The repository includes the data in FoSCI, which is our work early accessed on TSE2019.
documentation: the specification, user guide of the subjects.
testscenario: the test scenarios for manual functional testing.
testplan: the formal testplan files with test cases, recorded by Apache JMeter.
traces: the collected execution traces.
supplements： the supplementary materials for FoSCI paper draft.
Our FOSCI implementation is also published and please see the fosci-wrap on the release page (V2 release).
Software industrial practices decouple legacy software systems into microservice architectures to improve software maintainability and to achieve faster market delivery. Evaluating microservice code maintainability is a critical issue, facing the difficulties of multi-sourced data and diverse concerns of maintainability. By analyzing source code, code execution trace, and code revision history, we propose a multi-sourced feature space model to unify the representation of software multi-sourced data. Based on this model, we establish a microservice maintainability measurement system with comprehensive metrics, in terms of the concerns of functionality, modularity, modifiability, and interaction complexity. Accordingly, we also implement a tool prototype called MicroEvaluator, and carry out experimental analysis on open-source software systems.
Please see our paper for details: Jin WX, Zhong DH, Zhang YY, Yang MF, Liu T. Microservice Maintainability Measurement Based on Multi-sourced Feature Space[J]. Journal of Software, 2021, 32(5): 1322-1340
Please star this repository and reference the following papers if FoSCI helps you. We are continuing to update this work.
@article{jin2019service,
title={Service candidate identification from monolithic systems based on execution traces},
author={Jin, Wuxia and Liu, Ting and Cai, Yuanfang and Kazman, Rick and Mo, Ran and Zheng, Qinghua},
journal={IEEE Transactions on Software Engineering},
year={2019},
publisher={IEEE}
}
@inproceedings{jin2018functionality,
title={Functionality-oriented microservice extraction based on execution trace clustering},
author={Jin, Wuxia and Liu, Ting and Zheng, Qinghua and Cui, Di and Cai, Yuanfang},
booktitle={2018 IEEE International Conference on Web Services (ICWS)},
pages={211--218},
year={2018},
organization={IEEE}
}
@article{晋武侠2021基于多源特征空间的微服务可维护性评估,
title={基于多源特征空间的微服务可维护性评估},
author={晋武侠 and 钟定洪 and 张宇云 and 杨名帆 and 刘烃},
journal={软件学报},
volume={32},
number={5},
pages={1322--1340},
year={2021}
}