Empirical Exploration of Open-Source Issues for Predicting Privacy Compliance

Jenny Guber, Iris Reinhartz-Berger, Marina Litvak

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationAdvances in Conceptual Modeling - ER 2023 Workshops, CMLS, CMOMM4FAIR, EmpER, JUSMOD, OntoCom, QUAMES, and SmartFood, Proceedings
EditorsTiago Prince Sales, Giancarlo Guizzardi, João Araújo, José Borbinha
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783031471117
StatePublished - 2023
Event42nd International Conference on Conceptual Modeling, ER 2023 - Lisbon, Portugal
Duration: 6 Nov 20239 Nov 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14319 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference42nd International Conference on Conceptual Modeling, ER 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • Open Source
  • Privacy
  • Software Development
  • Software Reuse

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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