| Autor: |
Linchang Zhao, Zhaowei Shang, Ling Zhao, Anyong Qin, Yuan Yan Tang |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 7, Pp 7663-7677 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2018.2889061 |
| Popis: |
Software defect prediction (SDP) exerts a major role in software development, concerning reducing software costs and ensuring software quality. However, developing an accurate SDP model is still a severe and challenging task with the lack of training data. Fortunately, Siamese networks are powerful for learning a few samples and have been perfectly used in other fields. This paper explores the advantages of Siamese networks to propose a novel SDP model, Siamese dense neural networks (SDNNs), which integrates similarity feature learning and distance metric learning into a unified approach. It mainly includes two phases: model building and training. To be more specific, it means building the novel SDNN for capturing the highest-level similarity features and training the model to realize prediction through the designed contrast loss function with cosine proximity. Importantly, we extensively compared the SDNN approach with the state-of-the-art SDP approaches utilizing 10 software defect datasets. The experimental results show that our SDNN is a competitive approach and is able to improve the prediction performance more significantly compared with the benchmarked approaches. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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