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Architectural Support for Software Performance in Continuous Software Engineering: A Systematic Mapping Study

Romina Eramo, Michele Tucci, Daniele Di Pompeo, Vittorio Cortellessa, Antinisca Di Marco, Davide Taibi

TL;DR

This systematic mapping investigates how architectural support for software performance is integrated into Continuous Software Engineering (CSE). By analyzing 66 primary studies from an initial 215 papers, the work reveals a mature focus on performance evaluation, modeling, and prediction, typically driven by runtime/monitored data and SA-driven models. It highlights strong momentum around continuous monitoring and framework/tool support, but identifies notable gaps in uncertainty handling, Agile/DevOps integration, and data-intensive/cloud contexts, suggesting a need for higher-level abstractions and DSLs to better integrate diverse methodologies. The findings offer a structured framework for researchers and practitioners to advance performance-aware CSE and guide future research toward underexplored domains and methodologies.

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

Architectural Support for Software Performance in Continuous Software Engineering: A Systematic Mapping Study

TL;DR

This systematic mapping investigates how architectural support for software performance is integrated into Continuous Software Engineering (CSE). By analyzing 66 primary studies from an initial 215 papers, the work reveals a mature focus on performance evaluation, modeling, and prediction, typically driven by runtime/monitored data and SA-driven models. It highlights strong momentum around continuous monitoring and framework/tool support, but identifies notable gaps in uncertainty handling, Agile/DevOps integration, and data-intensive/cloud contexts, suggesting a need for higher-level abstractions and DSLs to better integrate diverse methodologies. The findings offer a structured framework for researchers and practitioners to advance performance-aware CSE and guide future research toward underexplored domains and methodologies.

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
Paper Structure (50 sections, 23 figures, 11 tables)

This paper contains 50 sections, 23 figures, 11 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Main concepts
  4. Related works
  5. Research Method
  6. Defining research questions
  7. RQ1: What research areas and target systems have been investigated?
  8. RQ2: What and how performance problems have been addressed?
  9. RQ3: What instruments have been adopted?
  10. RQ4: What are the gaps in current research gaps and the implications for future research?
  11. Conducting search
  12. Papers selection
  13. Data Extraction and Analysis
  14. Results Overview
  15. What research areas and target systems have been investigated (RQ$_1$)
  16. ...and 35 more sections

Figures (23)

  • Figure 1: Overview of the considered context
  • Figure 2: Mapping process
  • Figure 3: Overview and numbers of search and selection process
  • Figure 4: Selected paper types.
  • Figure 5: Selected paper per venues.
  • ...and 18 more figures