research-article

An extensible data-driven approach for evaluating the quality of microservice architectures

Authors:

Mario Cardarelli,

Ludovico Iovino,

Paolo Di Francesco,

Amleto Di Salle,

Ivano Malavolta,

Patricia LagoAuthors Info & Claims

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

Pages 1225 - 1234

https://doi.org/10.1145/3297280.3297400

Published: 08 April 2019 Publication History

Abstract

Microservice architecture (MSA) is defined as an architectural style where the software system is developed as a suite of small services, each running in its own process and communicating with lightweight mechanisms. The benefits of MSA are many, ranging from an increase in development productivity, to better business-IT alignment, agility, scalability, and technology flexibility. The high degree of microservices distribution and decoupling is, however, imposing a number of relevant challenges from an architectural perspective. In this context, measuring, controlling, and keeping a satisfactory level of quality of the system architecture is of paramount importance.

In this paper we propose an approach for the specification, aggregation, and evaluation of software quality attributes for the architecture of microservice-based systems. The proposed approach allows developers to (i) produce architecture models of the system, either manually or automatically via recovering techniques, (ii) contribute to an ecosystem of well-specified and automatically-computable software quality attributes for MSAs, and (iii) continuously measure and evaluate the architecture of their systems by (re-)using the software quality attributes defined in the ecosystem. The approach is implemented by using Model-Driven Engineering techniques.

The current implementation of the approach has been validated by assessing the maintainability of a third-party, publicly available benchmark system.

References

[1]

Nuha Alshuqayran, Nour Ali, and Roger Evans. 2016. A Systematic Mapping Study in Microservice Architecture. In Service-Oriented Computing and Applications (SOCA), 2016 IEEE 9th International Conference on. IEEE, 44--51.

[2]

Nuha Alshuqayran, Nour Ali, and Roger Evans. 2018. Towards Micro Service Architecture Recovery: An Empirical Study. In 2018 IEEE International Conference on Software Architecture (ICSA). IEEE.

[3]

Francesco Basciani, Juri Di Rocco, Davide Di Ruscio, Ludovico Iovino, and Alfonso Pierantonio. 2016. A Customizable Approach for the Automated Quality Assessment of Modelling Artifacts. In 10th International Conference on the Quality of Information and Communications Technology, QUATIC 2016, Lisbon, Portugal, September 6--9, 2016. 88--93.

[4]

Justus Bogner, Stefan Wagner, and Alfred Zimmermann. 2017. Automatically measuring the maintainability of service-and microservice-based systems: a literature review. In Proceedings of the 27th International Workshop on Software Measurement and 12th International Conference on Software Process and Product Measurement. ACM, 107--115.

Digital Library

[5]

Justus Bogner, Stefan Wagner, and Alfred Zimmermann. 2017. Towards a practical maintainability quality model for service-and microservice-based systems. In Proceedings of the 11th European Conference on Software Architecture: Companion Proceedings. ACM, 195--198.

Digital Library

[6]

Shahir Daya, Nguyen Van Duy, Kameswara Eati, Carlos M Ferreira, Dejan Glozic, Vasfi Gucer, Manav Gupta, Sunil Joshi, Valerie Lampkin, Marcelo Martins, et al. 2016. Microservices from Theory to Practice: Creating Applications in IBM Bluemix Using the Microservices Approach. IBM Redbooks.

[7]

Florian Deissenboeck, Lars Heinemann, Markus Herrmannsdoerfer, Klaus Lochmann, and Stefan Wagner. 2011. The quamoco tool chain for quality modeling and assessment. In Software Engineering (ICSE), 2011 33rd International Conference on. IEEE, 1007--1009.

Digital Library

[8]

Paolo Di Francesco, Patricia Lago, and Ivano Malavolta. 2017. Research on Architecting Microservices: trends, Focus, and Potential for Industrial Adoption. IEEE International Conference on Software Architecture (ICSA) (2017).

[9]

Davide Di Ruscio. 2007. Specification of model transformation and weaving in model driven engineering. Ph.D. Dissertation. PhD thesis, Universita degli Studi dell'Aquila (February 2007) http://www.di.univaq.it/diruscio/phdThesis.php.

[10]

International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC). 2001. Software Engineering-Product Quality: Quality model. Vol. 1. ISO/IEC.

[11]

Martin Fowler and J Lewis. Last accessed: Mar 2017. Microservices a definition of this new architectural term. URL: http://martinfowler.com/articles/microservices.html (Last accessed: Mar 2017).

[12]

Paolo Di Francesco, Patricia Lago, and Ivano Malavolta. 2018. Migrating Towards Microservice Architectures: An Industrial Survey. In IEEE International Conference on Software Architecture, ICSA 2018, Seattle, USA, April 30 - May 4, 2018. 29--39.

[13]

Andreas Goeb and Klaus Lochmann. 2011. A software quality model for SOA. In Proceedings of the 8th international workshop on Software quality. ACM, 18--25.

Digital Library

[14]

Giona Granchelli, Mario Cardarelli, Paolo Di Francesco, Ivano Malavolta, Ludovico Iovino, and Amleto Di Salle. 2017. MicroART: A Software Architecture Recovery Tool for Maintaining Microservice-based Systems. IEEE International Conference on Software Architecture (ICSA) (2017).

[15]

Giona Granchelli, Mario Cardarelli, Paolo Di Francesco, Ivano Malavolta, Ludovico Iovino, and Amleto Di Salle. 2017. Towards Recovering the Software Architecture of Microservice-based Systems. First International Workshop on Architecting with MicroServices (AMS) (2017).

[16]

Martin Hitz and Behzad Montazeri. 1995. Measuring coupling and cohesion in object-oriented systems. (1995).

[17]

Usha Kumari and Shuchita Upadhyaya. 2011. An interface complexity measure for component-based software systems. International Journal of Computer Applications 36, 1 (2011), 46--52.

[18]

Aminah Nuraini and Yani Widyani. 2014. Software with service oriented architecture quality assessment. In Data and Software Engineering (ICODSE), 2014 International Conference on. IEEE, 1--6.

[19]

Edward E Ogheneovo. 2014. On the relationship between software complexity and maintenance costs. Journal of Computer and Communications 2, 14 (2014), 1.

[20]

Paul Oman and Jack Hagemeister. 1992. Metrics for assessing a software system's maintainability. In Software Maintenance, 1992. Proceerdings., Conference on. IEEE, 337--344.

[21]

Claus Pahl and Pooyan Jamshidi. 2016. Microservices: A systematic mapping study. In Proceedings of the 6th International Conference on Cloud Computing and Services Science. 137--146.

Digital Library

[22]

Mikhail Perepletchikov, Caspar Ryan, Keith Frampton, and Zahir Tari. 2007. Coupling metrics for predicting maintainability in service-oriented designs. In Software Engineering Conference, 2007. ASWEC 2007. 18th Australian. IEEE, 329--340.

Digital Library

[23]

Reinhold Plösch, Harald Gruber, A Hentschel, Ch Körner, Gustav Pomberger, Stefan Schiffer, Matthias Saft, and S Storck. 2008. The EMISQ method and its tool support-expert-based evaluation of internal software quality. Innovations in Systems and Software Engineering 4, 1 (2008), 3--15.

[24]

Th Reiter, E Kapsammer, W Retschitzegger, and W Schwinger. 2005. Model integration through mega operations. In Workshop on Model-driven Web Engineering. 20.

[25]

Steven D Sheetz, David P Tegarden, and David E Monarchi. 1991. Measuring object-oriented system complexity. In Proc. 1st Workshop on information Technologies and Systems.

[26]

Renuka Sindhgatta, Bikram Sengupta, and Karthikeyan Ponnalagu. 2009. Measuring the quality of service oriented design. In Service-Oriented Computing. Springer, 485--499.

Digital Library

[27]

I Standard. 1999. Information technology-software product evaluation-part 1: General overview. ISO Standard (1999), 14598--1.

[28]

Christoph Stormer. 2007. Software Quality Attribute Analysis by Architecture Reconstruction (SQUA3RE). In Software Maintenance and Reengineering, 2007. CSMR'07. 11th European Conference on. IEEE, 361--364.

Digital Library

[29]

Anne Thomas and Aashish Gupta. 2017. Innovation insights for Microservices. https://www.gartner.com/doc/3579057/innovation-insight-microservices (2017).

[30]

Stefan Wagner, Andreas Goeb, Lars Heinemann, Michael Kläs, Constanza Lampasona, Klaus Lochmann, Alois Mayr, Reinhold Plösch, Andreas Seidl, Jonathan Streit, et al. 2015. Operationalised product quality models and assessment: The Quamoco approach. Information and Software Technology 62 (2015), 101--123.

Digital Library

[31]

Stefan Wagner, Klaus Lochmann, Lars Heinemann, Michael Kläs, Adam Trendowicz, Reinhold Plösch, Andreas Seidl, Andreas Goeb, and Jonathan Streit. 2012. The quamoco product quality modelling and assessment approach. In Proceedings of the 34th international conference on software engineering. IEEE Press, 1133--1142.

Digital Library

[32]

Jos B Warmer and Anneke G Kleppe. 1998. The object constraint language: Precise modeling with uml (addison-wesley object technology series). (1998).

Digital Library

Cited By

Li ZSaldías-Vallejos NSeco DRodríguez MRanjan R(2024)Long Live the Image: On Enabling Resilient Production Database Containers for Microservice ApplicationsIEEE Transactions on Software Engineering10.1109/TSE.2024.343662350:9(2363-2378)Online publication date: Sep-2024
https://doi.org/10.1109/TSE.2024.3436623
Bajaj DBharti UGupta IGupta PYadav A(2024)GTMicro—microservice identification approach based on deep NLP transformer model for greenfield developmentsInternational Journal of Information Technology10.1007/s41870-024-01766-516:5(2751-2761)Online publication date: 8-Mar-2024
https://doi.org/10.1007/s41870-024-01766-5
Driessen FFerreira Pires LMoreira JVerhoeven Pvan den Bosch S(2024)A Quantitative Assessment Method for Microservices Granularity to Improve MaintainabilityEnterprise Design, Operations, and Computing. EDOC 2023 Workshops10.1007/978-3-031-54712-6_13(211-226)Online publication date: 2-Mar-2024
https://doi.org/10.1007/978-3-031-54712-6_13
Show More Cited By

Index Terms

An extensible data-driven approach for evaluating the quality of microservice architectures
1. Applied computing
  1. Enterprise computing
    1. Service-oriented architectures
2. Software and its engineering
  1. Software notations and tools
    1. Context specific languages
      1. Domain specific languages
    2. Software maintenance tools

Recommendations

The applicability of palladio for assessing the quality of cloud-based microservice architectures
ECSA '19: Proceedings of the 13th European Conference on Software Architecture - Volume 2

When adopting microservices, software architects have to make several design decisions which impact the quality of the application in terms of scalability, elasticity and cost-efficiency. A prominent model-driven architectural simulator that aids ...
Tool Support for the Migration to Microservice Architecture: An Industrial Case Study
Software Architecture
Abstract
With the introduction of microservice architecture, many investigate how to migrate their legacy systems into this architectural paradigm. The migration process requires the recovery of the project architecture to be migrated together with the ...
Combined architectural framework for the selection of architectures using ATAM, FAHP and CBAM

Software architecture is the framework for all the technical decisions and as such has a tremendous impact on product cost and quality. It is always cost-effective to evaluate software quality as early as possible in the system development life cycle. ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

April 2019

2682 pages

ISBN:9781450359337

DOI:10.1145/3297280

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University, Marietta, Georgia
,
George A. Papadopoulos
University of Cyprus, Nicosia, Cyprus

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 April 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SAC '19

Sponsor:

SIGAPP

SAC '19: The 34th ACM/SIGAPP Symposium on Applied Computing

April 8 - 12, 2019

Limassol, Cyprus

Acceptance Rates

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
447
Total Downloads

Downloads (Last 12 months)72
Downloads (Last 6 weeks)5

Reflects downloads up to 15 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Li ZSaldías-Vallejos NSeco DRodríguez MRanjan R(2024)Long Live the Image: On Enabling Resilient Production Database Containers for Microservice ApplicationsIEEE Transactions on Software Engineering10.1109/TSE.2024.343662350:9(2363-2378)Online publication date: Sep-2024
https://doi.org/10.1109/TSE.2024.3436623
Bajaj DBharti UGupta IGupta PYadav A(2024)GTMicro—microservice identification approach based on deep NLP transformer model for greenfield developmentsInternational Journal of Information Technology10.1007/s41870-024-01766-516:5(2751-2761)Online publication date: 8-Mar-2024
https://doi.org/10.1007/s41870-024-01766-5
Driessen FFerreira Pires LMoreira JVerhoeven Pvan den Bosch S(2024)A Quantitative Assessment Method for Microservices Granularity to Improve MaintainabilityEnterprise Design, Operations, and Computing. EDOC 2023 Workshops10.1007/978-3-031-54712-6_13(211-226)Online publication date: 2-Mar-2024
https://doi.org/10.1007/978-3-031-54712-6_13
Salii SAjdari JZenuni X(2023)Migrating to a microservice architecture: benefits and challenges2023 46th MIPRO ICT and Electronics Convention (MIPRO)10.23919/MIPRO57284.2023.10159894(1670-1677)Online publication date: 22-May-2023
https://doi.org/10.23919/MIPRO57284.2023.10159894
Tapia VGaona C(2023)Research Opportunities in Microservices Quality Assessment: A Systematic Literature ReviewJournal of Advances in Information Technology10.12720/jait.14.5.991-100214:5(991-1002)Online publication date: 2023
https://doi.org/10.12720/jait.14.5.991-1002
Silva STuyishime ASantilli TPelliccione PIovino L(2023)Quality Metrics in Software Architecture2023 IEEE 20th International Conference on Software Architecture (ICSA)10.1109/ICSA56044.2023.00014(58-69)Online publication date: Mar-2023
https://doi.org/10.1109/ICSA56044.2023.00014
Rosa TGuerra ECorreia FGoldman A(2023)CharM — Evaluating a model for characterizing service-based architecturesJournal of Systems and Software10.1016/j.jss.2023.111826206:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111826
Milić MMakajić-Nikolić D(2022)Development of a Quality-Based Model for Software Architecture Optimization: A Case Study of Monolith and Microservice ArchitecturesSymmetry10.3390/sym1409182414:9(1824)Online publication date: 2-Sep-2022
https://doi.org/10.3390/sym14091824
Peng XZhang CZhao ZIsami AGuo XCui YRoychoudhury ACadar CKim M(2022)Trace analysis based microservice architecture measurementProceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3540250.3558951(1589-1599)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3540250.3558951
Bacchiega PPigazzini IFontana F(2022)Microservices smell detection through dynamic analysis2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)10.1109/SEAA56994.2022.00052(290-293)Online publication date: Aug-2022
https://doi.org/10.1109/SEAA56994.2022.00052
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents