A Hybrid Approach to Enhance Software Quality by Sentiment Analysis of Developer
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-56998-2_10
Reference18 articles.
1. Das, A.K., Yadav, S., Dhal, S.: Detecting code smells using deep learning. In: TENCON 2019–2019 IEEE Region 10 Conference (TENCON), pp. 2081–2086. IEEE (2019)
2. Patnaik, A., Padhy, N.: A hybrid approach to identify code smell using machine learning algorithms. Int. J. Open Source Soft. Processes (IJOSSP) 12(2), 21–35 (2021)
3. Sinha, V., Lazar, A., Sharif, B.: Analyzing developer sentiment in commit logs. In: Proceedings of the 13th International Conference on Mining Software Repositories, pp. 520–523 (2016)
4. Liao, Z., He, D., Chen, Z., Fan, X., Zhang, Y., Liu, S.: Exploring the characteristics of issue-related behaviors in github using visualization techniques. IEEE Access 6, 24003–24015 (2018)
5. Zanjani, M.B., Swartzendruber, G., Kagdi, H.: Impact analysis of change requests on source code based on interaction and commit histories. In: Proceedings of the 11th Working Conference on Mining Software Repositories, pp. 162–171 (2014)
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
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