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Detecting Variability in MATLAB/Simulink Models: An Industry-Inspired Technique and its Evaluation

Authors:

Alexander Schlie,

Sandro Schulze,

Ina SchaeferAuthors Info & Claims

SPLC '17: Proceedings of the 21st International Systems and Software Product Line Conference - Volume A

Pages 215 - 224

https://doi.org/10.1145/3106195.3106225

Published: 25 September 2017 Publication History

Abstract

Model-based languages such as MATLAB/Simulink play an essential role in the model-driven development of software systems. To comply with new requirements, it is common practice to create new variants by copying existing systems and modifying them. Commonly referred to as clone-and-own, severe problems arise in the long-run when no dedicated variability management is installed. To allow for a documented and structured reuse of systems, their variability information needs to be reverse-engineered. In this paper, we propose an advanced comparison procedure, the Matching Window Technique, and a customizable metric. Both allow us to overcome structural alterations commonly performed during clone-and-own. We analyze related MATLAB/Simulink models and determine, classify and represent their variability information in an understandable way. With our technique, we assist model engineers in maintaining and evolving existing variants. We provide three feasibility studies with real-world models from the automotive domain and show our technique to be fast and precise. Furthermore, we perform semi-structured interviews with domain experts to assess the potential applicability of our technique in practice.

References

[1]

S. Adolph, W. Hall, and P. Kruchten. 2011. Using Grounded Theory to Study the Experience of Software Development. Empirical Software Engineering 16 (2011), 487--513.

Digital Library

[2]

B. Al-Batran, B. Schätz, and B. Hummel. 2011. Semantic Clone Detection for Model-Based Development of Embedded Systems. In Proc. of the Intl. Conference on Model Driven Engineering Languages and Systems (MODELS) (LNCS), Vol. 6981. Springer, 258--272.

[3]

M. H. Alalfi, J. R. Cordy, and T. R. Dean. 2014. Analysis and Clustering of Model Clones: An Automotive Industrial Experience. In Proc. of the Intl. Conference on Software Maintenance, Reengineering and Reverse Engineering (CSMR-WCRE). IEEE, 375--378.

[4]

M. H. Alalfi, J. R. Cordy, T. R. Dean, M. Stephan, and A. Stevenson. 2012. Models are code too: Near-miss clone detection for Simulink models. In Proc. of the Intl. Conference on Software Maintenance (ICSM). IEEE, 295--304.

Digital Library

[5]

M. H. Alalfi, E.J. Rapos, A. Stevenson, M. Stephan, T. R. Dean, and J. R. Cordy. 2014. Semi-automatic Identification and Representation of Subsystem Variability in Simulink Models. In Proc. of the Intl. Conference on Software Maintenance and Evolution (ICSME). IEEE, 486--490.

Digital Library

[6]

K. Berg, J. Bishop, and D. Muthig. 2005. Tracing Software Product Line Variability: From Problem to Solution Space. In Proc. of the Annual Research Conf. of the South African Institute of Computer Scientists and Information Technologists on IT Research in Developing Countries (SAICSIT). South African Inst. for Computer Scientists and Information Technologists, 182--191.

[7]

R. Conradi and A.I. Wang. 2003. Empirical Methods and Studies in Software Engineering: Experiences from ESERNET. Springer.

[8]

K. Czarnecki and U. Eisenecker. 2000. Generative Programming: Methods, Tools, and Applications. Addison Wesley.

Digital Library

[9]

F. Deissenboeck, B. Hummel, E. Juergens, M. Pfaehler, and B. Schaetz. 2010. Model Clone Detection in Practice. In Proc. of the Intl. Workshop on Software Clones (IWSC). ACM, 57--64.

Digital Library

[10]

F. Deissenboeck, B. Hummel, E. Jürgens, B. Schätz, S. Wagner, J. Girard, and S. Teuchert. 2008. Clone Detection in Automotive Model-based Development. In Proc. of the Intl. Conference on Software Engineering (ICSE). ACM, 603--612.

Digital Library

[11]

Y. Dubinsky, J. Rubin, T. Berger, S. Duszynski, M. Becker, and K. Czarnecki. 2013. An Exploratory Study of Cloning in Industrial Software Product Lines. In Proc. of the European Conference on Software Maintenance and Reengineering (CSMR). IEEE, 25--34.

Digital Library

[12]

J. Font, M. Ballarín, Ø. Haugen, and C. Cetina. 2015. Automating the Variability Formalization of a Model Family by Means of Common Variability Language. In Proc. of the Intl. Software Product Line Conference (SPLC). ACM, 411--418.

Digital Library

[13]

B.G. Glaser and A.L. Strauss. 1967. The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine Publishing Company.

[14]

H. Grönninger, H. Krahn, C. Pinkernell, and B. Rumpe. 2014. Modeling Variants of Automotive Systems using Views. CoRR abs/1409.6629 (2014).

[15]

S. Holthusen, D. Wille, C. Legat, S. Beddig, I. Schaefer, and B. Vogel-Heuser. 2014. Family Model Mining for Function Block Diagrams in Automation Software. In Proc. of the Intl. Workshop on Reverse Variability Eng. (REVE). ACM, 36--43.

Digital Library

[16]

U. Kelter, J. Wehren, and J. Niere. 2005. A Generic Difference Algorithm for UML Models. Software Engineering 64, 105--116 (2005), 4--9.

[17]

V. I. Levenshtein. 1966. Binary Codes Capable of Correcting Deletions, Insertions, and Reversals. Soviet Physics Doklady 10, 8 (1966), 707--710.

[18]

Z. Liang, Y. Cheng, and J. Chen. 2014. A Novel Optimized Path-Based Algorithm for Model Clone Detection. Journal of Software 9, 7 (2014), 1810--1817.

[19]

R. Malhotra. 2016. Empirical Research in Software Engineering: Concepts, Analysis, and Applications. CRC Press.

[20]

J. Martinez, T. Ziadi, T. F. Bissyandé, J. Klein, and Y. le Traon. 2015. Automating the Extraction of Model-Based Software Product Lines from Model Variants (T). In Proc. of the Intl. Conference on Automated Software Engineering (ASE). IEEE, 396--406.

Digital Library

[21]

M.Q. Patton. 1987. How to Use Qalitative Methods in Evaluation. Sage Publications.

[22]

N. H. Pham, H. A. Nguyen, T. T. Nguyen, J. M. Al-Kofahi, and T. N. Nguyen. 2009. Complete and Accurate Clone Detection in Graph-based Models. In Proc. of the Intl. Conference on Software Engineering (ICSE). IEEE, 276--286.

Digital Library

[23]

K. Pohl, G. Böckle, and Linden, F. J. van der. 2005. Software Product Line Engineering: Foundations, Principles and Techniques. Springer.

[24]

C. Riva and C. D. Rosso. 2003. Experiences with Software Product Family Evolution. Proc. of the Intl. Workshop on Principles of Software Evol. (2003).

[25]

J. Rubin and M. Chechik. 2012. Combining Related Products into Product Lines. In Proc. of the Intl. Conference on Fundamental Approaches to Software Engineering (FASE) (LNCS), Vol. 7212. Springer, 285--300.

Digital Library

[26]

J. Rubin and M. Chechik. 2013. N-way Model Merging. In Proc. of the European Software Engineering Conference/Foundations of Software Engineering (ESEC/FSE). ACM, 301--311.

[27]

J. Rubin and M. Chechik. 2013. Quality of Merge-Refactorings for Product Lines. In Proc. of the Intl. Conference on Fundamental Approaches to Software Engineering (FASE) (LNCS), Vol. 7793. Springer, 83--98.

Digital Library

[28]

P. Runeson and M. Höst. 2009. Guidelines for Conducting and Reporting Case Study Research in Software Engineering. Empirical Software Engineering 14, 2 (2009), 131--164.

Digital Library

[29]

U. Ryssel, J. Ploennigs, and K. Kabitzsch. 2010. Automatic Variation-point Identification in Function-block-based Models. In Proc. of the Intl. Conference on Generative Programming and Component Engineering (GPCE). ACM, 23--32.

Digital Library

[30]

U. Ryssel, J. Ploennigs, and K. Kabitzsch. 2011. Extraction of Feature Models from Formal Contexts. In Proc. of the Intl. Software Product Line Conference (SPLC). ACM, 4:1--4:8.

Digital Library

[31]

U. Ryssel, J. Ploennigs, and K. Kabitzsch. 2012. Automatic library migration for the generation of hardware-in-the-loop models. Science of Computer Programming 77, 2 (2012), 83--95.

Digital Library

[32]

S. She, R. Lotufo, T. Berger, A. Wasowski, and K. Czarnecki. 2011. Reverse Engineering Feature Models. In Proc. of the Intl. Conference on Software Engineering (ICSE). IEEE, 461--470.

Digital Library

[33]

CQSE - Continuous Quality in Software Engineering. 2016. ConQAT. (Oct. 2016). https://www.conqat.org

[34]

Software Engineering Group, University of Siegen. 2016. The SiDiff project. (Oct. 2016). http://pi.informatik.uni-siegen.de/sidiff/

[35]

J. Weiland and P. Manhart. 2013. A Classification of Modeling Variability in Simulink. In Proc. of the Intl. Workshop on Variability Modeling in Software-intensive Systems (VaMoS). ACM, 7:1--7:8.

Digital Library

[36]

N. Weston, R. Chitchyan, and A. Rashid. 2009. A Framework for Constructing Semantically Composable Feature Models from Natural Language Requirements. In Proc. of the Intl. Software Product Line Conference (SPLC). ACM, 211--220.

[37]

D. Wille. 2014. Managing Lots of Models: The FaMine Approach. In Proc. of the Intl. Symposium on the Foundations of Software Engineering (FSE). ACM, 817--819.

Digital Library

[38]

D. Wille, S. Holthusen, S. Schulze, and I. Schaefer. 2013. Interface Variability in Family Model Mining. In Proc. of the Intl. Workshop on Model-Driven Approaches in Software Product Line Engineering (MAPLE). ACM, 44--51.

Digital Library

[39]

D. Wille, T. Runge, C. Seidl, and S. Schulze. 2017. Model-Based Delta Generation Using Extractive Software Product Line Engineering. In Proc. of the Intl. Workshop on Variability Modeling in Software-intensive Systems (VaMoS). ACM, 36--43.

Digital Library

[40]

X. Zhang, Ø. Haugen, and B. Møller-Pedersen. 2011. Model Comparison to Synthesize a Model-Driven Software Product Line. In Proc. of the Intl. Software Product Line Conference (SPLC). IEEE, 90--99.

Digital Library

Cited By

Boll AKehrer TGoedicke MCombemale BWimmer MChechik MEgyed A(2024)SMOKE: Simulink Model Obfuscator Keeping StructureProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3652620.3687788(41-45)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3652620.3687788
Gonçalves PCarvalheira P(2024)Cycle assessment of a four-stroke spark-ignition ethanol fueled engineSustainable Energy Technologies and Assessments10.1016/j.seta.2024.10372265(103722)Online publication date: May-2024
https://doi.org/10.1016/j.seta.2024.103722
Shrestha SBoll AChowdhury SKehrer TCsallner C(2023)EvoSL: A Large Open-Source Corpus of Changes in Simulink Models & Projects2023 ACM/IEEE 26th International Conference on Model Driven Engineering Languages and Systems (MODELS)10.1109/MODELS58315.2023.00024(273-284)Online publication date: 1-Oct-2023
https://doi.org/10.1109/MODELS58315.2023.00024
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Index Terms

Detecting Variability in MATLAB/Simulink Models: An Industry-Inspired Technique and its Evaluation
1. Social and professional topics
  1. Computing / technology policy
    1. Intellectual property
      1. Software reverse engineering
2. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Maintaining software
      2. Software evolution

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SPLC '17: Proceedings of the 21st International Systems and Software Product Line Conference - Volume A

September 2017

253 pages

ISBN:9781450352215

DOI:10.1145/3106195

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 September 2017

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SPLC '17

SPLC '17: 21st International Systems and Software Product Line Conference

September 25 - 29, 2017

Sevilla, Spain

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Cited By

Boll AKehrer TGoedicke MCombemale BWimmer MChechik MEgyed A(2024)SMOKE: Simulink Model Obfuscator Keeping StructureProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3652620.3687788(41-45)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3652620.3687788
Gonçalves PCarvalheira P(2024)Cycle assessment of a four-stroke spark-ignition ethanol fueled engineSustainable Energy Technologies and Assessments10.1016/j.seta.2024.10372265(103722)Online publication date: May-2024
https://doi.org/10.1016/j.seta.2024.103722
Shrestha SBoll AChowdhury SKehrer TCsallner C(2023)EvoSL: A Large Open-Source Corpus of Changes in Simulink Models & Projects2023 ACM/IEEE 26th International Conference on Model Driven Engineering Languages and Systems (MODELS)10.1109/MODELS58315.2023.00024(273-284)Online publication date: 1-Oct-2023
https://doi.org/10.1109/MODELS58315.2023.00024
Shrestha SChowdhury SCsallner C(2023)Replicability Study: Corpora For Understanding Simulink Models & Projects2023 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)10.1109/ESEM56168.2023.10304867(1-12)Online publication date: 26-Oct-2023
https://doi.org/10.1109/ESEM56168.2023.10304867
Jaskolka MPantelic VWassyng APaige RLawford M(2023)Repository mining for changes in Simulink and Stateflow modelsSoftware and Systems Modeling10.1007/s10270-023-01113-522:5(1713-1732)Online publication date: 16-Jun-2023
https://doi.org/10.1007/s10270-023-01113-5
Trombetta VFelfernig AFuentes L(2022)BEEHIVEProceedings of the 26th ACM International Systems and Software Product Line Conference - Volume B10.1145/3503229.3547064(17-22)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3503229.3547064
Guerra Ede Lara JChechik MSalay R(2022)Property Satisfiability Analysis for Product Lines of Modelling LanguagesIEEE Transactions on Software Engineering10.1109/TSE.2020.298950648:2(397-416)Online publication date: 1-Feb-2022
https://doi.org/10.1109/TSE.2020.2989506
Schultheis ABittner PThum TKehrer T(2022)Quantifying the Potential to Automate the Synchronization of Variants in Clone-and-Own2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME55016.2022.00032(269-280)Online publication date: Oct-2022
https://doi.org/10.1109/ICSME55016.2022.00032
Mahmood WStrüber DAnjorin ABerger T(2022)Effects of variability in models: a family of experimentsEmpirical Software Engineering10.1007/s10664-021-10112-327:3Online publication date: 17-Mar-2022
https://doi.org/10.1007/s10664-021-10112-3
Jaskolka MPantelic VWassyng ALawford MPaige R(2021)Repository Mining for Changes in Simulink Models2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)10.1109/MODELS50736.2021.00014(46-57)Online publication date: Oct-2021
https://doi.org/10.1109/MODELS50736.2021.00014
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