@article{wang_yu_qiu_jin_mueller_2020, title={BarrierFinder: recognizing ad hoc barriers}, volume={25}, ISSN={["1573-7616"]}, DOI={10.1007/s10664-020-09862-3}, number={6}, journal={EMPIRICAL SOFTWARE ENGINEERING}, author={Wang, Tao and Yu, Xiao and Qiu, Zhengyi and Jin, Guoliang and Mueller, Frank}, year={2020}, month={Nov}, pages={4676–4706} }
 @article{shao_qiu_yu_yang_jin_xie_wu_2020, title={Database-Access Performance Antipatterns in Database-Backed Web Applications}, ISSN={["1063-6773"]}, DOI={10.1109/ICSME46990.2020.00016}, abstractNote={Database-backed web applications are prone to performance bugs related to database accesses. While much work has been conducted on database-access antipatterns with some recent work focusing on performance impact, there still lacks a comprehensive view of database-access performance antipatterns in database-backed web applications. To date, no existing work systematically reports known antipatterns in the literature, and no existing work has studied database-access performance bugs in major types of web applications that access databases differently.To address this issue, we first summarize all known database-access performance antipatterns found through our literature survey, and we report all of them in this paper. We further collect database-access performance bugs from web applications that access databases through language-provided SQL interfaces, which have been largely ignored by recent work, to check how extensively the known antipatterns can cover these bugs. For bugs not covered by the known antipatterns, we extract new database-access performance antipatterns based on real-world performance bugs from such web applications. Our study in total reports 24 known and 10 new database-access performance antipatterns. Our results can guide future work to develop effective tool support for different types of web applications.}, journal={2020 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE MAINTENANCE AND EVOLUTION (ICSME 2020)}, author={Shao, Shudi and Qiu, Zhengyi and Yu, Xiao and Yang, Wei and Jin, Guoliang and Xie, Tao and Wu, Xintao}, year={2020}, pages={58–69} }
 @article{wang_yu_qiu_jin_mueller_2019, title={BARRIERFINDER: Recognizing Ad Hoc Barriers}, ISSN={["1063-6773"]}, DOI={10.1109/ICSME.2019.00049}, abstractNote={Ad hoc synchronizations are pervasive in multi-threaded programs. Due to their diversity and complexity, understanding the enforced synchronization relationships of ad hoc synchronizations is challenging but crucial to multi-threaded program development and maintenance. Existing techniques can partially detect primitive ad hoc synchronizations, but they cannot recognize complete implementations or infer the enforced synchronization relationships. In this paper, we propose a framework to automatically identify complex ad hoc synchronizations in full and infer their synchronization relationships. We instantiate the framework with a tool called BarrierFinder, which features various techniques, including program slicing and bounded symbolic execution, to efficiently explore the interleaving space of ad hoc synchronizations within multi-threaded programs and collect execution traces. BarrierFinder then uses these traces to characterize ad hoc synchronizations into different types with a focus on recognizing barriers. Our evaluation shows that BarrierFinder is both effective and efficient in doing this, and BarrierFinder is also helpful for programmers to understand the correctness of their implemented ad hoc synchronizations.}, journal={2019 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE MAINTENANCE AND EVOLUTION (ICSME 2019)}, author={Wang, Tao and Yu, Xiao and Qiu, Zhengyi and Jin, Guoliang and Mueller, Frank}, year={2019}, pages={323–327} }
 @article{yu_jin_2018, title={Dataflow Tunneling}, DOI={10.1145/3180155.3180171}, abstractNote={Request-based applications, e.g., most server-side applications, expose services to users in a request-based paradigm, in which requests are served by request-handler methods. An important task for request-based applications is inter-request analysis, which analyzes request-handler methods that are related by inter-request data dependencies together. However, in the request-based paradigm, data dependencies between related request-handler methods are implicitly established by the underlying frameworks that execute these methods. As a result, existing analysis tools are usually limited to the scope of each single method without the knowledge of dependencies between different methods. In this paper, we design an approach called dataflow tunneling to capture inter-request data dependencies from concrete application executions and produce data-dependency specifications. Our approach answers two key questions: (1) what request-handler methods have data dependencies and (2) what these data dependencies are. Our evaluation using applications developed with two representative and popular frameworks shows that our approach is general and accurate. We also present a characteristic study and a use case of cache tuning based on the mined specifications. We envision that our approach can provide key information to enable future inter-request analysis techniques.}, journal={PROCEEDINGS 2018 IEEE/ACM 40TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE)}, author={Yu, Xiao and Jin, Guoliang}, year={2018}, pages={586–597} }
 @article{yu_joshi_xu_jin_zhang_jiang_2016, title={CloudSeer: Workflow Monitoring of Cloud Infrastructures via Interleaved Logs}, volume={51}, ISSN={["1558-1160"]}, DOI={10.1145/2954679.2872407}, abstractNote={Cloud infrastructures provide a rich set of management tasks that operate computing, storage, and networking resources in the cloud. Monitoring the executions of these tasks is crucial for cloud providers to promptly find and understand problems that compromise cloud availability. However, such monitoring is challenging because there are multiple distributed service components involved in the executions. CloudSeer enables effective workflow monitoring. It takes a lightweight non-intrusive approach that purely works on interleaved logs widely existing in cloud infrastructures. CloudSeer first builds an automaton for the workflow of each management task based on normal executions, and then it checks log messages against a set of automata for workflow divergences in a streaming manner. Divergences found during the checking process indicate potential execution problems, which may or may not be accompanied by error log messages. For each potential problem, CloudSeer outputs necessary context information including the affected task automaton and related log messages hinting where the problem occurs to help further diagnosis. Our experiments on OpenStack, a popular open-source cloud infrastructure, show that CloudSeer's efficiency and problem-detection capability are suitable for online monitoring.}, number={4}, journal={ACM SIGPLAN NOTICES}, author={Yu, Xiao and Joshi, Pallavi and Xu, Jianwu and Jin, Guoliang and Zhang, Hui and Jiang, Guofei}, year={2016}, month={Apr}, pages={489–502} }
 @inproceedings{yu_joshi_xu_jin_zhang_jiang_2016, title={CloudSeer: Workflow monitoring of cloud infrastructures via interleaved logs}, volume={50}, number={2}, booktitle={Operating Systems Review}, author={Yu, X. and Joshi, P. and Xu, J. W. and Jin, G. L. and Zhang, H. and Jiang, G. F.}, year={2016}, pages={489–502} }