Enhancing software model encoding for feature location approaches based on machine learning techniques-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Enhancing software model encoding for feature location approaches based on machine learning techniques

Published:2021-08-23 Issue: Volume: Page:
ISSN:1619-1366
Container-title:Software and Systems Modeling
language:en
Short-container-title:Softw Syst Model

Author:

Marcén Ana C.,Pérez Francisca,Pastor Óscar,Cetina Carlos

Funder

Generalitat Valenciana

Secretaría de Estado de Investigación, Desarrollo e Innovación

Ministerio de Ciencia, Innovación y Universidades

Publisher

Springer Science and Business Media LLC

Subject

Modelling and Simulation,Software

Link

https://link.springer.com/content/pdf/10.1007/s10270-021-00920-y.pdf

Reference95 articles.

1. Antoniol, G., Canfora, G., Casazza, G., De Lucia, A., Merlo, E.: Recovering traceability links between code and documentation. IEEE Trans. Softw. Eng. 28(10), 970–983 (2002)

2. Arcega, L., Font, J., Haugen, Ø., Cetina, C.: An approach for bug localization in models using two levels: model and metamodel. Softw. Syst. Model. 18, 3551–3576 (2019). https://doi.org/10.1007/s10270-019-00727-y

3. Arcuri, A., Briand, L.: A Hitchhiker’s guide to statistical tests for assessing randomized algorithms in software engineering. Softw. Test. Verifi. Reliab. 24(3), 219–250 (2014)

4. Arcuri, A., Fraser, G.: Parameter tuning or default values? An empirical investigation in search-based software engineering. Empir. Softw. Eng. 18(3), 594–623 (2013)

5. B. Le, T.D., Lo, D., Le Goues, C., Grunske, L.: A learning-to-rank based fault localization approach using likely invariants. In: Proceedings of the 25th International Symposium on Software Testing and Analysis, pp. 177–188. ACM, New York, USA (2016)

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. FeatRacer: Locating Features Through Assisted Traceability;IEEE Transactions on Software Engineering;2023-12

2. Feature Location Using Extraction of Code Documentation;Proceedings of the 8th International Conference on Sustainable Information Engineering and Technology;2023-10-24

同舟云学术

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

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3