Performance Automation Practices for Xianyu Android Client

The Xianyu team, operating under a lean development model, faces strict performance goals (2‑week feature delivery, 1‑week development cycle, 1‑hour release readiness). Automation is critical for the success of their CI pipeline.

Problems identified:

Missing tools for Flutter performance data collection and UI automation; existing solutions are custom per business unit and do not support Flutter.

Test workload multiplies with the number of feature lanes (branches) in a release, making manual performance testing infeasible.

Solution overview:

Acquire Flutter performance metrics (FPS, CPU, memory, network) by extending native Android tools (e.g., dumpsys SurfaceFlinger) to handle Flutter's SurfaceView.

Implement UI automation for mixed Flutter/Native screens using Appium and Flutter's integration‑test framework.

Integrate performance scripts into CI so that each lane is automatically tested, results are collected, and alerts/reports are generated.

FPS collection script (Shell):

dumpsys SurfaceFlinger --latency -clear
#echo "dumpsys SurfaceFlinger..."
if [[ $isflutter = 0 ]]; then
  window=`dumpsys window windows | grep mCurrent | $bb awk '{print $3}'|$bb tr -d '}'` # Get the current window
  echo $window
fi
if [[ $isflutter = 1 ]]; then
  window=`dumpsys SurfaceFlinger --list |grep '^SurfaceView'|$bb awk 'NR==1{print $0}'`
fi
if [ -z "$window" ]; then
  window="SurfaceView"
fi
fi
$bb usleep $sleep_t
dumpsys SurfaceFlinger --latency "$window"|$bb awk -v time=$uptime -v target=$target -v kpi=$KPI "$awkfike" >>$file

CPU usage collection (Shell):

export bb="/data/local/tmp/busybox"
$bb top -b -n 1|$bb awk 'NR==4{print NF-1}'

Memory statistics extraction (Shell):

do_statistics(){
  ((COUNT+=1))
  isExist="$(echo $OUTPUT | grep "DalvikHeap")"
  if [[ ! -n $isexist ]]; then
    old_dumpsys=true
  else
    old_dumpsys=false
  fi
  if [[ $old_dumpsys = true ]]; then
    java_heap="$(echo $OUTPUT | grep "Dalvik" | $bb awk '{print $6}' | $bb tr -d '\r')"
  else
    java_heap="$(echo $OUTPUT | grep "DalvikHeap" | $bb awk '{print $8}' | $bb tr -d '\r')"
  fi
  ((JAVA_HEAP_TOTAL+=java_heap))
  ((JAVA_HEAP_AVG=JAVA_HEAP_TOTAL/COUNT))
  if [[ $java_heap -gt $JAVA_HEAP_PEAK ]]; then
    JAVA_HEAP_PEAK=$java_heap
  fi
  # Similar logic for native heap, graphics, unknown, etc.
}

Network traffic extraction (Shell):

uid="$(dumpsys package packages|$bb grep -E "Package|userId"|$bb awk -v OFS=" " '{if($1=="Package"){P=substr($2,2,length($2)-2)}else{if(substr($1,1,6)=="userId")print P,substr($1,8,length($1)-7)}}'|grep $package|$bb awk '{print $2}')"
echo "Net:"$uid
initreceive=`$bb awk -v OFS=" " 'NR>1{if($2=="wlan0"){wr[$4]+=$6;wt[$4]+=$8}else{if($2=="rmnet0"){rr[$4]+=$6;rt[$4]+=$8}}}END{for(i in wr){print i,wr[i]/1000,wt[i]/1000,"wifi"};for(i in rr){print i,rr[i]/1000,rt[i]/1000,"data"}}' /proc/net/xt_qtaguid/stats | grep $uid|$bb awk '{print $2}'`
inittransmit=`$bb awk -v OFS=" " 'NR>1{if($2=="wlan0"){wr[$4]+=$6;wt[$4]+=$8}else{if($2=="rmnet0"){rr[$4]+=$6;rt[$4]+=$8}}}END{for(i in wr){print i,wr[i]/1000,wt[i]/1000,"wifi"};for(i in rr){print i,rr[i]/1000,rt[i]/1000,"data"}}' /proc/net/xt_qtaguid/stats | grep $uid|$bb awk '{print $3}'`
getnet(){
  local data_t=`date +%Y/%m/%d" "%H:%M:%S`
  netdetail=`$bb awk -v OFS=, -v initreceive=$initreceive -v inittransmit=$inittransmit -v datat="$data_t" 'NR>1{if($2=="wlan0"){wr[$4]+=$6;wt[$4]+=$8}else{if($2=="rmnet0"){rr[$4]+=$6;rt[$4]+=$8}}}END{for(i in wr){print datat,i,wr[i]/1000-initreceive,wt[i]/1000-inittransmit,"wifi"};for(i in rr){print datat,i,rr[i]/1000-initreceive,rt[i]/1000-inittransmit,"data"}}' /proc/net/xt_qtaguid/stats | grep $uid
  echo $netdetail>>$filenet
}

These scripts are invoked from the CI pipeline, generating performance reports (FPS, CPU, memory, network) for each feature lane. The reports include metrics such as frame‑diff, jank, MFS, OKT, and SS, allowing quick identification of regressions.

Results demonstrated that the automated system could monitor dozens of lanes per release, detect performance degradations early, and support further tooling (AI error detection, test case generation, etc.).

References: TesterHome topics 2232 & 4775, related articles on Flutter integration testing and large‑scale data processing.