Viet, A.P. and Nguyen, K.D.T. and Pham, L.V. (2019) Transfer learning for predicting software faults. In: 11th International Conference on Knowledge and Systems Engineering, KSE 2019, 24 October 2019 through 26 October 2019.
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Abstract
This paper investigates a transfer learning application for predicting software faults. Detecting faulty modules in software projects is challenging due to two main issues 1) the low quality of existing handcrafted features leads to the bad performance of traditional learning models and 2) the shortage of annotated data hinders applying deep neural networks. Recently, transfer learning is a good solution to train deep neural networks with insufficient data. Our experiments for tasks of within-project and cross-project software fault prediction have shown the transferable possibility among project data. As a result, the performance of the base model is significantly improved and achieves competitive results with the state of the art method. © 2019 IEEE.
Item Type: | Conference or Workshop Item (Paper) |
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Divisions: | Faculties > Faculty of Information Technology |
Identification Number: | 10.1109/KSE.2019.8919351 |
Uncontrolled Keywords: | Application programs; Computer aided instruction; Forecasting; Neural networks; Systems engineering; Convolutional neural network; Low qualities; Software fault; Software fault prediction; Software project; State-of-the-art methods; Traditional learning; Transfer learning; Deep neural networks |
Additional Information: | Conference code: 155691. Language of original document: English. |
URI: | http://eprints.lqdtu.edu.vn/id/eprint/9246 |