Transfer learning for cross-company software defect prediction

368 indexed citations

Abstract

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This paper, published in 2011, received 368 indexed citations. Written by Ying Ma, Guangchun Luo, Xue Zeng and Aiguo Chen covering the research area of Software and Information Systems. It is primarily cited by scholars working on Information Systems (333 citations), Software (297 citations) and Artificial Intelligence (84 citations). Published in Information and Software Technology.

Countries where authors are citing Transfer learning for cross-company software defect prediction

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This map shows the geographic impact of Transfer learning for cross-company software defect prediction. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Transfer learning for cross-company software defect prediction with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Transfer learning for cross-company software defect prediction more than expected).

Fields of papers citing Transfer learning for cross-company software defect prediction

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Transfer learning for cross-company software defect prediction. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Transfer learning for cross-company software defect prediction.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/10.1016/j.infsof.2011.09.007.

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