An Approach to Semantic and Structural Features Learning for Software Defect Prediction
المؤلفون المشاركون
He, Peng
Meilong, Shi
Xiao, Haitao
Li, Huixin
Zeng, Cheng
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-04-06
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Research on software defect prediction has achieved great success at modeling predictors.
To build more accurate predictors, a number of hand-crafted features are proposed, such as static code features, process features, and social network features.
Few models, however, consider the semantic and structural features of programs.
Understanding the context information of source code files could explain a lot about the cause of defects in software.
In this paper, we leverage representation learning for semantic and structural features generation.
Specifically, we first extract token vectors of code files based on the Abstract Syntax Trees (ASTs) and then feed the token vectors into Convolutional Neural Network (CNN) to automatically learn semantic features.
Meanwhile, we also construct a complex network model based on the dependencies between code files, namely, software network (SN).
After that, to learn the structural features, we apply the network embedding method to the resulting SN.
Finally, we build a novel software defect prediction model based on the learned semantic and structural features (SDP-S2S).
We evaluated our method on 6 projects collected from public PROMISE repositories.
The results suggest that the contribution of structural features extracted from software network is prominent, and when combined with semantic features, the results seem to be better.
In addition, compared with the traditional hand-crafted features, the F-measure values of SDP-S2S are generally increased, with a maximum growth rate of 99.5%.
We also explore the parameter sensitivity in the learning process of semantic and structural features and provide guidance for the optimization of predictors.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Meilong, Shi& He, Peng& Xiao, Haitao& Li, Huixin& Zeng, Cheng. 2020. An Approach to Semantic and Structural Features Learning for Software Defect Prediction. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1196461
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Meilong, Shi…[et al.]. An Approach to Semantic and Structural Features Learning for Software Defect Prediction. Mathematical Problems in Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1196461
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Meilong, Shi& He, Peng& Xiao, Haitao& Li, Huixin& Zeng, Cheng. An Approach to Semantic and Structural Features Learning for Software Defect Prediction. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1196461
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1196461
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر