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Learning to Reuse: Adaptive Model Learning for Evolving Systems

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Integrated Formal Methods (IFM 2019)

Abstract

Software systems undergo several changes along their life-cycle and hence, their models may become outdated. To tackle this issue, we propose an efficient algorithm for adaptive learning, called \(\mathtt {partial\text {-}Dynamic~L^*_M}\) (\(\mathtt {\partial L^*_M}\)) that improves upon the state of the art by exploring observation tables on-the-fly to discard redundant prefixes and deprecated suffixes. Using 18 versions of the OpenSSL toolkit, we compare our proposed algorithm along with three adaptive algorithms. For the existing algorithms in the literature, our experiments indicate a strong positive correlation between number of membership queries and temporal distance between versions and; for our algorithm, we found a weak positive correlation between membership queries and temporal distance, as well, a significantly lower number of membership queries. These findings indicate that, compared to the state-of-the-art algorithms, our \(\mathtt {\partial L^*_M}\) algorithm is less sensitive to software evolution and more efficient than the current approaches for adaptive learning.

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Acknowledgment

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, and University of Leicester, College of Science & Engineering. Research carried out using the computational resources of the Center for Mathematical Sciences Applied to Industry (CeMEAI) funded by FAPESP (grant 2013/07375-0). The authors are also grateful to the anonymous reviewers; the VALidation and Verification (VALVE) research group at the University of Leicester; and Laboratory of Software Engineering (LabES) at the University of Sao Paulo (ICMC-USP) for their insightful comments and suggestions.

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Correspondence to Carlos Diego N. Damasceno , Mohammad Reza Mousavi or Adenilso da Silva Simao .

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Damasceno, C.D.N., Mousavi, M.R., da Silva Simao, A. (2019). Learning to Reuse: Adaptive Model Learning for Evolving Systems. In: Ahrendt, W., Tapia Tarifa, S. (eds) Integrated Formal Methods. IFM 2019. Lecture Notes in Computer Science(), vol 11918. Springer, Cham. https://doi.org/10.1007/978-3-030-34968-4_8

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  • DOI: https://doi.org/10.1007/978-3-030-34968-4_8

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