| Autor: |
Kaishun Wu, Song Huang, Yaqing Shi, Jing Zhu, Shiqi Tang |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 10, Pp 130415-130423 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2022.3227930 |
| Popis: |
When analyzing the defects of crowdsourced testing, the testing reports need to be preprocessed, including removing duplicate and false positives. At present, most crowdsourced testing research focuses on duplication of the reports, which has achieved high precision. However, studies on reducing false positives of defects have rarely been conducted. Starting from the duplication of defects in the reports, this paper discusses the relationship between the duplication and the precision of defects and proposes an estimation approach based on the defect distribution in historical crowdsourced testing projects. Experiments have shown that our approach can provide a priori knowledge of defects and exhibits good stability. We applied this approach to the defect population estimation of crowdsourced testing. With better experimental results, our improved model is more accurate than the original model. We attempt to apply it to the estimation of the defect population of crowdsourced testing, which is more accurate than the original model. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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