Bibliographische Detailangaben
| Titel: |
A Survey on Software Defect Prediction Using Deep Learning. |
| Autoren: |
Akimova, Elena N., Bersenev, Alexander Yu., Deikov, Artem A., Kobylkin, Konstantin S., Konygin, Anton V., Mezentsev, Ilya P., Misilov, Vladimir E. |
| Quelle: |
Mathematics (2227-7390); Jun2021, Vol. 9 Issue 11, p1180, 1p |
| Schlagwörter: |
SOFTWARE reliability, COMPUTER software quality control, DEEP learning, MACHINE learning, COMPUTER software, PROGRAMMING languages |
| Abstract: |
Defect prediction is one of the key challenges in software development and programming language research for improving software quality and reliability. The problem in this area is to properly identify the defective source code with high accuracy. Developing a fault prediction model is a challenging problem, and many approaches have been proposed throughout history. The recent breakthrough in machine learning technologies, especially the development of deep learning techniques, has led to many problems being solved by these methods. Our survey focuses on the deep learning techniques for defect prediction. We analyse the recent works on the topic, study the methods for automatic learning of the semantic and structural features from the code, discuss the open problems and present the recent trends in the field. [ABSTRACT FROM AUTHOR] |
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