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Automatic test report augmentation to assist crowdsourced testing

  • Xin Chen
  • He JiangEmail author
  • Zhenyu Chen
  • Tieke He
  • Liming Nie
Research Article
  • 12 Downloads

Abstract

In crowdsourced mobile application testing, workers are often inexperienced in and unfamiliar with software testing. Meanwhile, workers edit test reports in descriptive natural language on mobile devices. Thus, these test reports generally lack important details and challenge developers in understanding the bugs. To improve the quality of inspected test reports, we issue a new problem of test report augmentation by leveraging the additional useful information contained in duplicate test reports. In this paper, we propose a new framework named test report augmentation framework (TRAF) towards resolving the problem. First, natural language processing (NLP) techniques are adopted to preprocess the crowdsourced test reports. Then, three strategies are proposed to augment the environments, inputs, and descriptions of the inspected test reports, respectively. Finally, we visualize the augmented test reports to help developers distinguish the added information. To evaluate TRAF, we conduct experiments over five industrial datasets with 757 crowdsourced test reports. Experimental results show that TRAF can recommend relevant inputs to augment the inspected test reports with 98.49% in terms of NDCG and 88.65% in terms of precision on average, and identify valuable sentences from the descriptions of duplicates to augment the inspected test reports with 83.58% in terms of precision, 77.76% in terms of recall, and 78.72% in terms of F-measure on average. Meanwhile, empirical evaluation also demonstrates that augmented test reports can help developers understand and fix bugs better.

Keywords

crowdsourced testing test report TF-IDF natural language processing test report augmentation 

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Notes

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 61370144, 61722202, 61403057, and 61772107), and Jiangsu Prospective Project of Industry- University-Research (BY2015069-03). Besides, the authors would thank the three graduate students who devote their efforts for the data annotation.

Supplementary material

11704_2018_7308_MOESM1_ESM.ppt (276 kb)
Supplementary material, approximately 276 KB.

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Xin Chen
    • 1
    • 2
  • He Jiang
    • 2
    • 3
    Email author
  • Zhenyu Chen
    • 4
  • Tieke He
    • 4
  • Liming Nie
    • 5
  1. 1.School of Computer Science and TechnologyHangzhou Dianzi UniversityHangzhouChina
  2. 2.School of SoftwareDalian University of TechnologyDalianChina
  3. 3.Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceDalianChina
  4. 4.School of SoftwareNanjing UniversityNanjingChina
  5. 5.School of Information Science and TechnologyZhejiang Sci-Tech UniversityHangzhouChina

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