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The high tracking overhead, the amount of up-front effort required toselecting the trace points, and the lack of effective data analysis model arethe significant barriers to the adoption of intra-component tracking for faultdiagnosis today. This paper introduces a novel method for fault diagnosis bycombining adaptive function level dynamic tracking, target fault injection, andgraph convolutional network. In order to implement this method, we introducetechniques for (i) selecting function level trace points, (ii) constructingapproximate function call tree of program when using adaptive tracking, and(iii) constructing graph convolutional network with fault injection campaign.We evaluate our method using a web service benchmark composed of Redis, Nginx,Httpd, and SQlite. The experimental results show that this method outperformslog based method, full tracking method, and Gaussian influence method in theaccuracy of fault diagnosis, overhead, and performance impact on the diagnosistarget.

AFETM: Adaptive function execution trace monitoring for fault diagnosis