Comparison of Static Analysis Architecture Recovery Tools for Microservice Applications

Core Concepts

The authors aim to identify and compare static analysis architecture recovery tools for microservice applications by executing them on a common dataset. The study fills a gap in the literature by providing a comprehensive comparison based on effectiveness in architecture recovery.

Abstract

The content discusses the importance of architecture recovery tools for microservice applications due to their distributed nature. It highlights the challenges faced by developers in overseeing architectural designs and introduces the need for static analysis tools. The study aims to identify existing tools, execute them on a common dataset, and compare their effectiveness in architecture recovery. By focusing on precision, recall, and execution time, the research intends to provide valuable insights for researchers and practitioners in the field of software engineering.

Stats

"Various tools and techniques for this task are presented in academic and grey literature sources."
"We will focus on static approaches because they are also suitable for integration into fast-paced CI/CD pipelines."
"The results we expect to obtain from our study can be beneficial to both researchers and practitioners."

Quotes

"We plan to first identify static analysis architecture recovery tools for microservice applications via a multi-vocal literature review."
"With the growing adoption of the microservice architecture, the need for static analysis tools that specialize in microservice applications rises."
"The comparison shall be based on the tools’ observed effectiveness in architecture recovery on a shared dataset."

Key Insights Distilled From

Comparison of Static Analysis Architecture Recovery Tools for Microservice Applications

by Simon Schnei... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06941.pdf

Deeper Inquiries

How can industry adoption of these tools be facilitated?

Industry adoption of architecture recovery tools for microservice applications can be facilitated through several strategies:

Integration with CI/CD Pipelines: Ensure that the tools are compatible with common Continuous Integration/Continuous Deployment (CI/CD) pipelines used in industry. This integration allows for automated analysis during the development process, making it easier for developers to incorporate architectural insights into their workflow.

User-Friendly Interfaces: Develop user-friendly interfaces and documentation for the tools to make them accessible to a wider audience within organizations. Clear instructions on how to use the tools effectively will encourage adoption by developers and architects.

Training and Support: Provide training sessions or workshops to educate teams on how to utilize these tools effectively. Offering ongoing support and troubleshooting assistance can also boost confidence in using the tools.

Case Studies and Success Stories: Share case studies and success stories from other organizations that have successfully implemented these tools. Real-world examples demonstrate the value of using such tools in improving software quality and maintainability.

Customization Options: Allow customization options within the tool so that organizations can tailor the analysis based on their specific requirements or architectural patterns.

Collaboration with Industry Partners: Collaborate with industry partners to understand their pain points better and tailor the tool features according to real-world needs, increasing relevance and usability.

How characteristics not present in the initial dataset be effectively addressed during tool evaluation?

When evaluating architecture recovery tools where characteristics not present in the initial dataset need consideration, here's how they could be effectively addressed:

Manual Extension of Dataset:

Manually extend or annotate additional information into your existing dataset based on what needs evaluation.
Define clear guidelines or indicators within code artifacts that signify certain characteristics for consistent extraction across different evaluators.

Cross-Validation:

Have multiple evaluators independently extract additional characteristics from source code or documentation.
Resolve discrepancies through discussion among evaluators or involve a third party if needed.

Feasibility Study:

Conduct a feasibility study similar to what was done initially before proceeding with full-scale evaluations.

4.. Collaboration:
Involve domain experts who understand both software architecture principles as well as specific business requirements when extending datasets beyond standard attributes.

What potential limitations might arise from comparing tools with distinct extraction scopes?

Comparing architecture recovery tools with distinct extraction scopes may pose some challenges:
1.. Incomparable Results:
Tools extracting vastly different sets of characteristics may produce results that are challenging to compare directly due
dissimilarities between extracted data points
2.. Evaluation Bias:
Evaluating diverse sets of extracted features against each other could introduce bias towards one set over another,
leading skewed conclusions about effectiveness
3.. Complexity:
Managing comparisons between complex extractions requires careful consideration; ensuring fair assessment without
favoritism towards simpler or more familiar outputs is crucial
4.. Resource Intensive:
Analyzing varied extractions comprehensively demands significant time investment; balancing depth of evaluation across all
dimensions while maintaining accuracy is essential
5.. Interpretation Challenges:
Interpreting results accurately when dealing with disparate data types necessitates nuanced understanding;
misinterpretations could lead incorrect assumptions about tool performance
By acknowledging these limitations upfront, researchers can develop robust methodologies mitigate risks associated comparison diverse extraction scopes efficiently evaluate effectiveness various architecture recovery approaches