Interface protocol inference to aid understanding legacy software components

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Abstract

More and more high tech companies are struggling with the maintenance of legacy software. Legacy software is vital to many organizations, so even if its behavior is not completely understood it cannot be thrown away. To re-factor or re-engineer the legacy software components, the external behavior needs to be preserved after replacement so that the replaced components possess the same behavior in the system environment as the original components. Therefore, it is necessary to first completely understand the behavior of components over the interfaces, i.e., the interface protocols, and preserve this behavior during the software modification activities. For this purpose, we present an approach to infer the interface protocols of software components, from the behavioral models of those components learned with a blackbox technique, called active automata learning. We then perform a formal comparison between
learned models and reference models ensuring the behavioral relations are preserved. This provides a validation for the learned
results, thus developing confidence in applying the active learning
technique to reverse engineer the legacy software components in
the future.
Original languageEnglish
Title of host publicationProceedings of MODELS 2018 Workshops: ModComp, MRT, OCL, FlexMDE, EXE, COMMitMDE, MDETools, GEMOC, MORSE, MDE4IoT, MDEbug, MoDeVVa, ME, MULTI, HuFaMo, AMMoRe, PAINS co-located with ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS 2018)
EditorsRegina Hebig, Thorsten Berger
Number of pages6
Publication statusPublished - 14 Oct 2018
Event21st ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2018 - Copenhagen, Denmark
Duration: 14 Oct 201819 Oct 2018
http://ceur-ws.org/Vol-2245/

Publication series

NameCEUR Workshop Proceedings
ISSN (Print)1613-0073

Conference

Conference21st ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2018
CountryDenmark
CityCopenhagen
Period14/10/1819/10/18
Internet address

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Engineers
Industry

Keywords

  • Model checking
  • Models
  • automata learning

Cite this

Aslam, K., Luo, Y., Schiffelers, R. R. H., & van den Brand, M. G. J. (2018). Interface protocol inference to aid understanding legacy software components. In R. Hebig, & T. Berger (Eds.), Proceedings of MODELS 2018 Workshops: ModComp, MRT, OCL, FlexMDE, EXE, COMMitMDE, MDETools, GEMOC, MORSE, MDE4IoT, MDEbug, MoDeVVa, ME, MULTI, HuFaMo, AMMoRe, PAINS co-located with ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS 2018) (CEUR Workshop Proceedings).
Aslam, K. ; Luo, Y. ; Schiffelers, R.R.H. ; van den Brand, M.G.J. / Interface protocol inference to aid understanding legacy software components. Proceedings of MODELS 2018 Workshops: ModComp, MRT, OCL, FlexMDE, EXE, COMMitMDE, MDETools, GEMOC, MORSE, MDE4IoT, MDEbug, MoDeVVa, ME, MULTI, HuFaMo, AMMoRe, PAINS co-located with ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS 2018). editor / Regina Hebig ; Thorsten Berger. 2018. (CEUR Workshop Proceedings).
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title = "Interface protocol inference to aid understanding legacy software components",
abstract = "More and more high tech companies are struggling with the maintenance of legacy software. Legacy software is vital to many organizations, so even if its behavior is not completely understood it cannot be thrown away. To re-factor or re-engineer the legacy software components, the external behavior needs to be preserved after replacement so that the replaced components possess the same behavior in the system environment as the original components. Therefore, it is necessary to first completely understand the behavior of components over the interfaces, i.e., the interface protocols, and preserve this behavior during the software modification activities. For this purpose, we present an approach to infer the interface protocols of software components, from the behavioral models of those components learned with a blackbox technique, called active automata learning. We then perform a formal comparison betweenlearned models and reference models ensuring the behavioral relations are preserved. This provides a validation for the learned results, thus developing confidence in applying the active learningtechnique to reverse engineer the legacy software components inthe future.",
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author = "K. Aslam and Y. Luo and R.R.H. Schiffelers and {van den Brand}, M.G.J.",
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language = "English",
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booktitle = "Proceedings of MODELS 2018 Workshops: ModComp, MRT, OCL, FlexMDE, EXE, COMMitMDE, MDETools, GEMOC, MORSE, MDE4IoT, MDEbug, MoDeVVa, ME, MULTI, HuFaMo, AMMoRe, PAINS co-located with ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS 2018)",

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Aslam, K, Luo, Y, Schiffelers, RRH & van den Brand, MGJ 2018, Interface protocol inference to aid understanding legacy software components. in R Hebig & T Berger (eds), Proceedings of MODELS 2018 Workshops: ModComp, MRT, OCL, FlexMDE, EXE, COMMitMDE, MDETools, GEMOC, MORSE, MDE4IoT, MDEbug, MoDeVVa, ME, MULTI, HuFaMo, AMMoRe, PAINS co-located with ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS 2018). CEUR Workshop Proceedings, 21st ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2018, Copenhagen, Denmark, 14/10/18.

Interface protocol inference to aid understanding legacy software components. / Aslam, K.; Luo, Y.; Schiffelers, R.R.H.; van den Brand, M.G.J.

Proceedings of MODELS 2018 Workshops: ModComp, MRT, OCL, FlexMDE, EXE, COMMitMDE, MDETools, GEMOC, MORSE, MDE4IoT, MDEbug, MoDeVVa, ME, MULTI, HuFaMo, AMMoRe, PAINS co-located with ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS 2018). ed. / Regina Hebig; Thorsten Berger. 2018. (CEUR Workshop Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

TY - GEN

T1 - Interface protocol inference to aid understanding legacy software components

AU - Aslam, K.

AU - Luo, Y.

AU - Schiffelers, R.R.H.

AU - van den Brand, M.G.J.

PY - 2018/10/14

Y1 - 2018/10/14

N2 - More and more high tech companies are struggling with the maintenance of legacy software. Legacy software is vital to many organizations, so even if its behavior is not completely understood it cannot be thrown away. To re-factor or re-engineer the legacy software components, the external behavior needs to be preserved after replacement so that the replaced components possess the same behavior in the system environment as the original components. Therefore, it is necessary to first completely understand the behavior of components over the interfaces, i.e., the interface protocols, and preserve this behavior during the software modification activities. For this purpose, we present an approach to infer the interface protocols of software components, from the behavioral models of those components learned with a blackbox technique, called active automata learning. We then perform a formal comparison betweenlearned models and reference models ensuring the behavioral relations are preserved. This provides a validation for the learned results, thus developing confidence in applying the active learningtechnique to reverse engineer the legacy software components inthe future.

AB - More and more high tech companies are struggling with the maintenance of legacy software. Legacy software is vital to many organizations, so even if its behavior is not completely understood it cannot be thrown away. To re-factor or re-engineer the legacy software components, the external behavior needs to be preserved after replacement so that the replaced components possess the same behavior in the system environment as the original components. Therefore, it is necessary to first completely understand the behavior of components over the interfaces, i.e., the interface protocols, and preserve this behavior during the software modification activities. For this purpose, we present an approach to infer the interface protocols of software components, from the behavioral models of those components learned with a blackbox technique, called active automata learning. We then perform a formal comparison betweenlearned models and reference models ensuring the behavioral relations are preserved. This provides a validation for the learned results, thus developing confidence in applying the active learningtechnique to reverse engineer the legacy software components inthe future.

KW - Model checking

KW - Models

KW - automata learning

M3 - Conference contribution

T3 - CEUR Workshop Proceedings

BT - Proceedings of MODELS 2018 Workshops: ModComp, MRT, OCL, FlexMDE, EXE, COMMitMDE, MDETools, GEMOC, MORSE, MDE4IoT, MDEbug, MoDeVVa, ME, MULTI, HuFaMo, AMMoRe, PAINS co-located with ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS 2018)

A2 - Hebig, Regina

A2 - Berger, Thorsten

ER -

Aslam K, Luo Y, Schiffelers RRH, van den Brand MGJ. Interface protocol inference to aid understanding legacy software components. In Hebig R, Berger T, editors, Proceedings of MODELS 2018 Workshops: ModComp, MRT, OCL, FlexMDE, EXE, COMMitMDE, MDETools, GEMOC, MORSE, MDE4IoT, MDEbug, MoDeVVa, ME, MULTI, HuFaMo, AMMoRe, PAINS co-located with ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS 2018). 2018. (CEUR Workshop Proceedings).