{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T14:21:01Z","timestamp":1774534861650,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Taihu Laboratory of Deep Sea Technology and Science","award":["2035032202"],"award-info":[{"award-number":["2035032202"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>With the advancement of computer science, software has become increasingly prevalent across all facets of society, making software quality issues a focal point of industry concern. The scarcity of sufficient defect data in the early stages of projects undermines prediction accuracy, driving research into cross-project software defect prediction. The traditional manual measurement features face challenges due to the data distribution discrepancies between original and cross-project contexts, which hinder the prediction effectiveness. Furthermore, single features fail to comprehensively characterize software information. This paper proposes a domain adaptation and feature fusion-based cross-project software defect prediction method (DAFF-CPDP). The model employs the TCA+ algorithm for domain adaptation and utilizes an encoder layer for progressive feature fusion. Multiple Java projects were selected for evaluation. The comparisons with various baseline models demonstrated that the proposed model outperforms both the traditional machine learning-based feature models and the diverse deep learning-based single-feature or multi-feature models. Concurrently, this paper analyzes the impact of different source projects on target projects, confirming that class-balanced datasets and datasets with smaller distribution differences are more conducive to project prediction.<\/jats:p>","DOI":"10.3390\/a19040253","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T13:36:40Z","timestamp":1774532200000},"page":"253","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Project Software Defect Prediction Based on Domain Adaptation and Feature Fusion"],"prefix":"10.3390","volume":"19","author":[{"given":"Guanhua","family":"Guo","sequence":"first","affiliation":[{"name":"School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212114, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinglei","family":"Song","sequence":"additional","affiliation":[{"name":"School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212114, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212114, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Abdu, A., Zhai, Z., Algabri, R., Abdo, H.A., Hamad, K., and Al-antari, M.A. (2022). Deep Learning-Based Software Defect Prediction via Semantic Key Features of Source Code\u2014Systematic Survey. Mathematics, 10.","DOI":"10.3390\/math10173120"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s10515-024-00424-1","article-title":"Software Defect Prediction: Future Directions and Challenges","volume":"31","author":"Li","year":"2024","journal-title":"Autom. Softw. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1109\/TSE.2018.2877612","article-title":"Deep Semantic Feature Learning for Software Defect Prediction","volume":"46","author":"Wang","year":"2020","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez, I., Vallejo, F., J\u00e1tiva, P.P., and Dehghan Firoozabadi, A. (2024). An Analysis of WiFi Coverage Modeling for a Hotspot in the Parish of Checa Employing Deterministic and Empirical Propagation Models. Appl. Sci., 14.","DOI":"10.3390\/app142311120"},{"key":"ref_5","unstructured":"Deepalakshmi, J., and Chandran, M. (2021). A Detailed Literature Survey and Analysis of Heterogeneous Cross-Project Defect Prediction. Des. Eng., 14388\u201314395."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2320447","DOI":"10.1155\/2022\/2320447","article-title":"Cross-Project Defect Prediction Based on Two-Phase Feature Importance Amplification","volume":"2022","author":"Xing","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"He, Z., Peters, F., Menzies, T., and Yang, Y. (2013). Learning from Open-Source Projects: An Empirical Study on Defect Prediction. Proceedings of the 2013 ACM\/IEEE International Symposium on Empirical Software Engineering and Measurement, Baltimore, MD, USA, 10\u201311 October 2013, IEEE Computer Society.","DOI":"10.1109\/ESEM.2013.20"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"30037","DOI":"10.1109\/ACCESS.2020.2972644","article-title":"ALTRA: Cross-Project Software Defect Prediction via Active Learning and Tradaboost","volume":"8","author":"Yuan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1109\/TSE.1976.233837","article-title":"A Complexity Measure","volume":"SE-2","author":"McCabe","year":"1976","journal-title":"IIEEE Trans. Softw. Eng."},{"key":"ref_10","unstructured":"Halstead, M.H. (1977). Elements of Software Science (Operating and Programming Systems Series), Elsevier."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1109\/32.295895","article-title":"A Metrics Suite for Object Oriented Design","volume":"20","author":"Chidamber","year":"1994","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7877","DOI":"10.1007\/s00500-022-06830-5","article-title":"Software Defect Prediction Employing BiLSTM and BERT-Based Semantic Feature","volume":"26","author":"Uddin","year":"2022","journal-title":"Soft Comput."},{"key":"ref_13","unstructured":"Mou, L., Li, G., Liu, Y., Peng, H., Jin, Z., Xu, Y., and Zhang, L. (2014). Building Program Vector Representations for Deep Learning. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Dam, H.K., Pham, T., Ng, S.W., Tran, T., Grundy, J., Ghose, A., Kim, T., and Kim, C.-J. (2018). A Deep Tree-Based Model for Software Defect Prediction. arXiv.","DOI":"10.1109\/MSR.2019.00017"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1109\/TR.2020.3047396","article-title":"Software Defect Prediction Based on Gated Hierarchical LSTMs","volume":"70","author":"Wang","year":"2021","journal-title":"IEEE Trans. Rel."},{"key":"ref_16","first-page":"6230953","article-title":"Software Defect Prediction via Attention-Based Recurrent Neural Network","volume":"2019","author":"Fan","year":"2019","journal-title":"Sci. Program."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cruz, A.E.C., and Ochimizu, K. (2009). Towards Logistic Regression Models for Predicting Fault-Prone Code across Software Projects. Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement, Lake Buena Vista, FL, USA, 15\u201316 October 2009, IEEE.","DOI":"10.1109\/ESEM.2009.5316002"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","article-title":"Domain Adaptation via Transfer Component Analysis","volume":"22","author":"Pan","year":"2011","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nam, J., Pan, S.J., and Kim, S. (2013). Transfer Defect Learning. Proceedings of the 2013 35th International Conference on Software Engineering (ICSE), San Francisco, CA, USA, 18\u201326 May 2013, IEEE.","DOI":"10.1109\/ICSE.2013.6606584"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1109\/TSE.2016.2543218","article-title":"HYDRA: Massively Compositional Model for Cross-Project Defect Prediction","volume":"42","author":"Xia","year":"2016","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1109\/TR.2018.2804922","article-title":"Cross-Project and Within-Project Semisupervised Software Defect Prediction: A Unified Approach","volume":"67","author":"Wu","year":"2018","journal-title":"IEEE Trans. Reliab."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhu, Y., Yu, Q., and Chen, X. (2022). Cross-Project Defect Prediction Considering Multiple Data Distribution Simultaneously. Symmetry, 14.","DOI":"10.3390\/sym14020401"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, J., He, P., Zhu, J., and Lyu, M.R. (2017). Software Defect Prediction via Convolutional Neural Network. Proceedings of the 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS), Prague, Czech Republic, 25\u201329 July 2017, IEEE.","DOI":"10.1109\/QRS.2017.42"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/TSE.2018.2881961","article-title":"Automatic Feature Learning for Predicting Vulnerable Software Components","volume":"47","author":"Dam","year":"2021","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"65356","DOI":"10.1109\/ACCESS.2025.3559325","article-title":"Intrusion Detection in IoT Networks Using Dynamic Graph Modeling and Graph-Based Neural Networks","volume":"13","author":"Govea","year":"2025","journal-title":"IEEE Access"},{"key":"ref_26","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., and Bengio, Y. (May, January 30). Graph Attention Networks. Proceedings of the 2018 6th International Conference on Learning Representations (ICLR), Vancouver, BC, Canada."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107057","DOI":"10.1016\/j.infsof.2022.107057","article-title":"Software Defect Prediction with Semantic and Structural Information of Codes Based on Graph Neural Networks","volume":"152","author":"Zhou","year":"2022","journal-title":"Inf. Softw. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"14771","DOI":"10.1038\/s41598-024-65639-4","article-title":"Semantic and Traditional Feature Fusion for Software Defect Prediction Using Hybrid Deep Learning Model","volume":"14","author":"Abdu","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_29","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv."},{"key":"ref_30","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., and Dean, J. (2013). Distributed Representations of Words and Phrases and Their Compositionality. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-Sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2023","DOI":"10.1007\/s10462-021-10044-w","article-title":"Handling Class-Imbalance with KNN (Neighbourhood) Under-Sampling for Software Defect Prediction","volume":"55","author":"Goyal","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_33","unstructured":"He, H., Bai, Y., Garcia, E.A., and Li, S. (2008). ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning. Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong, 1\u20136 June 2008, IEEE."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Munir, H.S., Ren, S., Mustafa, M., Siddique, C.N., and Qayyum, S. (2021). Attention Based GRU-LSTM for Software Defect Prediction. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0247444"},{"key":"ref_35","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, Curran Associates, Inc."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/4\/253\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T13:47:13Z","timestamp":1774532833000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/4\/253"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,26]]},"references-count":35,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["a19040253"],"URL":"https:\/\/doi.org\/10.3390\/a19040253","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,26]]}}}