The continuous software engineering paradigm is gaining popularity in modern development practices, where the interleaving of design and runtime activities is induced by the continuous evolution of software systems. In this context, performance assessment is not easy, but recent studies have shown that architectural models evolving with the software can support this goal. In this paper, we present a mapping study aimed at classifying existing scientific contributions that deal with the architectural support for performance-targeted continuous software engineering. We have applied the systematic mapping methodology to an initial set of 215 potentially relevant papers and selected 66 primary studies that we have analyzed to characterize and classify the current state of research. This classification helps to focus on the main aspects that are being considered in this domain and, mostly, on the emerging findings and implications for future research. Editor's note: Open Science material was validated by the Journal of Systems and Software Open Science Board. (see [https://www.sciencedirect.com/science/article/pii/S0164121221002168] for an example for where to place the statement and how to format it).

Architectural support for software performance in continuous software engineering: A systematic mapping study

Eramo R.
;
Tucci M.;Di Pompeo D.;Cortellessa V.;Di Marco A.;
2024-01-01

Abstract

The continuous software engineering paradigm is gaining popularity in modern development practices, where the interleaving of design and runtime activities is induced by the continuous evolution of software systems. In this context, performance assessment is not easy, but recent studies have shown that architectural models evolving with the software can support this goal. In this paper, we present a mapping study aimed at classifying existing scientific contributions that deal with the architectural support for performance-targeted continuous software engineering. We have applied the systematic mapping methodology to an initial set of 215 potentially relevant papers and selected 66 primary studies that we have analyzed to characterize and classify the current state of research. This classification helps to focus on the main aspects that are being considered in this domain and, mostly, on the emerging findings and implications for future research. Editor's note: Open Science material was validated by the Journal of Systems and Software Open Science Board. (see [https://www.sciencedirect.com/science/article/pii/S0164121221002168] for an example for where to place the statement and how to format it).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/221763
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