Automated quality assessment for crowdsourced test reports of mobile applications

@article{Chen2018AutomatedQA,
  title={Automated quality assessment for crowdsourced test reports of mobile applications},
  author={Xin Chen and He Jiang and Xiaochen Li and Tieke He and Zhenyu Chen},
  journal={2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
  year={2018},
  pages={368-379}
}
In crowdsourced mobile application testing, crowd workers help developers perform testing and submit test reports for unexpected behaviors. These submitted test reports usually provide critical information for developers to understand and reproduce the bugs. However, due to the poor performance of workers and the inconvenience of editing on mobile devices, the quality of test reports may vary sharply. At times developers have to spend a significant portion of their available resources to handle… CONTINUE READING

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Results and Topics from this paper.

Key Quantitative Results

  • Experiments conducted over five crowdsourced test report datasets of mobile applications show that TERQAF can correctly predict the quality of test reports with accuracy of up to 88.06% and outperform baselines by up to 23.06%.
  • Experimental results show that TERQAF can achieve 88.06% of accuracy in predicting the quality of test reports and outperform baselines by up to 23.06%.

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