article

Software Model Refactoring Driven by Performance Antipattern Detection

Authors:

Vittorio Cortellessa,

Daniele Di Pompeo,

Vincenzo Stoico,

Michele TucciAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 49, Issue 4

Pages 53 - 58

https://doi.org/10.1145/3543146.3543159

Published: 06 June 2022 Publication History

Abstract

The satisfaction of ever more stringent performance requirements is one of the main reasons for software evolution. However, determining the primary causes of performance degradation is generally challenging, as they may depend on the joint combination of multiple factors (e.g., workload, software deployment, hardware utilization). With the increasing complexity of software systems, classical bottleneck analysis seems to show limitations in capturing complex performance problems. Hence, in the last decade, the detection of performance antipatterns has gained momentum as an effective way to identify performance degradation causes. In this tool paper we introduce PADRE (Performance Antipattern Detection and REfactoring), a tool for: (i) detecting performance antipattern in UML models, and (ii) refactoring models with the aim of removing the detected antipatterns. PADRE has been implemented within Epsilon, which is an open-source platform for model-driven engineering, and it grounds on a methodology that allows performance antipattern detection and refactoring within the same implementation context.

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Cited By

Capuano RVaccaro F(2023)The Quality-Driven Refactoring Approach in BIM Italia2023 IEEE 20th International Conference on Software Architecture Companion (ICSA-C)10.1109/ICSA-C57050.2023.00021(22-31)Online publication date: Mar-2023
https://doi.org/10.1109/ICSA-C57050.2023.00021
Capuano RMuccini H(2023)A Graph-Based Java Projects Representation for Antipatterns DetectionSoftware Architecture10.1007/978-3-031-42592-9_17(250-265)Online publication date: 18-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-42592-9_17

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Software Model Refactoring Driven by Performance Antipattern Detection

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cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 49, Issue 4

March 2022

130 pages

ISSN:0163-5999

DOI:10.1145/3543146

Editor:
Zhenhua Liu
Stony Brook University

Issue’s Table of Contents

Copyright © 2022 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 06 June 2022

Published in SIGMETRICS Volume 49, Issue 4

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Cited By

Capuano RVaccaro F(2023)The Quality-Driven Refactoring Approach in BIM Italia2023 IEEE 20th International Conference on Software Architecture Companion (ICSA-C)10.1109/ICSA-C57050.2023.00021(22-31)Online publication date: Mar-2023
https://doi.org/10.1109/ICSA-C57050.2023.00021
Capuano RMuccini H(2023)A Graph-Based Java Projects Representation for Antipatterns DetectionSoftware Architecture10.1007/978-3-031-42592-9_17(250-265)Online publication date: 18-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-42592-9_17

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