| 摘要: |
| 移动应用(APP)软件的版本更新速度正在加快, 对软件缺陷的有效分析, 可以帮助开发人员理解和及时修复软件缺陷. 然而, 现有研究的分析对象大多较为单一, 存在信息孤立、零散、质量差等问题, 并且没有充分考虑数据验证及版本失配问题, 分析结果存在较大误差, 导致无效的软件演化. 为了提供更有效的缺陷分析结果, 提出一种面向版本演化的APP软件缺陷跟踪分析方法(ASD-TAOVE). 首先, 从多源、异构的APP软件数据中抽取APP软件缺陷内容并挖掘缺陷事件的因果关系; 接着, 设计了一种APP软件缺陷内容验证方法, 基于信息熵结合文本特征和结构特征定量分析缺陷怀疑度, 用于缺陷内容验证并构建APP软件缺陷内容异构图; 然后, 为了考虑版本演化带来的影响, 设计了一个APP软件缺陷跟踪分析方法, 用于在版本演化中分析缺陷的演化关系, 并将其转化为缺陷/演化元路径; 最后, 通过一个基于深度学习的异构信息网络完成APP软件缺陷分析. 针对4个研究问题(RQ)的实验结果, 证实了ASD-TAOVE方法在面向版本演化过程中对缺陷内容验证与跟踪分析的有效性, 缺陷识别准确率分别提升约9.9%和5% (平均7.5%). 与同类基线方法相比, ASD-TAOVE方法可分析丰富的APP软件数据, 提供有效的缺陷信息. |
| 关键词: APP软件缺陷 跟踪分析 版本演化 缺陷内容提取 缺陷内容验证 异构信息网络 |
| DOI：10.13328/j.cnki.jos.007107 |
| 分类号: |
| 基金项目:国家自然科学基金(62162038,61462049,61063006,60703116);国家重点研发计划(2018YFB1003904);云南省计算机技术应用重点实验室开放基金(2020101) |
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| APP Software Defect Tracking and Analysis Method Oriented to Version Evolution |
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LIU Hai-Yi1,2, JIANG Ying1,2, ZHAO Ze-Jiang1,2
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1.Yunnan Key Laboratory of Artificial Intelligence (Kunming University of Science and Technology), Kunming 650504, China;2.Faculty of Information Engineering and Automation (Kunming University of Science and Technology), Kunming 650504, China
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| Abstract: |
| The speed of evolution in mobile application (APP) software market is accelerating. Effective analysis of software defects can help developers understand and repair software defects in time. However, the analysis object of existing research is not enough, which leads to isolated, fragmented information, and poor information quality. In addition, because of insufficient consideration of data verification and version mismatch issues, there are some errors in the analysis results, resulting in invalid software evolution. In order to provide more effective defect analysis results, an APP software defect tracking and analysis method oriented to version evolution (ASD-TAOVE) is proposed. First, the content of APP software defects is extracted from multi-source, heterogeneous APP software data, and the causal relationship of defect events is discovered. Then, a verification method for APP software defect content is designed, which is based on information entropy combined with text features and structural features to calculate the defect suspicious formula for verification and construction of APP software defect content heterogeneity graph. In order to consider the impact of version evolution, an APP software defect tracking analysis method is designed to analyze the evolution relationship of defects in version evolution. The evolution relationship can be transformed into the defect/evolutionary meta-paths which are useful for defect analysis. Finally, this study designs a heterogeneous information network based on deep learning to complete APP software defect analysis. The experimental results of four research questions (RQ) confirmed the effectiveness of ASD-TAOVE method of defect content verification and tracking analysis in the process of version-oriented evolution, and the accuracy of defect identification increased by about 9.9% and 5% respectively (average 7.5%). Compared with baseline methods, the ASD-TAOVE method can analyze more APP software data and provide effective defect information. |
| Key words: APP software defect tracking analysis version evolution defect content extraction defect content verification heterogeneous information network |
