The Use of Pretrained Model for Matching App Reviews and Bug Reports

Xiaojuan Wang, Wenyu Zhang, Shanyan Lai, Chunyang Ye, Hui Zhou
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Abstract

Matching APP reviews with bug reports can help APP developers to quickly identify new bugs from the users’ feedback. Existing solutions represent the semantics of APP reviews and bug reports via carefully designed features and models, the performance of which however depends heavily on the manually designed model and the training data set. Large-scale pretrained models can well capture the semantics of text and have demonstrated their success in many NLP tasks. Inspired by this, we explore the effect of various pretrained models on the matching accuracy of app review and bug report. We conduct a systematic study to analyze the factors of four major pretrained models (including T5, Sentence T5, Sentence MiniLM, Sentence BERT and so on) on the matching accuracy. We find that the accuracy of Sentence T5 and Sentence MiniLM in four open source applications is significantly greater than that of the state-of-the-art approach DeepMatcher. Based on the findings, we design a novel approach to match the APP reviews with bug reports based on the pretrained model Sentence T5 and Sentence MiniLM to calculate the sentence similarity. We test it on four open source applications and the results show that our method outperforms the existing solution. On average, the precision of Sentence T5 and Sentence MiniLM are increased by 17% and 13%, respectively, and the hit ratio are increased by 15% and 14%, respectively.
使用预训练模型匹配应用评论和漏洞报告
将APP评论与bug报告相匹配,可以帮助APP开发者从用户反馈中快速识别新的bug。现有的解决方案通过精心设计的功能和模型来表示APP审查和bug报告的语义,但其性能严重依赖于手动设计的模型和训练数据集。大规模预训练模型可以很好地捕获文本的语义,并已在许多NLP任务中证明了它们的成功。受此启发,我们探讨了各种预训练模型对应用审核和bug报告匹配精度的影响。我们系统地研究了四种主要的预训练模型(包括T5、Sentence T5、Sentence MiniLM、Sentence BERT等)对匹配精度的影响因素。我们发现,在四个开源应用程序中,句子T5和句子MiniLM的准确性明显高于最先进的方法DeepMatcher。在此基础上,我们设计了一种基于预训练模型Sentence T5和Sentence MiniLM计算句子相似度的APP评论与bug报告匹配方法。我们在四个开源应用程序上进行了测试,结果表明我们的方法优于现有的解决方案。平均而言,句子T5和句子MiniLM的准确率分别提高了17%和13%,命中率分别提高了15%和14%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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