Context-sensitive delta inference for identifying workload-dependent performance bottlenecks

  • Authors:
  • Xusheng Xiao;Shi Han;Dongmei Zhang;Tao Xie

  • Affiliations:
  • North Carolina State University, USA;Microsoft Research, China;Microsoft Research, China;North Carolina State University, USA

  • Venue:
  • Proceedings of the 2013 International Symposium on Software Testing and Analysis
  • Year:
  • 2013

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Abstract

Software hangs can be caused by expensive operations in responsive actions (such as time-consuming operations in UI threads). Some of the expensive operations depend on the input workloads, referred to as workload-dependent performance bottlenecks (WDPBs). WDPBs are usually caused by workload-dependent loops (i.e., WDPB loops) that contain relatively expensive operations. Traditional performance testing and single-execution profiling may not reveal WDPBs due to incorrect assumptions of workloads. To address these issues, we propose the DeltaInfer approach that predicts WDPB loops under large workloads via inferring iteration counts of WDPB loops using complexity models for the workload size. DeltaInfer incorporates a novel concept named context-sensitive delta inference that consists of two parts: temporal inference for inferring the complexity models of different program locations, and spatial inference for identifying WDPB loops as WDPB candidates. We conducted evaluations on two popular open-source GUI applications, and identified impactful WDPBs that caused 10 performance bugs.