{"title":"基于深度图像理解的自动Bug推断","authors":"Shengcheng Yu, Wanmin Huang, Jingui Zhang, Haitao Zheng","doi":"10.1109/DSA56465.2022.00051","DOIUrl":null,"url":null,"abstract":"In mobile crowdsourced testing, crowdworkers are usually far from experts, and low-quality bug reports are submitted, in which the bug descriptions are usually poorly written. Thus, the bug descriptions are hard to read and helpless for bug inference and bug understanding. To ease the understanding of bug scenarios, we present a novel method called BIU (Bug Inference via Image Understanding), which employs image understanding techniques to help crowdworkers automatically infer bugs and generate bug descriptions using bug screenshots. In this way, the burden of crowdworkers will be lowered and their working efficiency and report quality will be greatly improved. According to our preliminary experiments, the accuracy of BIU can reach up to 90%. The demonstration video can be found at: https://youtu.be/ZBOIqtdRFaU.","PeriodicalId":208148,"journal":{"name":"2022 9th International Conference on Dependable Systems and Their Applications (DSA)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automatic Bug Inference via Deep Image Understanding\",\"authors\":\"Shengcheng Yu, Wanmin Huang, Jingui Zhang, Haitao Zheng\",\"doi\":\"10.1109/DSA56465.2022.00051\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In mobile crowdsourced testing, crowdworkers are usually far from experts, and low-quality bug reports are submitted, in which the bug descriptions are usually poorly written. Thus, the bug descriptions are hard to read and helpless for bug inference and bug understanding. To ease the understanding of bug scenarios, we present a novel method called BIU (Bug Inference via Image Understanding), which employs image understanding techniques to help crowdworkers automatically infer bugs and generate bug descriptions using bug screenshots. In this way, the burden of crowdworkers will be lowered and their working efficiency and report quality will be greatly improved. According to our preliminary experiments, the accuracy of BIU can reach up to 90%. The demonstration video can be found at: https://youtu.be/ZBOIqtdRFaU.\",\"PeriodicalId\":208148,\"journal\":{\"name\":\"2022 9th International Conference on Dependable Systems and Their Applications (DSA)\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 9th International Conference on Dependable Systems and Their Applications (DSA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DSA56465.2022.00051\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 9th International Conference on Dependable Systems and Their Applications (DSA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSA56465.2022.00051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
在移动众包测试中，众包工作者通常与专家相距甚远，提交的bug报告质量很低，其中的bug描述通常写得很差。因此，错误描述很难阅读，也无法进行错误推理和错误理解。为了简化对bug场景的理解，我们提出了一种名为BIU (bug Inference via Image understanding)的新方法，该方法使用图像理解技术来帮助众包工作者自动推断bug并使用bug截图生成bug描述。这样可以减轻众包工作者的负担，大大提高他们的工作效率和报告质量。根据我们的初步实验，BIU的准确率可以达到90%。该演示视频可在https://youtu.be/ZBOIqtdRFaU上找到。
Automatic Bug Inference via Deep Image Understanding
In mobile crowdsourced testing, crowdworkers are usually far from experts, and low-quality bug reports are submitted, in which the bug descriptions are usually poorly written. Thus, the bug descriptions are hard to read and helpless for bug inference and bug understanding. To ease the understanding of bug scenarios, we present a novel method called BIU (Bug Inference via Image Understanding), which employs image understanding techniques to help crowdworkers automatically infer bugs and generate bug descriptions using bug screenshots. In this way, the burden of crowdworkers will be lowered and their working efficiency and report quality will be greatly improved. According to our preliminary experiments, the accuracy of BIU can reach up to 90%. The demonstration video can be found at: https://youtu.be/ZBOIqtdRFaU.
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