ID 原文 译文
25315 然而，现有的软件度量指标主要集中在源代码的结构信息上，程序语义信息考虑较少。 However, the existing software metrics are mainly focused on structure information of source code, and the semantic information is lacking．
25316 编译优化是对程序语义进行深入分析的结果，直观地认为它应该在一定程度上能够反映程序的语义信息，有助于软件缺陷预测。 Compilation optimizationis the result of deep analysis of program semantics, and intuitively we believe that it should reflect the semantic information of the program in some ways to help defect prediction．
25317 因此，为分析编译优化度量指标对软件缺陷预测的影响，本文首先基于当前编译器中广泛使用的优化选项，设计了 9 种编译优化度量指标。结合源代码结构层面的度量指标，构建了 5 种软件缺陷预测度量模型。 Based on the optimization options widely used in the current compiler, this paper extracts 9 compilation optimization metrics, and proposes five types of metrics models that designed by different metrics sets．
25318 利用 weka 中提供的 13 种常用的分类器，对比分析了添加不同优化度量指标的模型效果，对编译优化度量与软件缺陷预测之间的关系进行了评价，同时与 DP-CNN(Defect Prediction via Convolutional Neural Network)模型进行了对比。 The relationship between compilation optimization metrics and software defect predictions was evaluated by 13 commonly used classifiers in weka, and also compared with DP-CNN．
25319 实验结果表明:编译优化度量指标对软件缺陷预测的召回率有显著影响; Experimental results show: Compilationoptimization metrics have a significant impact on the recall rate of software defect prediction;
25320 在代码复杂度度量指标的基础上增加编译优化度量指标，可以提升所有软件缺陷预测模型的性能，平均提升幅度约为 5% ; Static code metrics combined with compilation optimization metrics can improve the performance of software defect prediction in all classifiers, which can improve the performance of prediction by about 5% ;
25321 基于代码大小的优化度量和基于性能的优化度量具有各自的特点，两者相结合可以在软件缺陷预测中获得更好的性能。 Code size based optimization metrics and performance based optimization metrics have their characteristics, combined both of them can get better performance in software defect prediction．
25322 面向领域的自然语言理解技术是垂直搜索引擎、领域相关问答系统等应用的核心技术之一。 Domain-specific natural language understanding technology is one of the core technology of vertical search engines, domain-specific question answering system and other applications．
25323 本文在已构建的基于本体和语义文法的自然语言理解系统的基础上，提出一种基于错误驱动的语义文法自动扩展学习方法，对于解析错误的句子，利用核心文法生成部分解析树，按照打分函数选择一组最佳的部分解析树，利用预测模型预测部分解析树的上层节点并试图构建完整的解析树，从而学习得到新的文法规则，对于学习得到的不同类型的规则进行验证并更新核心文法库，通过对句子的可学习性度量来筛选学习对象，从而提高文法扩展学习的整体质量和效率。 This research focus on a novel constrained semantic grammar and its automatic learning methods based on an existing domain-specific question answering system. An error-driven learning method of semantic grammar is proposed. The method first partially parses the ungrammatical sentence based on the core semantic grammar, then it attempts to build a complete parse tree, including predicting the top-level node of the partial parsing tree, generating and verifying hypotheses of new grammar rules. Learnability metrics is used to filter sentences in the training corpus to improve the overall quality and efficiency of grammar extending algorithm．
25324 分别在两个不同规模的领域数据集进行了测试，在交互式学习范式下，测试对比了学习算法在不同规模领域的学习效率，在批量学习范式下，测试对比了更新后的文法和核心文法在两个领域数据集上的准确率和识别率等性能指标。 The proposed algorithm is applied to two domains of different scales. In the interactive learning paradigm, learning efficiency are compared in different domains. In the batch learning paradigm, the paper compares the accuracy, MＲＲ and recognition rate of the extended grammar and core grammar on two datasets．