TY - GEN
T1 - Interface protocol inference to aid understanding legacy software components
AU - Aslam, K.
AU - Luo, Y.
AU - Schiffelers, R.
AU - Van Den Brand, M.
PY - 2018
Y1 - 2018
N2 - © 2018 CEUR-WS. All rights reserved.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.
AB - © 2018 CEUR-WS. All rights reserved.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.
M3 - Conference contribution
VL - 2245
T3 - CEUR Workshop Proceedings
SP - 6
EP - 11
BT - MODELS-WS 2018 - 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 - Berger, T.
A2 - Hebig, R.
PB - CEUR-WS
T2 - 2018 MODELS Workshops: ModComp, MRT, OCL, FlexMDE, EXE, COMMitMDE, MDETools, GEMOC, MORSE, MDE4IoT, MDEbug, MoDeVVa, ME, MULTI, HuFaMo, AMMoRe, PAINS, MODELS-WS 2018
Y2 - 14 October 2018 through 19 October 2018
ER -