WS-DREAM: Towards Open Datasets and Source Code for Web Service Research

WS-DREAM is a Distributed REliability Assessment Mechanism for Web services. WS-DREAM contains 3 main components:

Web services QoS prediction component, which allows users to carry out reliability and quality assessment of Web services in a collaborative way.
Web services log management component, which provides an end-to-end framework for the reliability enhancement of services systems via logs.
Web services review mining component, which analyzes user reviews automatically by natural language processing techniques.

This website is to disseminate our research results as well as release open datasets and source code for Web service research. With both datasets and source code publicly released, our WS-DREAM repository would allow ease of reproducing the existing approaches and flexibility of extending new ones.

+ Our publications about WS-DREAM have been cited 2000+ times in total.

+ Our datasets have been downloaded by 370+ research organizations globally, and have been used in 200+ papers.

+ Our prototypes have been used by industrial groups in Microsoft, Tencent, and Huawei.

Log Management

We have implemented (1) log advisor, (2) log parsers, and (3) anomaly detectors, and released their source code in our GitHub repository. For log advisor, we implement the "learning to log" framework that can determine optimal logging points. Considering log parser, 7 log parsers have been released, including 5 offline log parsers and 2 online log parsers. For anomaly detector, we implement 6 log-based anomaly detection methods, including 3 unsupervised detectors and 3 supervised detectors. Please feel free to contact us if you have any comments or questions regarding the code. We also appreciate any contributions from you.

Fork GitHub Repo

Review Mining

We have implemented (1) IDEA, (2) CrossMiner, and (3) PAID, and released their source code in our GitHub repository. In particular, IDEA aims to identify emerging issues for mobile services effectively based on only review analysis. CrossMiner analyzes essential mobile services issues on different platforms. PAID is a framework that prioritizes mobile services issues for developers. Please feel free to contact us if you have any comments or questions regarding the code. We also appreciate any contributions from you.

Fork GitHub Repo

Bibliography

Five representative publications that use or cite WS-DREAM:

Min Du, Feifei Li, Guineng Zheng, Vivek Srikumar, "DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning," in Proc. of ACM Conference on Computer and Communications Security (CCS), 2017.
Goran Delac, Marin Silic, and Sinisa Srbljic, "A Reliability Improvement Method for SOA-Based Applications," IEEE Transactions on Dependable and Secure Computing (TDSC), 2015.
Wancai Zhang, Hailong Sun, Xudong Liu, and Xiaohui Guo, "Temporal QoS-Aware Web Service Recommendation via Non-negative Tensor Factorization," in Proc. of International World Wide Web Conference (WWW), 2014.
Marin Silic, Goran Delac, and Sinisa Srbljic, "Prediction of Atomic Web Services Reliability Based on K-Means Clustering," in Proc. of Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE), 2013.
Wei Lo, Jianwei Yin, Shuiguang Deng, Ying Li, and Zhaohui Wu, "An Extended Matrix Factorization Approach for QoS Prediction in Service Selection," in Proc. of International Conference on Services Computing (SCC), 2012. [Best Student Paper Award]

Publication

Ten representative publications about WS-DREAM: [Full list]

Shilin He, Qingwei Lin, Jian-Guang Lou, Hongyu Zhang, Michael R.Lyu, Dongmei Zhang, "Identifying Impactful Service System Problems via Log Analysis," in Proc. of the 26th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), 2018.
Cuiyun Gao, Jichuan Zeng, Michael R. Lyu, Irwin King, "Online App Review Analysis for Identifying Emerging Issues," in Proc. of the 40th International Conference on Software Engineering (ICSE), 2018.
Pinjia He, Jieming Zhu, Shilin He, Jian Li, Michael R. Lyu, "Towards Automated Log Parsing for Large-Scale Log Data Analysis," IEEE Transactions on Dependable and Secure Computing (TDSC), 2017.
Jieming Zhu, Pinjia He, Zibin Zheng, Michael R. Lyu, "Online QoS Prediction for Runtime Service Adaptation via Adaptive Matrix Factorization," IEEE Transactions on Parallel and Distributed Systems (TPDS), 2017.
Hongbing Wang, Lei Wang, Qi Yu, Zibin Zheng, Michael R. Lyu, Athman Bouguettaya, "Online Reliability Prediction via Motifs-based Dynamic Bayesian Networks for Service-Oriented Systems," IEEE Transactions on Software Engineering (TSE), 2017.
Jieming Zhu, Pinjia He, Qiang Fu, Hongyu Zhang, Michael R. Lyu, Dongmei Zhang "Learning to Log: Helping Developers Make Informed Logging Decisions," in Proc. of the 37th International Conference on Software Engineering (ICSE), 2015.
Zibin Zheng, Hao Ma, Michael R. Lyu, Irwin King, "QoS-Aware Web Service Recommendation by Collaborative Filtering," IEEE Transactions on Services Computing (TSC), 2013.
Zibin Zheng, Michael R. Lyu, "Collaborative Reliability Prediction of Service-Oriented Systems," in Proc. of ACM/IEEE International Conference on Software Engineering (ICSE), 2010. [ACM SIGSOFT Distinguished Paper Award]
Zibin Zheng, Yilei Zhang, Michael R. Lyu, "Distributed QoS Evaluation for Real-World Web Services," in Proc. of IEEE International Conference on Web Services (ICWS), 2010. [Best Student Paper Award]
Zibin Zheng, Michael R. Lyu, "WS-DREAM: A distributed Reliability Assessment Mechanism for Web Services," in Proc. of IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2008.