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Found 18 papers, 1 papers with code
Exploring LLM-based Agents for Root Cause Analysis
Lastly, we conduct a case study with a team at Microsoft to equip the ReAct agent with tools that give it access to external diagnostic services that are used by the team for manual RCA.
Intelligent Monitoring Framework for Cloud Services: A Data-Driven Approach
We conduct an extensive empirical study and derive key insights on the major classes of monitors employed by cloud services at Microsoft, their associated dimensions, and the interrelationship between service properties and this ontology.
X-lifecycle Learning for Cloud Incident Management using LLMs
to generate insights for detection, root causing and mitigating of incidents.
Dependency Aware Incident Linking in Large Cloud Systems
In this paper, we propose the dependency-aware incident linking (DiLink) framework which leverages both textual and service dependency graph information to improve the accuracy and coverage of incident links not only coming from same service, but also from different services and workloads.
Automated Root Causing of Cloud Incidents using In-Context Learning with GPT-4
The results reveal that our in-context learning approach outperforms the previous fine-tuned large language models such as GPT-3 by an average of 24. 8\% across all metrics, with an impressive 49. 7\% improvement over the zero-shot model.
COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy
We address the challenge of learning safe and robust decision policies in presence of uncertainty in context of the real scientific problem of adaptive resource oversubscription to enhance resource efficiency while ensuring safety against resource congestion risk.
PACE-LM: Prompting and Augmentation for Calibrated Confidence Estimation with GPT-4 in Cloud Incident Root Cause Analysis
Major cloud providers have employed advanced AI-based solutions like large language models to aid humans in identifying the root causes of cloud incidents.
Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models
In this work, we do the first large-scale study to evaluate the effectiveness of these models for helping engineers root cause and mitigate production incidents.
AutoTSG: Learning and Synthesis for Incident Troubleshooting
To alleviate these gaps, we investigate the automation of TSGs and propose AutoTSG -- a novel framework for automation of TSGs to executable workflows by combining machine learning and program synthesis.
DeepAnalyze: Learning to Localize Crashes at Scale
We evaluate our model with over a million real-world crashes from four popular Microsoft applications and show that DeepAnalyze, trained with crashes from one set of applications, not only accurately localizes crashes of the same applications, but also bootstraps crash localization for other applications with zero to very little additional training data.
SoftNER: Mining Knowledge Graphs From Cloud Incidents
We have deployed SoftNER at Microsoft, a major cloud service provider and have evaluated it on more than 2 months of cloud incidents.
Nudge: Accelerating Overdue Pull Requests Towards Completion
The key novelty of Nudge is that it succeeds in reducing pull request resolution time, while ensuring that developers perceive the notifications sent as useful, at the scale of thousands of repositories.
Search4Code: Code Search Intent Classification Using Weak Supervision
We evaluate the approach against several baselines on a real-world dataset comprised of over 1 million queries mined from Bing web search engine and show that the CNN based model can achieve an accuracy of 77% and 76% for C# and Java respectively.
Neural Knowledge Extraction From Cloud Service Incidents
Prior work on incident management has been heavily focused on the challenges with incident triaging and de-duplication.
An Empirical Study of Software Exceptions in the Field using Search Logs
Using the machine learning model, we extracted exceptions from raw queries and performed popularity, effort, success, query characteristic and web domain analysis.
Product Insights: Analyzing Product Intents in Web Search
Web search engines are frequently used to access information about products.
Studying Ransomware Attacks Using Web Search Logs
Cyber attacks are increasingly becoming prevalent and causing significant damage to individuals, businesses and even countries.
Analyzing Web Search Behavior for Software Engineering Tasks
Subsequently, we propose a taxonomy of intents to identify the various contexts in which web search is used in software engineering.
