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Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents.
Correlating events with time series for incident diagnosis
Luo, C., Lou, J.-G., Lin, Q., Fu, Q., Ding, R., Zhang, D., and Wang, Z · 2014
Earlier work this paper cites.
Simple testing can prevent most critical failures: An analysis of production failures in distributed data-intensive systems
Yuan, D., Luo, Y., Zhuang, X., Rodrigues, G. R., Zhao, X., Zhang, Y., Jain, P., and Stumm, M · 2014
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Taking the blame game out of data centers operations with netpoirot
Arzani, B., Ciraci, S., Loo, B. T., Schuster, A., and Outhred, G · 2016
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Scalability bugs: When 100-node testing is not enough
Leesatapornwongsa, T., Stuardo, C. A., Suminto, R. O., Ke, H., Lukman, J. F., and Gunawi, H. S · 2017
Earlier work this paper cites.
Automated bug removal for software-defined networks
Wu, Y., Chen, A., Haeberlen, A., Zhou, W., and Loo, B. T · 2017
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An analysis of network-partitioning failures in cloud systems
Alquraan, A., Takruri, H., Alfatafta, M., and Al-Kiswany, S · 2018
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An empirical study on crash recovery bugs in large-scale distributed systems
Gao, Y., Dou, W., Qin, F., Gao, C., Wang, D., Wei, J., Huang, R., Zhou, L., and Wu, Y · 2018
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Robust and rapid adaption for concept drift in software system anomaly detection
Ma, M., Zhang, S., Pei, D., Huang, X., and Dai, H · 2018
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Understanding exception-related bugs in large-scale cloud systems
Chen, H., Dou, W., Jiang, Y., and Qin, F · 2019
Earlier work this paper cites.
An empirical investigation of incident triage for online service systems
Chen, J., He, X., Lin, Q., Xu, Y., Zhang, H., Hao, D., Gao, F., Xu, Z., Dang, Y., and Zhang, D · 2019
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Continuous incident triage for large-scale online service systems
Chen, J., He, X., Lin, Q., Zhang, H., Hao, D., Gao, F., Xu, Z., Dang, Y., and Zhang, D · 2019
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What bugs cause production cloud incidents?
Liu, H., Lu, S., Musuvathi, M., and Nath, S · 2019
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Decaf: Diagnosing and triaging performance issues in large-scale cloud services
Bansal, C., Renganathan, S., Asudani, A., Midy, O., and Janakiraman, M · 2020
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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How incidental are the incidents? characterizing and prioritizing incidents for large-scale online service systems
Chen, J., Zhang, S., He, X., Lin, Q., Zhang, H., Hao, D., Kang, Y., Gao, F., Xu, Z., Dang, Y., et al · 2020
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How to mitigate the incident? an effective troubleshooting guide recommendation technique for online service systems
Jiang, J., Lu, W., Chen, J., Lin, Q., Zhao, P., Kang, Y., Zhang, H., Xiong, Y., Gao, F., Xu, Z., et al · 2020
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Understanding, detecting and localizing partial failures in large system software
Lou, C., Huang, P., and Smith, S · 2020
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Automap: Diagnose your microservice-based web applications automatically
Ma, M., Xu, J., Wang, Y., Chen, P., Zhang, Z., and Wang, P · 2020
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Diagnosing root causes of intermittent slow queries in cloud databases
Ma, M., Yin, Z., Zhang, S., Wang, S., Zheng, C., Jiang, X., Hu, H., Luo, C., Li, Y., Qiu, N., et al · 2020
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Testing Configuration Changes in Context to Prevent Production Failures
Sun, X., Cheng, R., Chen, J., Ang, E., Legunsen, O., and Xu, T · 2020
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Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al · 2021
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Practical root cause localization for microservice systems via trace analysis
Li, Z., Chen, J., Jiao, R., Zhao, N., Wang, Z., Zhang, S., Wu, Y., Jiang, L., Yan, L., Wang, Z., et al · 2021
Cited alongside, same era.
Microhecl: High-efficient root cause localization in large-scale microservice systems
Liu, D., He, C., Peng, X., Lin, F., Zhang, C., Gong, S., Li, Z., Ou, J., and Wu, Z · 2021
Cited alongside, same era.
Jump-starting multivariate time series anomaly detection for online service systems
Ma, M., Zhang, S., Chen, J., Xu, J., Li, H., Lin, Y., Nie, X., Zhou, B., Wang, Y., and Pei, D · 2021
Cited alongside, same era.
Studying the usage of text-to-text transfer transformer to support code-related tasks
Mastropaolo, A., Scalabrino, S., Cooper, N., Palacio, D. N., Poshyvanyk, D., Oliveto, R., and Bavota, G · 2021
Cited alongside, same era.
Watson: Abstracting behaviors from audit logs via aggregation of contextual semantics
Zeng, J., Chua, Z. L., Chen, Y., Ji, K., Liang, Z., and Mao, J · 2021
Cited alongside, same era.
Using pre-trained language models to resolve textual and semantic merge conflicts (experience paper)
Zhang, J., Mytkowicz, T., Kaufman, M., Piskac, R., and Lahiri, S. K · 2022
Later among the works it cites.
Recommending root-cause and mitigation steps for cloud incidents using large language models
Ahmed, T., Ghosh, S., Bansal, C., Zimmermann, T., Zhang, X., and Rajmohan, S · 2023
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Chatgpt may pass the bar exam soon, but has a long way to go for the lexglue benchmark
Chalkidis, I · 2023
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Push-Button Reliability Testing for Cloud-Backed Applications with Rainmaker
Chen, Y., Sun, X., Nath, S., Yang, Z., and Xu, T · 2023
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Detection is better than cure: A cloud incidents perspective
Ganatra, V., Parayil, A., Ghosh, S., Kang, Y., Ma, M., Bansal, C., Nath, S., and Mace, J · 2023
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Onion: identifying incident-indicating logs for cloud systems
Zhang, X., Xu, Y., Qin, S., He, S., Qiao, B., Li, Z., Zhang, H., Li, X., Dang, Y., Lin, Q., et al · 2021
Cited alongside, same era.
Cloudrca: a root cause analysis framework for cloud computing platforms
Zhang, Y., Guan, Z., Qian, H., Xu, L., Liu, H., Wen, Q., Sun, L., Jiang, J., Fan, L., and Ke, M · 2021
Cited alongside, same era.
Understanding and detecting software upgrade failures in distributed systems
Zhang, Y., Yang, J., Jin, Z., Sethi, U., Rodrigues, K., Lu, S., and Yuan, D · 2021
Cited alongside, same era.
Vulrepair: a t5-based automated software vulnerability repair
Fu, M., Tantithamthavorn, C., Le, T., Nguyen, V., and Phung, D · 2022
Cited alongside, same era.
How to fight production incidents? an empirical study on a large-scale cloud service
Ghosh, S., Shetty, M., Bansal, C., and Nath, S · 2022
Cited alongside, same era.
An empirical study of log analysis at microsoft
He, S., Zhang, X., He, P., Xu, Y., Li, L., Kang, Y., Ma, M., Wei, Y., Dang, Y., Rajmohan, S., et al · 2022
Cited alongside, same era.
Sok: History is a vast early warning system: Auditing the provenance of system intrusions
Inam, M. A., Chen, Y., Goyal, A., Liu, J., Mink, J., Michael, N., Gaur, S., Bates, A., and Hassan, W. U · 2022
Cited alongside, same era.
Acto: Automatic End-to-End Testing for Operation Correctness of Cloud System Management
Gu, J. T., Sun, X., Zhang, W., Jiang, Y., Wang, C., Vaziri, M., Legunsen, O., and Xu, T · 2023
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Assess and summarize: Improve outage understanding with large language models
Jin, P., Zhang, S., Ma, M., Li, H., Kang, Y., Li, L., Liu, Y., Qiao, B., Zhang, C., Zhao, P., et al · 2023
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Evaluating gpt-4 and chatgpt on japanese medical licensing examinations
Kasai, J., Kasai, Y., Sakaguchi, K., Yamada, Y., and Radev, D · 2023
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Codec: Cost-effective duration prediction system for deadline scheduling in the cloud
Li, H., Ma, M., Liu, Y., Qin, S., Qiao, B., Yao, R., Chaturvedi, H., Tran, T., Chintalapati, M., Rajmohan, S., Lin, Q., and Zhang, D · 2023
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Did we miss something important? studying and exploring variable-aware log abstraction
Li, Z., Luo, C., Chen, T.-H., Shang, W., He, S., Lin, Q., and Zhang, D · 2023
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Configuration validation with large language models
Lian, X., Chen, Y., Cheng, R., Huang, J., Thakkar, P., and Xu, T · 2023
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Tiktoken: A python library for tokenizing text
OpenAI · 2023
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Evaluation of chatgpt as a question answering system for answering complex questions
Tan, Y., Min, D., Li, Y., Li, W., Hu, N., Chen, Y., and Qi, G · 2023
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Unsupervised anomaly detection on microservice traces through graph vae
Xie, Z., Xu, H., Chen, W., Li, W., Jiang, H., Su, L., Wang, H., and Pei, D · 2023
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Aegis: Attribution of control plane change impact across layers and components for cloud systems
Yan, X., Hsieh, K., Liyanage, Y., Ma, M., Chintalapati, M., Lin, Q., Dang, Y., and Zhang, D · 2023
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Traceark: Towards actionable performance anomaly alerting for online service systems
Zeng, Z., Zhang, Y., Xu, Y., Ma, M., Qiao, B., Zou, W., Chen, Q., Zhang, M., Zhang, X., Zhang, H., et al · 2023
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Traceark: Towards actionable performance anomaly alerting for online service systems
Zeng, Z., Zhang, Y., Xu, Y., Ma, M., Qiao, B., Zou, W., Chen, Q., Zhang, M., Zhang, X., Zhang, H., Gao, X., Fan, H., Rajmohan, S., Lin, Q., and Zhang, D · 2023
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System log parsing: A survey
Zhang, T., Qiu, H., Castellano, G., Rifai, M., Chen, C. S., and Pianese, F · 2023
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Automatic chain of thought prompting in large language models
Zhang, Z., Zhang, A., Li, M., and Smola, A · 2023
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Robust multimodal failure detection for microservice systems
Zhao, C., Ma, M., Zhong, Z., Zhang, S., Tan, Z., Xiong, X., Yu, L., Feng, J., Sun, Y., Zhang, Y., et al · 2023
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