Article (Scientific journals)
A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry Forms - RCR Report
Belgacem, Hichem; Li, Xiaochen; BIANCULLI, Domenico et al.
2025In ACM Transactions on Software Engineering and Methodology, 34 (2), p. 56:1-56:7
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Abstract :
[en] This paper represents the Replicated Computational Results (RCR) related to our TOSEM paper “A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry Forms”, where we proposed LAFF, an approach to automatically suggest possible values of categorical fields in data entry forms, which is a common user interface feature in many software systems. In this RCR report, we provide details about our replication package. We make available the different scripts needed to fully replicate the results obtained in our paper.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SVV - Software Verification and Validation
Disciplines :
Computer science
Author, co-author :
Belgacem, Hichem ;  Luxembourg Institute of Science and Technology, Luxembourg
Li, Xiaochen ;  Dalian University of Technology, China
BIANCULLI, Domenico  ;  University of Luxembourg
Briand, Lionel ;  Lero SFI Centre for Software Research and University of Limerick, Ireland and University of Ottawa, Canada
External co-authors :
yes
Language :
English
Title :
A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry Forms - RCR Report
Publication date :
20 January 2025
Journal title :
ACM Transactions on Software Engineering and Methodology
ISSN :
1049-331X
Publisher :
Association for Computing Machinery (ACM)
Volume :
34
Issue :
2
Pages :
56:1-56:7
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Security, Reliability and Trust
Funding text :
Financial support for this work was provided by the Alphonse Weicker Foundation and by BGL BNP Paribas Luxembourg.
Available on ORBilu :
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