TY - GEN
T1 - Empirical Exploration of Open-Source Issues for Predicting Privacy Compliance
AU - Guber, Jenny
AU - Reinhartz-Berger, Iris
AU - Litvak, Marina
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - In the last decade, privacy has gained a significant interest in software and information systems engineering mainly due to the emergence of privacy regulations, including the General Data Protection Regulation (GDPR). However, checking privacy compliance is challenging and depends on many factors, such as the programming language and the software architecture, as well as the underlying regulation. In this exploratory research, we aim to study whether positive discussions on privacy-related issues in Open-Source Software (OSS) environments can predict privacy compliance of the software. Such predictions are beneficial in different scenarios, including in software reuse. Our main contribution will lie in conceptually modeling and understanding the relations between privacy compliance and positive discussions of privacy-related OSS issues. The research comprises three parts: (1) identifying privacy-related issues using supervised machine learning techniques; (2) improving the identification of privacy-related issues utilizing ontologies; and (3) identifying the sentiment of privacy-related issues and analyzing relations to privacy compliance. This paper describes the design and results of part 1, as well as the design of parts 2 and 3.
AB - In the last decade, privacy has gained a significant interest in software and information systems engineering mainly due to the emergence of privacy regulations, including the General Data Protection Regulation (GDPR). However, checking privacy compliance is challenging and depends on many factors, such as the programming language and the software architecture, as well as the underlying regulation. In this exploratory research, we aim to study whether positive discussions on privacy-related issues in Open-Source Software (OSS) environments can predict privacy compliance of the software. Such predictions are beneficial in different scenarios, including in software reuse. Our main contribution will lie in conceptually modeling and understanding the relations between privacy compliance and positive discussions of privacy-related OSS issues. The research comprises three parts: (1) identifying privacy-related issues using supervised machine learning techniques; (2) improving the identification of privacy-related issues utilizing ontologies; and (3) identifying the sentiment of privacy-related issues and analyzing relations to privacy compliance. This paper describes the design and results of part 1, as well as the design of parts 2 and 3.
KW - Open Source
KW - Privacy
KW - Software Development
KW - Software Reuse
UR - http://www.scopus.com/inward/record.url?scp=85177183812&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-47112-4_6
DO - 10.1007/978-3-031-47112-4_6
M3 - Conference contribution
AN - SCOPUS:85177183812
SN - 9783031471117
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 63
EP - 73
BT - Advances in Conceptual Modeling - ER 2023 Workshops, CMLS, CMOMM4FAIR, EmpER, JUSMOD, OntoCom, QUAMES, and SmartFood, Proceedings
A2 - Sales, Tiago Prince
A2 - Guizzardi, Giancarlo
A2 - Araújo, João
A2 - Borbinha, José
PB - Springer Science and Business Media Deutschland GmbH
T2 - 42nd International Conference on Conceptual Modeling, ER 2023
Y2 - 6 November 2023 through 9 November 2023
ER -