity in the search algorithm; this information is load-bearing for evaluating the reliability of the effectiveness claim.
  2. [Experiments] Experiments section: Validation of the 30 identified failures relies solely on post-experiment developer confirmation rather than an a priori independent oracle, formal specification of expected behaviors, or cross-check against the original regulatory/safety documents; this directly affects whether the failures represent genuine defects or requirement misinterpretations.
minor comments (2)
  1. [Abstract] The abstract reports aggregate runtime but does not break down success/failure by controller or requirement category, which would aid assessment of generalizability.
  2. Consider including a dedicated threats-to-validity subsection discussing the representativeness of the two e-Bike models and the potential for modeling artifacts.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our experience report. We address each major comment below and indicate planned revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The reported 83% success rate (30/36) and runtime statistics (including min/max/std) are presented without reference to the underlying experimental design, including how the 36 experiments were constructed, the distribution of requirements across the two controllers, or measures taken to control for stochasticity in the search algorithm; this information is load-bearing for evaluating the reliability of the effectiveness claim.

    Authors: The abstract is intentionally concise, with full details on experiment construction, requirement distribution across the two controllers, and the experimental protocol provided in the Experiments section. To mitigate stochasticity, each experiment used a fixed random seed and was executed with a consistent search budget. We agree that a brief reference would aid readers and will revise the abstract to note the experimental setup and that 36 experiments were derived from the requirements of the two controllers. revision: yes

  2. Referee: [Experiments] Experiments section: Validation of the 30 identified failures relies solely on post-experiment developer confirmation rather than an a priori independent oracle, formal specification of expected behaviors, or cross-check against the original regulatory/safety documents; this directly affects whether the failures represent genuine defects or requirement misinterpretations.

    Authors: As an industrial experience report, validation used post-experiment confirmation by the model developer, who possesses detailed knowledge of the controllers and requirements. Failures were discussed against the original regulatory and safety documents during confirmation. We recognize this is not an a priori formal oracle and will add explicit discussion of this validation approach, its alignment with industrial practice, and associated limitations in the Threats to Validity section. revision: partial

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is a straightforward empirical report of SBST tool performance on two specific e-Bike Simulink controllers. It states measured outcomes (30/36 experiments yielded failure-revealing cases, average runtime 1h17m26s, developer confirmation) without any equations, derivations, fitted parameters, or predictions that reduce to inputs by construction. No self-citation chain, uniqueness theorem, or ansatz is invoked to support a central claim; the requirements are treated as given inputs and results are externally validated by the model developer. The work is self-contained against its stated benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is an empirical evaluation of an existing tool on new subject systems. No mathematical derivations, fitted parameters, or postulated entities are introduced.

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Failure Modes and Effects Analysis: An Experience from the E-Bike Domain

    cs.SE 2025-09 accept novelty 3.0

    An industrial experience report applying simulation-driven FMEA to an e-bike CPS, where expert validation confirmed model accuracy and revealed unexpected fault effects in 5 of 13 cases.

Reference graph

Works this paper leans on

79 extracted references · 79 canonical work pages · cited by 1 Pith paper

  1. [1]

    Carmelina Abagnale, Massimo Cardone, Paolo Iodice, Renato Marialto, Salvatore Strano, Mario Terzo, and Giovanni Vorraro. 2016. Design and Development of an Innovative E-Bike. Energy Procedia 101 (2016), 774–781. https://doi.org/10. 1016/j.egypro.2016.11.098

    work page 2016

  2. [2]

    Ahmed, Angelo Gargantini, and Miroslav Bures

    Bestoun S. Ahmed, Angelo Gargantini, and Miroslav Bures. 2020. An Automated Testing Framework For Smart TV apps Based on Model Separation. In 2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 62–73. https://doi.org/10.1109/icstw50294.2020.00026

    work page doi:10.1109/icstw50294.2020.00026 2020

  3. [3]

    Briand, Hadi Hemmati, and Rajwinder Kaur Panesar- Walawege

    Shaukat Ali, Lionel C. Briand, Hadi Hemmati, and Rajwinder Kaur Panesar- Walawege. 2010. A Systematic Review of the Application and Empirical Inves- tigation of Search-Based Test Case Generation. IEEE Transactions on Software Engineering 36, 6 (2010), 742–762. https://doi.org/10.1109/TSE.2009.52

    work page doi:10.1109/tse.2009.52 2010

  4. [4]

    Yashwanth Annapureddy, Che Liu, Georgios Fainekos, and Sriram Sankara- narayanan. 2011. S-taliro: a tool for temporal logic falsification for hybrid sys- tems. In International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS). Springer-Verlag, 254–257

    work page 2011

  5. [5]

    Paolo Arcaini, Elvinia Riccobene, Angelo Gargantini, et al. 2016. Model-Based Offline and Online Testing for Medical Software. In European & Asian Sys- tem, Software & Service Process Improvement & Innovation (EuroAsiaSPI2 2016) . WHITEBOX, 11–20

    work page 2016

  6. [6]

    International Competition on Verifying Continuous and Hybrid Systems

    ARCH-COMP 2022 [Online]. International Competition on Verifying Continuous and Hybrid Systems . ARCH-COMP. Retrieved April 2022 from https://cps- vo.org/group/ARCH/FriendlyCompetition

    work page 2022

  7. [7]

    Aitor Arrieta, Shuai Wang, Urtzi Markiegi, Goiuria Sagardui, and Leire Etxeberria

    work page

  8. [8]

    In IEEE Congress on Evolutionary Computation (CEC)

    Search-based Test Case Generation for Cyber-Physical Systems. In IEEE Congress on Evolutionary Computation (CEC) . IEEE Press, 688–697. https://doi. org/10.1109/CEC.2017.7969377

    work page doi:10.1109/cec.2017.7969377 2017

  9. [9]

    Mostafa Ayesh, Namya Mehan, Ethan Dhanraj, Abdul El-Rahwan, Simon Emil Opalka, Tony Fan, Akil Hamilton, Akshay Mathews Jacob, Rahul Anthony Sun- darrajan, Bryan Widjaja, et al. 2022. Two Simulink Models With Requirements for a Simple Controller of a Pacemaker Device. In International Workshop on Applied Verification of Continuous and Hybrid Systems , Vol....

    work page 2022

  10. [10]

    Lethbridge, and Maged Elassar

    Omar Badreddin, Timothy C. Lethbridge, and Maged Elassar. 2013. Modeling Practices in Open Source Software. In Open Source Software: Quality Verification. Springer, 127–139

    work page 2013

  11. [11]

    Luciano Baresi, Marcio Delamaro, and Paulo Nardi. 2017. Test Oracles for Simulink-Like Models. Automated Software Engineering 24, 2 (2017), 369–391

    work page 2017

  12. [12]

    Alexander Boll, Florian Brokhausen, Tiago Amorim, Timo Kehrer, and Andreas Vogelsang. 2021. Characteristics, Potentials, and Limitations of Open-Source Simulink Projects for Empirical Research. Software and Systems Modeling 20, 6 (2021), 2111–2130

    work page 2021

  13. [13]

    Alexander Boll and Timo Kehrer. 2020. On the Replicability of Experimental Tool Evaluations in Model-Based Development: Lessons Learnt from a Systematic Literature Review Focusing on MATLAB/Simulink. In Systems Modelling and Management. Springer, 111–130. https://doi.org/10.1007/978-3-030-58167-1_9

    work page doi:10.1007/978-3-030-58167-1_9 2020

  14. [14]

    Alexander Boll, Nicole Vieregg, and Timo Kehrer. 2024. Replicability of Exper- imental Tool Evaluations in Model-Based Software and Systems Engineering With MATLAB/Simulink. Innovations in Systems and Software Engineering 20, 3 (2024), 209–224

    work page 2024

  15. [15]

    Andrea Bombarda, Silvia Bonfanti, and Angelo Gargantini. 2022. Automatic Test Generation with ASMETA for the Mechanical Ventilator Milano Controller. In Testing Software and Systems. Springer International Publishing, Cham, 65–72. https://doi.org/10.1007/978-3-031-04673-5_5

    work page doi:10.1007/978-3-031-04673-5_5 2022

  16. [16]

    Fabio Corti, Marcello Minervini, Paolo Giangrande, Alberto Reatti, Paolo Ma- lighetti, and Luca Pugi. 2024. Simulink-Based Simulation of Electric Bicy- cle Dynamics and Regenerative Braking for Battery State of Charge Assess- ment. In Mediterranean Electrotechnical Conference (MELECON) . IEEE, 803–808. https://doi.org/10.1109/melecon56669.2024.10608778

    work page doi:10.1109/melecon56669.2024.10608778 2024

  17. [17]

    Wei Ding, Peng Liang, Antony Tang, Hans van Vliet, and Mojtaba Shahin

    work page

  18. [18]

    In International Conference on Engineering of Com- plex Computer Systems (ICECCS)

    How Do Open Source Communities Document Software Architecture: An Exploratory Survey. In International Conference on Engineering of Com- plex Computer Systems (ICECCS) . IEEE Computer Society, 136–145. https: //doi.org/10.1109/ICECCS.2014.26

    work page doi:10.1109/iceccs.2014.26 2014

  19. [19]

    Alexandre Donzé. 2010. Breach, A Toolbox for Verification and Parameter Synthe- sis of Hybrid Systems. In Computer Aided Verification (CA V). Springer, 167–170

    work page 2010

  20. [20]

    Tore Dyba, Barbara A Kitchenham, and Magne Jorgensen. 2005. Evidence-Based Software Engineering for Practitioners. IEEE software 22, 1 (2005), 58–65

    work page 2005

  21. [21]

    Emelie Engström and Kai Petersen. 2015. Mapping Software Testing Practice With Software Testing Research — SERP-Test taxonomy. In IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). 1–4. https://doi.org/10.1109/ICSTW.2015.7107470

    work page doi:10.1109/icstw.2015.7107470 2015

  22. [22]

    Gidon Ernst, Paolo Arcaini, Georgios Fainekos, Federico Formica, Jun Inoue, Tanmay Khandait, Mohammad Mahdi Mahboob, Claudio Menghi, Giulia Pedrielli, Masaki Waga, Yoriyuki Yamagata, and Zhenya Zhang. 2022. ARCH-COMP 2022 Category Report: Falsification with Ubounded Resources. In International Workshop on Applied Verification of Continuous and Hybrid Syst...

    work page 2022

  23. [23]

    Gidon Ernst, Sean Sedwards, Zhenya Zhang, and Ichiro Hasuo. 2019. Fast Falsifi- cation of Hybrid Systems Using Probabilistically Adaptive Input. In Quantitative Evaluation of Systems . Springer, 165–181. https://doi.org/10.1007/978-3-030- 30281-8_10

    work page doi:10.1007/978-3-030- 2019

  24. [24]

    Fainekos, Sriram Sankaranarayanan, Koichi Ueda, and Hakan Yazarel

    Georgios E. Fainekos, Sriram Sankaranarayanan, Koichi Ueda, and Hakan Yazarel

    work page

  25. [25]

    In2012 American Control Conference (ACC)

    Verification of Automotive Control Applications Using S-TaLiRo. In2012 American Control Conference (ACC). 3567–3572. https://doi.org/10.1109/ACC. 2012.6315384

    work page doi:10.1109/acc 2012

  26. [26]

    Federico Formica, Tony Fan, and Claudio Menghi. 2023. Search-Based Software Testing Driven by Automatically Generated and Manually Defined Fitness Func- tions. ACM Transactions on Software Engineering and Methodology 33, 2, Article 40 (2023), 37 pages. https://doi.org/10.1145/3624745

    work page doi:10.1145/3624745 2023

  27. [27]

    Federico Formica, Tony Fan, Akshay Rajhans, Vera Pantelic, Mark Lawford, and Claudio Menghi. 2024. Simulation-Based Testing of Simulink Models With Test Sequence and Test Assessment Blocks. IEEE Transactions on Software Engineering 50, 2 (Feb. 2024), 239–257. https://doi.org/10.1109/tse.2023.3343753

    work page doi:10.1109/tse.2023.3343753 2024

  28. [28]

    Federico Formica, Nicholas Petrunti, Lucas Bruck, Vera Pantelic, Mark Law- ford, and Claudio Menghi. 2023. Test Case Generation for Drivability Re- quirements of an Automotive Cruise Controller: An Experience with an In- dustrial Simulator. In Joint European Software Engineering Conference and Sym- posium on the Foundations of Software Engineering (ESEC/F...

    work page doi:10.1145/3611643.3613894 2023

  29. [29]

    FOSELAB. 2024. Experimental Results. https://github.com/foselab/HECATE- Bikes. Accessed: November 19, 2024

    work page 2024

  30. [30]

    Vinay Gupta, Jitesh Kumawat, Rupendra Kumar Pachauri, and Shashikant. 2024. Design and Development Gear-Electric Bike and Performance Testing for Indian Road Conditions. Springer, 469–479

    work page 2024

  31. [31]

    Mark Harman, Yue Jia, and Yuanyuan Zhang. 2015. Achievements, Open Problems and Challenges for Search Based Software Testing. In IEEE Interna- tional Conference on Software Testing, Verification and Validation (ICST) . 1–12. https://doi.org/10.1109/ICST.2015.7102580

    work page doi:10.1109/icst.2015.7102580 2015

  32. [32]

    Mark Harman and Bryan F. Jones. 2001. Search-Based Software Engineering. Information and Software Technology 43, 14 (2001), 833–839. https://doi.org/10. 1016/s0950-5849(01)00189-6

    work page 2001

  33. [33]

    Jörg Holtmann, Grischa Liebel, and Jan-Philipp Steghöfer. 2024. Processes, Meth- ods, and Tools in Model-Based Engineering—A Qualitative Multiple-Case Study. Journal of Systems and Software 210 (2024), 111943

    work page 2024

  34. [34]

    Dmytro Humeniuk, Foutse Khomh, and Giuliano Antoniol. 2022. A Search- Based Framework for Automatic Generation of Testing Environments for Cy- ber–Physical Systems. Information and Software Technology 149 (Sept. 2022), 106936. https://doi.org/10.1016/j.infsof.2022.106936

    work page doi:10.1016/j.infsof.2022.106936 2022

  35. [35]

    Mathworks Inc. 2024. Add-Ons. https://www.mathworks.com/products/simulink.html. Accessed: November 19, 2024

    work page 2024

  36. [36]

    Mathworks Inc. 2024. Design. Simulate. Deplo. https://www.mathworks.com/help/matlab/add-ons.html. Accessed: No- vember 19, 2024

    work page 2024

  37. [37]

    Mathworks Inc. 2024. Simulink Test Develop, Manage, and Execute Simulation- Based Tests. https://www.mathworks.com/products/simulink-test.html. Ac- cessed: November 7, 2024

    work page 2024

  38. [38]

    Mathworks Inc. 2024. Test Assessment. https://www.mathworks.com/help/sltest/ ref/testassessment.html. Accessed: November 7, 2024

    work page 2024

  39. [39]

    Mathworks Inc. 2024. Test Sequence Basics. https://www.mathworks.com/help/ sltest/ug/introduction-to-test-sequences.html. Accessed: November 7, 2024

    work page 2024

  40. [40]

    Mathworks Inc. 2024. Use Test Sequence Scenarios in the Test Sequence Editor and Test Manager. Accessed: November 7, 2024

    work page 2024

  41. [41]

    Fortune Business Insights. 2024. E-Bike Drive Unit Market Size, Share & COVID- 19 Impact Analysis, By Product Type (Mid-drive Motors and Hub Motors), By Application (OEM and Aftermarket), and Regional Forecasts, 2023-2030. https:// www.fortunebusinessinsights.com/e-bike-drive-unit-market-107520. Accessed: November 7, 2024

    work page 2024

  42. [42]

    Natalia Juristo and Omar S Gómez. 2012. Replication of Software Engineering Ex- periments. Empirical Software Engineering and Verification: International Summer Schools, LASER 2008-2010, Revised Tutorial Lectures (2012), 60–88

    work page 2012

  43. [43]

    Tanmay Khandait, Federico Formica, Paolo Arcaini, Surdeep Chotaliya, Georgios Fainekos, Abdelrahman Hekal, Atanu Kundu, Ethan Lew, Michele Loreti, Claudio Menghi, Laura Nenzi, Giulia Pedrielli, Jarkko Peltomäki, Ivan Porres, Rajarshi Ray, Valentin Soloviev, Ennio Visconti, Masaki Waga, and Zhenya Zhang. 2024. ARCH-COMP 2024 Category Report: Falsification....

    work page doi:10.29007/hgfv 2024

  44. [44]

    Barbara Kitchenham, Tore Dyba, and Magne Jorgensen. 2004. Evidence-Based Software Engineering. In International Conference on Software Engineering . 273–

    work page 2004

  45. [45]

    https://doi.org/10.1109/ICSE.2004.1317449

    work page doi:10.1109/icse.2004.1317449 2004

  46. [46]

    Raluca Lefticaru, Savas Konur, Unal Yildirim, Amad Uddin, Felician Campean, and Marian Gheorghe. 2017. Towards an Integrated Approach to Verification FSE ’25, June 23–27, 2025, Trondheim, Norway Marzella et al. and Model-Based Testing in System Engineering. In IEEE International Confer- ence on Internet of Things (iThings) and IEEE Green Computing and Com...

    work page doi:10.1109/ithings- 2017

  47. [47]

    Grischa Liebel, Nadja Marko, Matthias Tichy, Andrea Leitner, and Jörgen Hansson

    work page

  48. [48]

    Software & Systems Modeling 17 (2018), 91–113

    Model-Based Engineering in the Embedded Systems Domain: An Industrial Survey on the State-of-Practice. Software & Systems Modeling 17 (2018), 91–113

    work page 2018

  49. [49]

    Xiao Ling and Tim Menzies. 2023. On the Benefits of Semi-Supervised Test Case Generation for Simulation Models. https://doi.org/10.48550/ARXIV.2305.03714

    work page doi:10.48550/arxiv.2305.03714 2023

  50. [50]

    Hayati Mamur and Alper Kağan Candan. 2020. Detailed Simulation of Re- generative Braking of BLDC Motor for Electric Vehicles. Bilge International Journal of Science and Technology Research 4, 2 (Sept. 2020), 63–72. https: //doi.org/10.30516/bilgesci.646901

    work page doi:10.30516/bilgesci.646901 2020

  51. [51]

    Mathworks. 2024. Introduction to Brushless DC Motor Control. https://www.mathworks.com/campaigns/offers/next/introduction-to- brushless-dc-motor-control.html. Accessed: November 7, 2024

    work page 2024

  52. [52]

    Briand, and Thomas Bruckmann

    Reza Matinnejad, Shiva Nejati, Lionel C. Briand, and Thomas Bruckmann. 2016. Automated Test Suite Generation for Time-Continuous Simulink Models. In International Conference on Software Engineering (ICSE) . ACM, 595–606. https: //doi.org/10.1145/2884781.2884797

    work page doi:10.1145/2884781.2884797 2016

  53. [53]

    Melo, Jeffrey C

    Silvana M. Melo, Jeffrey C. Carver, Paulo S.L. Souza, and Simone R.S. Souza. 2019. Empirical Research on Concurrent Software Testing: A Systematic Mapping Study. Information and Software Technology 105 (2019), 226–251

    work page 2019

  54. [54]

    Claudio Menghi, Paolo Arcaini, Walstan Baptista, Gidon Ernst, Georgios Fainekos, Federico Formica, Sauvik Gon, Tanmay Khandait, Atanu Kundu, Giulia Pedrielli, Jarkko Peltomäki, Ivan Porres, Rajarshi Ray, Masaki Waga, and Zhenya Zhang

    work page

  55. [55]

    In International Workshop on Applied Verification of Continuous and Hybrid Systems (ARCH) (EPiC Series in Computing, Vol

    ARCH-COMP23 Category Report: Falsification. In International Workshop on Applied Verification of Continuous and Hybrid Systems (ARCH) (EPiC Series in Computing, Vol. 96). EasyChair, 151–169. https://doi.org/10.29007/6nqs

    work page doi:10.29007/6nqs

  56. [56]

    Claudio Menghi, Shiva Nejati, Lionel Briand, and Yago Isasi Parache. 2020. Approximation-Refinement Testing of Compute-Intensive Cyber-Physical Mod- els: An Approach Based on System Identification. In International Conference on Software Engineering (ICSE) . ACM/IEEE, 372–384. https://doi.org/10.1145/ 3377811.3380370

    work page arXiv 2020

  57. [57]

    Vitaliy Mezhuyev, Mostafa Al-Emran, Mohd Arfian Ismail, Luigi Benedicenti, and Durkahpuvanesvari A. P. Chandran. 2019. The Acceptance of Search-Based Software Engineering Techniques: An Empirical Evaluation Using the Technology Acceptance Model. IEEE Access 7 (2019), 101073–101085. https://doi.org/10.1109/ ACCESS.2019.2917913

    work page arXiv 2019

  58. [58]

    Marcello Minervini, Paolo Giangrande, Fabio Corti, Paolo Malighetti, and Lorenzo Mantione. 2024. Regenerative Braking Capabilities in E-Bike Vehicles: Com- parison Between two Drive Architectures. In 2024 IEEE International Confer- ence on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehi- cles & International Transportation Electrif...

    work page doi:10.1109/esars-itec60450.2024.10819816 2024

  59. [59]

    Salvatore Musumeci, Fabio Mandrile, Vincenzo Barba, and Marco Palma. 2021. Low-Voltage GaN FETs in Motor Control Application; Issues and Advantages: A Review. Energies 14, 19 (Oct. 2021), 6378. https://doi.org/10.3390/en14196378

    work page doi:10.3390/en14196378 2021

  60. [60]

    Brembo N.V. 2024. Brembo Homepage. https://www.brembo.com/en/. Accessed: November 19, 2024

    work page 2024

  61. [61]

    Annibale Panichella, Fitsum Meshesha Kifetew, and Paolo Tonella. 2018. A large scale empirical comparison of state-of-the-art search-based test case generators. Information and Software Technology 104 (2018), 236–256

    work page 2018

  62. [62]

    Andriani Parastiwi, P. C. M. Al-Akbary, and Hari Kurnia Safitri. 2020. Analysis and Testing of DC Motor Control System for Electric Bike.Conference Series: Materials Science and Engineering 732, 1 (2020), 012055. https://doi.org/10.1088/1757- 899x/732/1/012055

    work page doi:10.1088/1757- 2020

  63. [63]

    Jarkko Peltomäki and Ivan Porres. 2022. Falsification of Multiple Requirements for Cyber-Physical Systems Using Online Generative Adversarial Networks and Multi-Armed Bandits. In International Conference on Software Testing, Verifica- tion and Validation Workshops (ICSTW) . IEEE, 21–28. https://doi.org/10.1109/ ICSTW55395.2022.00018

    work page arXiv 2022

  64. [64]

    Pirelli. 2024. Pirelli Homepage. http://www.pirelli.com. Accessed: November 19, 2024

    work page 2024

  65. [65]

    Abdel Salam Sayyad, Katerina Goseva-Popstojanova, Tim Menzies, and Hany Ammar. 2013. On Parameter Tuning in Search Based Software Engineering: A Replicated Empirical Study. In International Workshop on Replication in Empirical Software Engineering Research (RESER) . IEEE, 84–90. https://doi.org/10.1109/ RESER.2013.6

    work page 2013

  66. [66]

    Paul Schepers, Karin Klein Wolt, and Elliot Fishman. 2018. The safety of e-bikes in The Netherlands . International Transport Forum Discussion Paper 2018-02. Paris. https://doi.org/10.1787/21de1ffa-en

    work page doi:10.1787/21de1ffa-en 2018

  67. [67]

    Krems, and Tina Gehlert

    Katja Schleinitz, Tibor Petzoldt, Luise Franke-Bartholdt, Josef F. Krems, and Tina Gehlert. 2017. The German Naturalistic Cycling Study – Comparing cycling speed of riders of different e-bikes and conventional bicycles. Safety Science 92 (2017), 290–297. https://doi.org/10.1016/j.ssci.2015.07.027

    work page doi:10.1016/j.ssci.2015.07.027 2017

  68. [68]

    Martin Shepperd, Nemitari Ajienka, and Steve Counsell. 2018. The Role and Value of Replication in Empirical Software Engineering Results. Information and Software Technology 99 (2018), 120–132

    work page 2018

  69. [69]

    Sohil Lal Shrestha, Shafiul Azam Chowdhury, and Christoph Csallner. 2023. Replicability Study: Corpora For Understanding Simulink Models & Projects . In ACM/IEEE International Symposium on Empirical Software Engineering and Mea- surement (ESEM). IEEE, 1–12. https://doi.org/10.1109/ESEM56168.2023.10304867

    work page doi:10.1109/esem56168.2023.10304867 2023

  70. [70]

    Andrea Stocco, Brian Pulfer, and Paolo Tonella. 2023. Model vs System Level Testing of Autonomous Driving Systems: A Replication and Extension Study. Empirical Software Engineering 28, 3 (2023), 73

    work page 2023

  71. [71]

    Mathworks Customer Stories. 2024. Bosch eBike Systems Develops Electric Bike Controller with Model-Based Design. https://www.mathworks.com/ company/user_stories/bosch-ebike-systems-develops-electric-bike-controller- with-model-based-design.html. Accessed: November 7, 2024

    work page 2024

  72. [72]

    Quinn Thibeault, Jacob Anderson, Aniruddh Chandratre, Giulia Pedrielli, and Georgios Fainekos. 2021. PSY-TaLiRo: A Python Toolbox for Search-Based Test Generation for Cyber-Physical Systems. In Formal Methods for Industrial Critical Systems. Springer, 223–231. https://doi.org/10.1007/978-3-030-85248-1_15

    work page doi:10.1007/978-3-030-85248-1_15 2021

  73. [73]

    EE Times. 2024. Software Takes eBikes to New Heights. https://www.eetimes. eu/software-takes-ebikes-to-new-heights/. Accessed: November 7, 2024

    work page 2024

  74. [74]

    Masaki Waga. 2020. Falsification of Cyber-Physical Systems With Robustness- Guided Black-Box Checking. In International Conference on Hybrid Systems: Com- putation and Control (HSCC ’20). ACM, 1–13. https://doi.org/10.1145/3365365. 3382193

    work page doi:10.1145/3365365 2020

  75. [75]

    Yoriyuki Yamagata, Shuang Liu, Takumi Akazaki, Yihai Duan, and Jianye Hao

    work page

  76. [76]

    IEEE Transactions on Software Engineering 47, 12 (Dec

    Falsification of Cyber-Physical Systems Using Deep Reinforcement Learn- ing. IEEE Transactions on Software Engineering 47, 12 (Dec. 2021), 2823–2840. https://doi.org/10.1109/tse.2020.2969178

    work page doi:10.1109/tse.2020.2969178 2021

  77. [77]

    Mosterman

    Justyna Zander, Ina Schieferdecker, and Pieter J. Mosterman. 2017. Model-based testing for embedded systems . CRC press

    work page 2017

  78. [78]

    Zhenya Zhang, Deyun Lyu, Paolo Arcaini, Lei Ma, Ichiro Hasuo, and Jianjun Zhao. 2021. Effective Hybrid System Falsification Using Monte Carlo Tree Search Guided by QB-Robustness. In Computer Aided Verification (CA V). Springer Inter- national Publishing, Cham, 595–618

    work page 2021

  79. [79]

    Xi Zheng, Christine Julien, Miryung Kim, and Sarfraz Khurshid. 2015. Perceptions on the State of the Art in Verification and Validation in Cyber-Physical Systems. IEEE Systems Journal 11, 4 (2015), 2614–2627

    work page 2015