Size Metrics for Service-Oriented Architecture

Munialo, Samson Wanjala; Muketha, Geoffrey Muchiri; Omieno, Kelvin Kabeti

doi:10.5121/ijsea.2019.10206

Cited by 3 publications

(2 citation statements)

References 13 publications

(20 reference statements)

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“…The more extensive and granular the microservice, the more challenging it is to maintain due to the possibilities of multiple responsibilities to the microservice. Size metrics are crucial design attributes in software estimation before executing migration [72].…”

Section: Sizementioning

confidence: 99%

From Monolith to Microservice: Measuring Architecture Maintainability

Hasan¹,

Osman²,

Admodisastro³

et al. 2023

IJACSA

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The migration of monolithic applications to the cloud is a popular trend, with microservice architecture being a commonly targeted architectural pattern. The motivation behind this migration is often rooted in the challenges associated with maintaining legacy applications and the need to adapt to rapidly changing business requirements. To ensure that the migration to microservices is a sound decision for enhancing maintainability, designers must carefully consider the underlying factors driving this software architecture migration. This study proposes a set of software architecture metrics for evaluating the maintainability of microservice architectural designs for monolith to microservice architecture migration. These metrics consider various factors, such as coupling, complexity, cohesion, and size, which are crucial for ensuring that the software architecture remains maintainable in the long term. Drawing upon previous product quality models that share similar design properties with microservice, we have derived maintainability metrics that can help measure the quality of microservice architecture. In this work, we introduced our first version of structural metrics for measuring the maintainability quality of microservice architecture concerning its cloud-native characteristics. This work allows us to get early feedback on proposed metrics before a detailed evaluation. With these metrics, designers can measure their microservice architecture quality to fully leverage the benefits of the cloud environment, thus ensuring that the migration to microservice is a beneficial decision for enhancing the maintainability of their software architecture applications.

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Section: Sizementioning

confidence: 99%

From Monolith to Microservice: Measuring Architecture Maintainability

Hasan¹,

Osman²,

Admodisastro³

et al. 2023

IJACSA

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“…Software size has an impact on its complexity according to research conducted by [14]. [26] state that size metrics are a major factor in the determination of successiful software projects. Additionally, the study by [15] suggests size metrics have a significant impact on project effort, duration, and productivity.…”

Section: Introductionmentioning

confidence: 99%

A Metrics -Based Model for Estimating the Maintenance Effort of Python Software

Mukunga¹,

Ndia²,

Wambugu³

2023

IJSEA

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Software project management includes a substantial area for estimating software maintenance effort. Estimation of software maintenance effort improves the overall performance and efficiency of software. The Constructive Cost Model (COCOMO) and other effort estimation models are mentioned in literature but are inappropriate for Python programming language. This research aimed to modify the Constructive Cost Model (COCOMO II) by considering a range of Python maintenance effort influencing factors to get estimations and incorporated size and complexity metrics to estimate maintenance effort. A within-subjects experimental design was adopted and an experiment questionnaire was administered to forty subjects aiming to rate the maintainability of twenty Python programs. Data collected from the experiment questionnaire was analyzed using descriptive statistics. Metric values were collected using a developed metric tool. The subject ratings on software maintainability were correlated with the developed model’s maintenance effort, a strong correlation of 0.610 was reported meaning that the model is valid.

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