Current day software development relies heavily on the use of service architectures and on agile iterative development methods to design, implement, and deploy systems. These practices result in systems made up of multiple services that introduce new data flows and evolving designs that escape the control of a single designer. Academic privacy engineering literature typically abstracts away such conditions of software production in order to achieve generalizable results. Yet, through a systematic study of the literature, we show that proposed solutions inevitably make assumptions about software architectures, development methods and scope of designer control that are misaligned with current practices. These misalignments are likely to pose an obstacle to operationalizing privacy engineering solutions in the wild. Specifically, we identify important limitations in the approaches that researchers take to design and evaluate privacy enhancing technologies which ripple to proposals for privacy engineering methodologies. Based on our analysis, we delineate research and actions needed to re-align research with practice, changes that serve a precondition for the operationalization of academic privacy results in common software engineering practices.
Nowadays, most companies need to collect, store, and manage personal information in order to deliver their services. Accordingly, privacy has emerged as a key concern for these companies since they need to comply with privacy laws and regulations. To deal with them properly, such privacy concerns should be considered since the early phases of system design. Ontologies have proven to be a key factor for elaborating high-quality requirements models. However, most existing work deals with privacy as a special case of security requirements, thereby missing essential traits of this family of requir…
Given the relevance of coordination in the field of global software engineering, this work was carried out to further understand coordination mechanisms. Specifically, we investigated meetings and the collaboration tool Slack. We conducted a longitudinal case study using a mixed-methods approach with surveys, observations, interviews, and chat logs. Our quantitative results show that employees in global projects spend 7 hours 45 minutes per week on average in scheduled meetings and 8 hours 54 minutes in unscheduled meetings. Furthermore, distributed teams were significantly larger than co-loca…
Privacy has been frequently identified as a main concern for system developers while dealing with/managing personal information. Despite this, most existing work on privacy requirements deals with them as a special case of security requirements. Therefore, key aspects of privacy are, usually, overlooked. In this context, wrong design decisions might be made due to insufficient understanding of privacy concerns. In this paper, we address this problem with a systematic literature review whose main purpose is to identify the main concepts/relations for capturing privacy requirements. In addition,…
Models are fundamentally crucial to many scientific fields, including software engineering, systems engineering, enterprise modeling, and business modeling. This paper focuses on diagrammatic conceptual modeling, as opposed to mathematical or computational models, wherein a conceptual model is a translation of reality processes into an abstract mechanism that has similar structure and parallel events of the external processes. Although various modeling approaches exist, including UML (Unified Modeling Language) in software engineering and its dialect, SysML (System Modeling Language), in syste…
Cloud native information systems engineering enables scalable and resilient service infrastructures for all major online offerings. These are built following agile development practices. At the same time, a growing demand for privacy-friendly services is articulated by societal norms and policy through effective legislative frameworks. In this paper, we identify the conceptual dimensions of cloud native privacy engineering and propose an integrative approach to be addressed in practice to overcome the shortcomings of existing privacy enhancing technologies. Furthermore, we propose a reference …
Recent years have seen the successful application of deep learning to software engineering (SE). In particular, the development and use of pre-trained models of source code has enabled state-of-the-art results to be achieved on a wide variety of SE tasks. This paper provides an overview of this rapidly advancing field of research and reflects on future research directions.
Supporting the Adoption of Privacy-Enhancing Technologies through Requirements Engineering
Blekinge Institute of Technology（布莱金厄理工学院）；fortiss GmbH（fortiss有限公司）；Google（谷歌）；MAYA Data Privacy Limited（MAYA数据隐私有限公司）；adidas AG（阿迪达斯股份公司）；JAGGAER LLC（JAGGAER有限责任公司）
Mobile applications (apps) have become deeply personal, constantly demanding access to privacy-sensitive information in exchange for more personalized user experiences. Such privacy-invading practices have generated major multidimensional and unconventional privacy concerns among app users. To address these concerns, the research on mobile app privacy has experienced rapid growth over the past decade. In general, this line of research is aimed at systematically exposing the privacy practices of apps and proposing solutions to protect the privacy of mobile app users. In this survey paper, we co…
In the field of microservices, Model-Driven Engineering has emerged as a powerful methodology for architectural design, and new programming languages have introduced language abstractions to deal with microservice development more effectively. In this article, we present the first preliminary investigation of how the two approaches can be married, taking the LEMMA framework and the Jolie programming language as respective representatives. By developing a conceptual metamodel for Jolie, we elicit a strong link between the two approaches, which shows that there is much to gain. We discuss a few …
In Europe and indeed worldwide, the General Data Protection Regulation (GDPR) provides protection to individuals regarding their personal data in the face of new technological developments. GDPR is widely viewed as the benchmark for data protection and privacy regulations that harmonizes data privacy laws across Europe. Although the GDPR is highly beneficial to individuals, it presents significant challenges for organizations monitoring or storing personal information. Since there is currently no automated solution with broad industrial applicability, organizations have no choice but to carry …
Federal Institute of the Southeast of Minas Gerais (IF Sudeste MG)（米纳斯吉拉斯东南联邦学院）；Federal Institute of Minas Gerais (IFMG)（米纳斯吉拉斯联邦学院）；Federal University of Minas Gerais (UFMG)（米纳斯吉拉斯联邦大学）
Deep Learning (DL) is being used nowadays in many traditional Software Engineering (SE) problems and tasks. However, since the renaissance of DL techniques is still very recent, we lack works that summarize and condense the most recent and relevant research conducted at the intersection of DL and SE. Therefore, in this paper, we describe the first results of a mapping study covering 81 papers about DL & SE. Our results confirm that DL is gaining momentum among SE researchers over the years and that the top-3 research problems tackled by the analyzed papers are documentation, defect prediction,…
Privacy-preservation for sensitive data has become a challenging issue in cloud computing. Threat modeling as a part of requirements engineering in secure software development provides a structured approach for identifying attacks and proposing countermeasures against the exploitation of vulnerabilities in a system . This paper describes an extension of Cloud Privacy Threat Modeling (CPTM) methodology for privacy threat modeling in relation to processing sensitive data in cloud computing environments. It describes the modeling methodology that involved applying Method Engineering to specify ch…
The design and development process for Internet of Things (IoT) applications is more complicated than for desktop, mobile, or web applications. IoT applications require both software and hardware to work together across multiple different types of nodes (e.g., microcontrollers, system-on-chips, mobile phones, miniaturised single-board computers, and cloud platforms) with different capabilities under different conditions. IoT applications typically collect and analyse personal data that can be used to derive sensitive information about individuals. Without proper privacy protections in place, I…
Software engineering educators are continually challenged by rapidly evolving concepts, technologies, and industry demands. Due to the omnipresence of software in a digitalized society, higher education institutions (HEIs) have to educate the students such that they learn how to learn, and that they are equipped with a profound basic knowledge and with latest knowledge about modern software and system development. Since industry demands change constantly, HEIs are challenged in meeting such current and future demands in a timely manner. This paper analyzes the current state of practice in soft…
Abstraction applied in computer networking hides network details behind a well-defined representation by building a model that captures an essential aspect of the network system. Two current methods of representation are available, one based on graph theory, where a network node is reduced to a point in a graph, and the other the use of non-methodological iconic depictions such as human heads, walls, towers or computer racks. In this paper, we adopt an abstract representation methodology, the thinging machine (TM), proposed in software engineering to model computer networks. TM defines a singl…
Autism Spectrum Disorder (ASD) is neurodevelopmental condition characterized by social interaction and communication difficulties, along with narrow and repetitive interests. Being an spectrum disorder, ASD affects individuals with a large range of combinations of challenges along dimensions such intelligence, social skills, or sensory processing. Hence, any computer-based technology for ASD ought to be personalized to meet the particular profile and needs of each person that uses it. Within the realm of Software Engineering, there is an extensive body of research and practice on software cust…
When Prompt Engineering Meets Software Engineering: CNL-P as Natural and Robust "APIs'' for Human-AI Interaction
CSIRO’s Data61（CSIRO数据61实验室）；Jiangxi Provincial Key Laboratory for High Performance Computing（江西省高性能计算重点实验室）；State International Science & Technology Cooperation Base of Networked Supporting Software（网络化支持软件国际科技合作基地）；Language Intelligence Research Center（语言智能研究中心）；Jiangxi Normal University（江西师范大学）；Institute of AI, Newcastle University, UK（人工智能研究院，新castle大学，英国）；UGAiForge
In this paper, we present TILT, a transparency information language and toolkit explicitly designed to represent and process transparency information in line with the requirements of the GDPR and allowing for a more automated and adaptive use of such information than established, legalese data protection policies do. We provide a detailed analysis of transparency obligations from the GDPR to identify the expressiveness required for a formal transparency language intended to meet respective legal requirements. In addition, we identify a set of further, non-functional requirements that need to b…
To provide privacy-aware software systems, it is crucial to consider privacy from the very beginning of the development. However, developers do not have the expertise and the knowledge required to embed the legal and social requirements for data protection into software systems. Objective: We present an approach to decrease privacy risks during agile software development by automatically detecting privacy-related information in the context of user story requirements, a prominent notation in agile Requirement Engineering (RE). Methods: The proposed approach combines Natural Language Processing …
Property-Driven Synthetic Data Engineering for Data-Scarce Software Systems: Reflections from the Breast Cancer Domain
University of Bergamo（博洛尼亚大学）；University of Toronto（多伦多大学）；Humanitas Clinical and Research Center, IRCCS（人类itas临床与研究中心）；ASST Papa Giovanni XXIII（ASST圣若望二十三世医院）
