Einblick - Microservices Architecture - # Microservice Feature Dataset

Comprehensive Feature Dataset for Analyzing Microservices-based Systems

Q: How can the dataset be extended to include microservice systems developed using other frameworks or technologies beyond Spring Cloud?

To extend the dataset to include microservice systems developed using other frameworks or technologies beyond Spring Cloud, a systematic approach can be adopted. Firstly, a comprehensive review of popular microservice frameworks and technologies should be conducted to identify key players in the industry. This review should encompass frameworks like Kubernetes, Docker, Apache ServiceComb, and technologies like REST, gRPC, and GraphQL. Once the frameworks and technologies are identified, a new set of search criteria can be established to target repositories that utilize these frameworks and technologies. Specific keywords related to these frameworks and technologies can be used in the search process to filter out relevant microservice systems. For example, keywords like "Kubernetes microservices" or "RESTful microservices" can be employed in the search queries. After identifying potential microservice systems developed using different frameworks and technologies, a similar extraction program can be applied to collect feature data from these systems. The extraction metrics may need to be adjusted or expanded to accommodate the specific characteristics and structures of microservices developed using different frameworks.

Q: What are the potential limitations or biases in the dataset due to the selection criteria and the diversity of the collected microservice systems?

The dataset may have limitations and biases due to the selection criteria and the diversity of the collected microservice systems. Some potential limitations and biases include: Selection Bias: The dataset may be biased towards microservice systems developed using Spring Cloud due to the specific search criteria employed. This bias may result in an underrepresentation of microservices developed using other frameworks or technologies. Scale Bias: The dataset may contain a mix of small-scale and large-scale microservice systems, leading to variations in the distribution of metrics. Large-scale systems may introduce outliers that skew the overall dataset distribution. Technology Bias: The dataset may be biased towards certain technologies or architectural styles prevalent in the Spring Cloud ecosystem. This bias could impact the generalizability of findings to microservices developed using different technologies. Quality Bias: The quality of the extracted data may vary across different microservice systems, leading to inconsistencies in the dataset. Errors in the extraction process or manual verification could introduce biases in the dataset. Domain Bias: The selected microservice systems may belong to specific domains or industries, potentially limiting the applicability of the dataset to a broader range of use cases.

Q: How can the dataset be leveraged to develop novel techniques for the automated detection and remediation of microservice bad smells, beyond the application of machine learning and heuristic algorithms?

The dataset can be leveraged to develop novel techniques for the automated detection and remediation of microservice bad smells through the following approaches: Natural Language Processing (NLP): Utilize NLP techniques to analyze code comments, commit messages, and documentation in the microservice systems to identify patterns related to bad smells. NLP can help in understanding the context and intent behind code changes, aiding in the detection of bad smells. Graph Analysis: Represent the microservice systems as graphs to analyze dependencies, interactions, and structural patterns. Graph algorithms can be applied to detect anomalies, cyclic dependencies, or anti-patterns within the microservices architecture. Continuous Integration/Continuous Deployment (CI/CD) Integration: Integrate the dataset with CI/CD pipelines to perform automated checks for bad smells during the build and deployment processes. This real-time feedback loop can help in identifying and addressing bad smells early in the development lifecycle. Predictive Analytics: Use historical data from the dataset to predict potential bad smells in new microservice systems. Machine learning models can be trained on the dataset to forecast the likelihood of specific bad smells based on the system's characteristics and metrics. Automated Refactoring Tools: Develop automated refactoring tools that leverage the dataset to suggest and implement refactoring strategies for addressing identified bad smells. These tools can provide actionable insights and recommendations for improving the overall quality of microservice systems.

Kernkonzepte

This paper presents the construction of a comprehensive feature dataset for open-source microservices-based systems developed using the Spring Cloud framework. The dataset captures various metrics related to the architecture, design, and interactions of individual microservices, enabling in-depth analysis and detection of microservice bad smells.

Zusammenfassung

The paper addresses the lack of an appropriate open-source microservice feature dataset by taking the following steps:

Curation of a catalog of 55 open-source microservice systems developed using the Spring Cloud framework, selected based on specific criteria to ensure a certain level of maturity and diversity.
Establishment of 23 metrics to capture the fundamental aspects of individual microservices, including their presentation layer, business logic layer, data access layer, and model objects. These metrics enable the evaluation of microservice granularity, design, and interaction relationships.
Development of an extraction program to parse the collected microservice systems and extract the necessary feature data, followed by manual verification to ensure the accuracy of the extracted data.
Creation of a comprehensive feature dataset in the form of a CSV file, which is made publicly available. This dataset can serve as a valuable resource for the application of machine learning algorithms and further research in the domain of microservice bad smell detection.

The paper also discusses the limitations of the dataset, such as the potential exclusion of exceptional open-source microservice systems and the challenges in achieving 100% accurate automatic extraction due to the diverse technologies and development standards employed in microservice systems.

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Statistiken

The effective lines of source code in all .java files of the microservice is 338.
The number of entity classes used for persistent storage in the microservice is 100.
The number of attributes contained in the microservice entity classes is 100.
The number of controllers in the microservice is 100.
The number of interfaces in the microservice is 100.
The number of abstract classes in the microservice is 100.
The number of service implementation classes in the microservice is 100.
The number of Data Transfer Object classes in the microservice is 100.
The number of APIs exposed by the microservice is 100.
The maximum value of the parameter list size of all APIs exposed is 100.

Zitate

"The availability of such datasets may contribute to the detection of microservice bad smells unexpectedly."
"To address the current gap, this paper analyzes the architecture and interactions of microservices based on Spring in various Spring Cloud style microservice systems, establishes the relevant metrics of various fundamental elements of microservices in Spring Boot style, which are based on the three-tier architecture, collects open-source microservice systems, implements the extraction program, and constructs a dataset containing microservice feature data, which in turn paves the way for exploring poor practices within and between different microservices through machine learning, heuristic algorithms, and other means."

Wichtige Erkenntnisse aus

A Feature Dataset of Microservices-based Systems

by Weipan Yang,... um arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01789.pdf

A Feature Dataset of Microservices-based Systems

Tiefere Fragen

How can the dataset be extended to include microservice systems developed using other frameworks or technologies beyond Spring Cloud?

To extend the dataset to include microservice systems developed using other frameworks or technologies beyond Spring Cloud, a systematic approach can be adopted. Firstly, a comprehensive review of popular microservice frameworks and technologies should be conducted to identify key players in the industry. This review should encompass frameworks like Kubernetes, Docker, Apache ServiceComb, and technologies like REST, gRPC, and GraphQL.
Once the frameworks and technologies are identified, a new set of search criteria can be established to target repositories that utilize these frameworks and technologies. Specific keywords related to these frameworks and technologies can be used in the search process to filter out relevant microservice systems. For example, keywords like "Kubernetes microservices" or "RESTful microservices" can be employed in the search queries.
After identifying potential microservice systems developed using different frameworks and technologies, a similar extraction program can be applied to collect feature data from these systems. The extraction metrics may need to be adjusted or expanded to accommodate the specific characteristics and structures of microservices developed using different frameworks.

What are the potential limitations or biases in the dataset due to the selection criteria and the diversity of the collected microservice systems?

The dataset may have limitations and biases due to the selection criteria and the diversity of the collected microservice systems. Some potential limitations and biases include:

Selection Bias: The dataset may be biased towards microservice systems developed using Spring Cloud due to the specific search criteria employed. This bias may result in an underrepresentation of microservices developed using other frameworks or technologies.

Scale Bias: The dataset may contain a mix of small-scale and large-scale microservice systems, leading to variations in the distribution of metrics. Large-scale systems may introduce outliers that skew the overall dataset distribution.

Technology Bias: The dataset may be biased towards certain technologies or architectural styles prevalent in the Spring Cloud ecosystem. This bias could impact the generalizability of findings to microservices developed using different technologies.

Quality Bias: The quality of the extracted data may vary across different microservice systems, leading to inconsistencies in the dataset. Errors in the extraction process or manual verification could introduce biases in the dataset.

Domain Bias: The selected microservice systems may belong to specific domains or industries, potentially limiting the applicability of the dataset to a broader range of use cases.

How can the dataset be leveraged to develop novel techniques for the automated detection and remediation of microservice bad smells, beyond the application of machine learning and heuristic algorithms?

The dataset can be leveraged to develop novel techniques for the automated detection and remediation of microservice bad smells through the following approaches:

Natural Language Processing (NLP): Utilize NLP techniques to analyze code comments, commit messages, and documentation in the microservice systems to identify patterns related to bad smells. NLP can help in understanding the context and intent behind code changes, aiding in the detection of bad smells.

Graph Analysis: Represent the microservice systems as graphs to analyze dependencies, interactions, and structural patterns. Graph algorithms can be applied to detect anomalies, cyclic dependencies, or anti-patterns within the microservices architecture.

Continuous Integration/Continuous Deployment (CI/CD) Integration: Integrate the dataset with CI/CD pipelines to perform automated checks for bad smells during the build and deployment processes. This real-time feedback loop can help in identifying and addressing bad smells early in the development lifecycle.

Predictive Analytics: Use historical data from the dataset to predict potential bad smells in new microservice systems. Machine learning models can be trained on the dataset to forecast the likelihood of specific bad smells based on the system's characteristics and metrics.

Automated Refactoring Tools: Develop automated refactoring tools that leverage the dataset to suggest and implement refactoring strategies for addressing identified bad smells. These tools can provide actionable insights and recommendations for improving the overall quality of microservice systems.