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毕业论文报告

毕业论文报告. 孙悦明 2012.04. 论文题目. 使用本体结构辅助系统化调研. 提纲. 论文工作介绍 系统化调研简介 结构化摘要简介 工作整体介绍 SLRONT 的构建 扩展 SLRONT 为 COSONT 实例化 COSONT 支持 SLR 中的关键步骤. 系统化调研简介. 系统化调研 SLR Protocol 定义研究问题 各个步骤的执行准则. Identification of Research. Study Selection. Study Quality Assessment. Data Extraction. Data

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毕业论文报告

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  1. 毕业论文报告 孙悦明 2012.04

  2. 论文题目 使用本体结构辅助系统化调研

  3. 提纲 • 论文工作介绍 • 系统化调研简介 • 结构化摘要简介 • 工作整体介绍 • SLRONT的构建 • 扩展SLRONT为COSONT • 实例化COSONT • 支持SLR中的关键步骤

  4. 系统化调研简介 • 系统化调研 • SLR Protocol • 定义研究问题 • 各个步骤的执行准则 Identification of Research Study Selection Study Quality Assessment Data Extraction Data Synthesis

  5. 结构化摘要简介 • 结构化摘要 • 完备性与清晰性 Background Object Method Result Conclusion

  6. 工作整体介绍 • 工作流程图

  7. SLRONT的构建 • Review Protocol部分

  8. SLRONT的构建 • Primary Study部分

  9. 扩展为COSONT • 主要扩展Structured abstract部分

  10. COSONT • 估算方法部分的扩展

  11. 实例化COSONT—非结构化摘要 • Background • Method • Conclusion part

  12. 实例化COSONT—基于规则的分析 In this paper, we propose an approach that converts cost estimation into a classification problem and that classifies new software projects in one of the effort classes, each of which corresponds to an effort interval. • MAINSEN • R1: S = PP, NP VP • R2: S = NP VP • CONCLU • R3: S = (PP,)+ NP VP • 定位结果 Resultsof the study show a significant correlation between the software development effort and all three models.

  13. 实例化COSONT—抽取本体信息 • 抽取的名词词组对比 • 抽取结论 The results show that KNN is an effective method. - amod(method-9, effective-8) - nsubj(method-9, KNN-5) - cop(method-9, is-6) - det(method-9, an-7) This paper describes a controlled experiment of student programmers performing maintenance tasks on a C++ program.

  14. 实例化COSONT

  15. 支持SLR中的关键步骤 • 辅助系统化调研第二步 • 寻找带有regression and neural network的文章 • 专家判断： • 自动化方法

  16. Q&A

  17. 感谢各位老师和同学！ 2012.04

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