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Found 6 papers, 2 papers with code
CodeScholar: Growing Idiomatic Code Examples
A tool that could generate realistic, idiomatic, and contextual usage examples for one or more APIs would be immensely beneficial to developers.
DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines
We integrate our constructs into the recent DSPy programming model for LMs, and present new strategies that allow DSPy to compile programs with LM Assertions into more reliable and accurate systems.
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.
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.
