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REVIEW 3 major objections 6 minor 107 references

Engineering Digital Systems for Humanity: a Research Roadmap

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper argues that software engineering should treat human, societal, and environmental values as first-class drivers and offers a 14-direction research roadmap derived from the roles humans play with digital systems.

desk verdict A solid, well-structured SE roadmap that honestly owns its limits, but the environmental pillar is thin where the title promises a third of the scope. read the letter →

arxiv 2412.19668 v1 pith:PF72RPF5 submitted 2024-12-27 cs.SE cs.CYcs.HC

Marco Autili , Martina De Sanctis , Paola Inverardi , Patrizio Pelliccione This is my paper · ORCID
classification cs.SEcs.CYcs.HC
keywords humanvaluessocietalenvironmentalresearchroadmapsoftwareengineeringrolestrustworthinessdigitalsystemsforhumanity
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Software systems are usually built to satisfy business goals and follow technology drivers; the paper argues that this is no longer enough. Because digital systems now mediate jobs, lending, care, and public life, engineering must treat human, societal, and environmental (HSE) values as first-class drivers. To make that concrete, the paper organizes the problem around four human-system challenges: humans as proactive programmers of systems, humans reacting to system events, humans passively affected by system decisions, and the cross-cutting pair of trust and trustworthiness. From six HSE drivers and these four challenges it derives a roadmap of 14 research directions in four engineering areas: development process, requirements engineering, software architecture and design, and verification and validation. The point of the roadmap is to give software engineers a concrete agenda for making their work accountable to the people and societies it serves.

What carries the argument

The organizing device is a taxonomy of human roles, defined by whether the human initiates, reacts to, or is affected by the system: proactive roles need accessible continuous programming languages and monitoring, reactive roles need ethical interaction and adjustable autonomy, and passive roles need fairness, transparency, and new quality standards. The fourth component, trust versus trustworthiness, separates the human's subjective acceptance from the system's objective safe-and-secure design. The paper also uses explicit two-step mappings—HSE drivers to challenges, and challenges to research directions—so that every research direction is traceable back to a driver.

What would settle it

An empirical study that documents a mode of human-system coexistence that none of the proactive, reactive, or passive roles can describe, or an expert elicitation that surfaces a stakeholder need not captured by the six HSE drivers, would falsify the claimed coverage. The paper's own external-validity concession marks this as the point most likely to fail.

Watch

Extended reading notes

Core claim

The paper's central claim is that human, societal, and environmental values belong in the engineering loop as requirements, not as afterthoughts or external constraints. It identifies six HSE drivers—societal and environmental well-being, accountability, privacy and data governance, human agency and oversight, transparency and explainability, and diversity, non-discrimination, and fairness—and maps them to four macro challenges. Each challenge corresponds to a human role in coexisting with digital systems: continuous systems programming for the proactive human, human-system interaction for the reactive human, digital-systems impact for the passive human, and trust versus trustworthiness as a transversal challenge. The roadmap then translates these challenges into 14 research directions, including seamless development-execution processes, runtime negotiation of HSE requirements, new architecture tactics, and field-based verification and continuous compliance.

Load-bearing premise

The roadmap's value depends on the assumption that the three human roles plus trust/trustworthiness cover the whole space of human coexistence with digital systems, and that the chosen HSE drivers are the right ones; the paper explicitly says it cannot claim these sets are complete.

Editorial extensions

If this is right

  • Requirements engineering must treat qualities such as fairness, accountability, and transparency as first-class, possibly re-opening existing quality models like ISO/IEC 25010.
  • Systems should support continuous programming by non-expert users after deployment, with equally continuous monitoring, assessment, and compliance.
  • Design-time tradeoffs among values give way to runtime negotiation, because HSE profiles are subjective, evolving, and can conflict among the humans sharing a system.
  • Verification and validation must move into the field and cover the whole lifecycle, since autonomy, adaptation, and post-market updates defeat design-time-only assurance.
  • Software engineering research and universities may take on governance roles, producing frameworks that protect humans rather than only optimizing technology.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper leaves the 14 research directions unprioritized; a natural next step would be to map dependencies among them, for instance runtime negotiation presupposes elicitation and specification of HSE requirements.
  • The HSE-debt metaphor could be made operational by borrowing technical-debt measurement ideas, but that would require defining metrics for the cost of not addressing values, which the paper does not supply.
  • The quality-label analogy for measuring trust-related qualities could eventually support standardized value labels for AI services, but the paper only hints at such a scheme.
  • The role taxonomy could be tested empirically on current AI assistants: a user who neither initiates, responds, nor is merely affected—but co-constructs behavior through implicit signals—would strain the taxonomy and reveal whether a fourth role is needed.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This paper proposes a research roadmap for engineering digital systems for humanity. It argues that software engineering should consider human, societal, and environmental (HSE) drivers in addition to business and technology drivers. It identifies four macro-challenges based on human roles (proactive, reactive, passive, and a transversal trust/trustworthiness challenge) and derives 14 research directions organized into four groups: development process, requirements engineering, software architecture and design, and verification and validation. The roadmap is constructed using a design science methodology with three iterations, including literature review, a workshop at FSE 2024, and validation in three of the authors' research projects. The paper includes a threats-to-validity section acknowledging the incompleteness of the proposed drivers, challenges, and directions.

Significance. The paper addresses a timely and important problem: how to bring HSE values into software engineering practice, motivated by regulations such as the EU AI Act. Its main strengths are the explicit design science methodology, the clear structure of challenges and research directions, and the grounding in concrete projects (HALO, Robochor, EXOSOUL) and in the SE2030 workshop report. The roadmap has the potential to influence research agendas in responsible AI and human-centered software engineering. However, its significance is currently limited by the asymmetry between the human/societal and environmental pillars: environmental values are declared as a driver but are not developed into concrete research directions, which weakens the claim of covering HSE values comprehensively.

major comments (3)
  1. [Section 5, RD1.1–RD4.3; Section 3.3, D1] The roadmap does not concretely address the environmental pillar of the HSE drivers. Although D1 defines 'Societal and environmental well-being' and several challenges are tagged as 'relevant for all values' (CH1.2, CH2.2, CH3.1, CH4.2, CH4.3), none of the 14 research directions in Section 5 proposes concrete work on environmental sustainability, such as energy efficiency, carbon footprint, resource consumption, e-waste, or climate impact of digital systems. The only environmental-specific element is the high-level HSE-debt metaphor in RD3.4. Since the paper's title and abstract claim a roadmap for 'human, societal, and environmental' values, this asymmetry is a load-bearing gap rather than a mere completeness limitation. The authors should either add research directions that operationalize the environmental driver, or explicitly state that environmental values are treated as cross-cutting and illustrate how each direction would be instantiated for environmental concerns.
  2. [Section 5.2, RD2.3] The runtime-negotiation direction rests on the unstated assumption that 'HSE requirements are graduable' (Section 5.2, RD2.3). This assumption is load-bearing because the proposed shift from design-time tradeoffs to runtime negotiation requires that values can be relaxed or downgraded, and the paper does not discuss which values admit degrees of satisfaction or how to handle non-negotiable values (e.g., human dignity, safety). The authors should either justify the graduability assumption, or delimit the class of HSE requirements to which runtime negotiation applies.
  3. [Section 2, Methodology; Figure 4] The mapping between challenges and research directions is asserted rather than derived. The paper states that the roadmap was built 'on' the drivers and challenges (Section 5) and presents the mapping in Figure 4, but the text does not explain the criteria for associating a challenge with a direction, nor is there an evaluation of the mapping's completeness or redundancy. The validation is carried out within three projects involving all co-authors and reuses several of the authors' prior frameworks (EXOSOUL, SLEEC compilation, specification patterns). This self-supporting structure, acknowledged in the external-validity paragraph of Section 2, leaves the central claim of the roadmap largely dependent on the authors' own judgment. I would expect at least a more systematic derivation of the mapping or an external validation step to strengthen the roadmap's credibility.
minor comments (6)
  1. [Section 1, first paragraph] There are apparent typographical errors: 'environemnt' should be 'environment' and 'reseach' should be 'research'.
  2. [Section 4.1, CH1.1] 'Easy of use' should be 'Ease of use'.
  3. [Section 5.1, RD1.2] The term 'Seamless DevExe' is used without definition; consider introducing it before using it.
  4. [Section 5.4, RD4.3] 'Bruxelles effect' is a misspelling of 'Brussels effect'.
  5. [Figures 3 and 4] The mappings between drivers, challenges, and research directions are presented only as figures; a table or a more detailed verbal explanation would improve accessibility and traceability.
  6. [Section 5.2, RD2.2] The description of SLEEC rules and their translation to formal languages could be clarified with a concrete example or a workflow diagram.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the roadmap is assembled from external guidelines, regulations, literature, and workshop input; the authors' self-citations are intellectual lineage rather than load-bearing reductions.

full rationale

This is a qualitative research-roadmap article. There are no equations, fitted parameters, or predicted quantities whose derivation could reduce to its own inputs, so the classic circularity failure modes (self-definitional predictions, fitted inputs called predictions, renaming known results) do not apply. The central chain—HSE drivers, macro-challenges, 14 research directions, and the driver-challenge-direction mappings—is built from a literature review, laws and regulations (GDPR, AI Act), institutional guidelines (UNESCO, IEEE, EU, OECD), and the SE2030 workshop report [98], which is not authored by this paper's authors. The main self-citation is [100], a prior paper by three of the four authors, used as the source of the six HSE drivers; however, the paper states that the drivers were retrieved in [100] via a literature review including UNESCO [117], IEEE [2], EU [58], OECD [93], and US government [119] guidance, and the current paper re-presents the drivers with those external anchors. Thus the drivers are not defined in terms of the roadmap, and the roadmap is not used as evidence for the drivers. The validation of the roadmap inside the authors' own projects (HALO, Robochor, EXOSOUL) is a self-involvement threat to external validity, but it is disclosed as such and functions as a plausibility testbed, not as the generative source of the roadmap's content. The paper also explicitly concedes in Section 2: 'we cannot claim that the HSE drivers, challenges, and research directions are complete,' which addresses the principal legitimate limitation. The skeptic's observation that the environmental pillar (D1) is only thinly operationalized in RD1.1–RD4.3 is a scope-consistency concern, not a circularity: no research direction is secretly identical to an input, and the paper does not claim that every value is equally developed in every direction. Accordingly, no specific circular step can be exhibited, and the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 4 assumptions · 1 invented entities

The roadmap rests on domain assumptions about the completeness of the role taxonomy and the HSE driver set, both of which the authors acknowledge may be incomplete. No free parameters are fitted; the only invented entity is the conceptual HSE debt metaphor, which has no independent empirical anchor.

assumptions (4)
  • domain assumption The tripartite taxonomy of human roles (proactive, reactive, passive) is adequate to structure all human-system coexistence challenges.
    Section 3.2 introduces the three roles as categories and uses them to define CH1-CH3; if a relevant role is missing, the roadmap is incomplete.
  • domain assumption The HSE drivers elicited in the authors' earlier work (De Sanctis et al., QUATIC 2024 [100]) are the relevant drivers for engineering digital systems for humanity.
    Section 3.3 lists six drivers D1-D6 as the basis for challenges and roadmap; the paper does not re-elicit or independently validate them.
  • domain assumption Design science with three iterations, internal author validation, and the SE2030 workshop yields a valid roadmap.
    Section 2 describes the methodology; the validity of the output depends on this method being appropriate for roadmap construction.
  • ad hoc to paper HSE requirements are graduable, so runtime negotiation can relax or downgrade them.
    RD2.3 in Section 5.2 assumes requirements can be satisfied to a degree; without this, the proposed runtime negotiation direction loses its basis.
invented entities (1)
  • Human, societal, and environmental debt (HSE debt)
    purpose: Metaphor for the cost of not satisfactorily addressing HSE values during system engineering; proposed to motivate investment and continuous monitoring.
    Introduced in RD3.4 (Section 5.3) as an extension of technical debt and social debt; no measurable definition or falsifiable handle is given.

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Cite this review

Pith. "Pith review of Engineering Digital Systems for Humanity: a Research Roadmap." pith.science (2026). https://pith.science/paper/PF72RPF5

@misc{pith2026241219668,
  author       = {Pith},
  title        = {Pith review of: Engineering Digital Systems for Humanity: a Research Roadmap},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PF72RPF5}},
  note         = {Machine review of arXiv:2412.19668}
}
read the original abstract

As testified by new regulations like the European AI Act, worries about the human and societal impact of (autonomous) software technologies are becoming of public concern. Human, societal, and environmental values, alongside traditional software quality, are increasingly recognized as essential for sustainability and long-term well-being. Traditionally, systems are engineered taking into account business goals and technology drivers. Considering the growing awareness in the community, in this paper, we argue that engineering of systems should also consider human, societal, and environmental drivers. Then, we identify the macro and technological challenges by focusing on humans and their role while co-existing with digital systems. The first challenge considers humans in a proactive role when interacting with digital systems, i.e., taking initiative in making things happen instead of reacting to events. The second concerns humans having a reactive role in interacting with digital systems, i.e., humans interacting with digital systems as a reaction to events. The third challenge focuses on humans with a passive role, i.e., they experience, enjoy or even suffer the decisions and/or actions of digital systems. The fourth challenge concerns the duality of trust and trustworthiness, with humans playing any role. Building on the new human, societal, and environmental drivers and the macro and technological challenges, we identify a research roadmap of digital systems for humanity. The research roadmap is concretized in a number of research directions organized into four groups: development process, requirements engineering, software architecture and design, and verification and validation.

Figures

Figures reproduced from arXiv: 2412.19668 by the authors.

Figure 1
Figure 1. Research methodology • Needs of the stakeholders discussed in [100]; • A literature review and analyzed scienti!c papers in the !eld; • Guidelines and recommendations, including guidelines from institutional bodies (e.g., UN￾ESCO [117]), AI ethics frameworks (e.g., the Institute of Electrical and Electronics Engineers (IEEE) AI Ethics Framework [2], the European Union (EU) Ethics Guidelines for Trustwor￾thy AI [58],… view at source ↗
Figure 2
Figure 2. Main Challenges for Engineering Digital Systems for Humanity [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 3
Figure 3. Mapping between HSE Drivers and Challenges [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Mapping between HSE Challenges and Research Directions [PITH_FULL_IMAGE:figures/full_fig_p019_4.png]

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Reference graph

Works this paper leans on

107 extracted references · 37 canonical work pages

  1. [1]

    De!nition of Technology Driver

    [1]2020. De!nition of Technology Driver. https://www.chemicool.com/de !nition/technology_driver.html . [Online; accessed 08-November-2020]. [2]Institute of Electrical and Electronics Engineers (IEEE). 2019 - version

    2020

  2. [4]

    In Hybrid Human Arti"cial Intelligence (HHAI) 2024

    In Search of Clarity: Discerning Between Human Replacement and Augmentation. In Hybrid Human Arti"cial Intelligence (HHAI) 2024 . [6]Costanza Al!eri, Paola Inverardi, Patrizio Migliarini, and Massimiliano Palmiero

    2024

  3. [10]

    Nature563, 7729 (2018), 59–64

    The Moral Machine Experiment. Nature563, 7729 (2018), 59–64. https://www.nature.com/ articles/s41586-018-0637-6 [12]Maria Teresa Baldassarre, Domenico Gigante, Marcos Kalinowski, and Azzurra Ragone

    2018

  4. [11]

    In Proceedings of the 3rd International Conference on AI Engineering: Software Engineering for AI, CAIN 2024

    POLARIS: A framework to guide the development of Trustworthy AI systems. In Proceedings of the 3rd International Conference on AI Engineering: Software Engineering for AI, CAIN 2024 . IEEE/ACM. [13]Barbara Rita Barricelli, Fabio Cassano, Daniela Fogli, and Antonio Piccinno

    2024

  5. [12]

    Journal of Systems and Software 149 (2019), 101–137

    End-user development, end-user programming and end-user software engineering: A systematic mapping study. Journal of Systems and Software 149 (2019), 101–137. https://doi.org/10.1016/j.jss.2018.11.041 [14]Ezio Bartocci, Yliès Falcone, Adrian Francalanza, and Giles Reger

    work page doi:10.1016/j.jss.2018.11.041 2019

  6. [13]

    Springer International Publishing, Cham, 1–33

    Introduction to Runtime Veri "cation. Springer International Publishing, Cham, 1–33. https://doi.org/10.1007/978-3-319-75632-5_1 [15]Richard Baskerville, Abayomi Baiyere, et al .2018. Design science research contributions: Finding a balance between artifact and theory. Journal of the Association for Information Systems 19, 5 (2018). [16]Len Bass, Paul Cle...

    doi:10.1007/978-3-319-75632-5_1 2018

  7. [14]

    In 37th IEEE/ACM International Conference on Software Engineering, ICSE 2015, Florence, Italy, May 16-24, 2015, Volume 2 , Antonia Bertolino, Gerardo Canfora, and Sebastian G

    Sustainability Design and Software: The Karlskrona Manifesto. In 37th IEEE/ACM International Conference on Software Engineering, ICSE 2015, Florence, Italy, May 16-24, 2015, Volume 2 , Antonia Bertolino, Gerardo Canfora, and Sebastian G. Elbaum (Eds.). IEEE Computer Society, 467–476. https://doi.org/10.1109/ICSE.2015.179 [18]Amel Bennaceur, Diane Hassett,...

    work page doi:10.1109/icse.2015.179 2015

  8. [16]

    https://doi.org/10.1007/S10515-024-00415-2 [100]Martina De Sanctis, Paola Inverardi, and Patrizio Pelliccione

    work page doi:10.1007/s10515-024-00415-2

Show all 107 references
  1. [17]

    In 19th International Conference on Software Engineering for Adaptive and Self-Managing Systems (SEAMS@ICSE) 2024

    Human empowerment in self-adaptive socio-technical systems. In 19th International Conference on Software Engineering for Adaptive and Self-Managing Systems (SEAMS@ICSE) 2024 . To appear. [23]Anu Bradford

    2024

  2. [20]

    In 2021 IEEE 18th International Conference on Software Architecture (ICSA)

    Aligning Architecture with Business Goals in the Automotive Domain. In 2021 IEEE 18th International Conference on Software Architecture (ICSA) . 126–137. https://doi.org/10.1109/ICSA51549.2021.00020 [27]Ricardo Caldas, Juan Antonio Piñera García, Matei Schiopu, Patrizio Pellic...

    2021

  3. [21]

    IEEE Transactions on Software Engineering (2024), 1–24

    Runtime Veri !cation and Field-based Testing for ROS-based Robotic Systems. IEEE Transactions on Software Engineering (2024), 1–24. https://doi.org/10.1109/TSE.2024.3444697 [28]Radu Calinescu, Ra"aela Mirandola, Diego Perez-Palacin, and Danny Weyns

    2024

  4. [23]

    Bias in machine learning software: why? how? what to do?. In ESEC/FSE ’21: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , Diomidis Spinellis, Georgios Gousios, Marsha Chechik, and Massimiliano Di Penta (Eds.)....

  5. [25]

    AI & SOCIETY 38 (05 2022), 1–13

    Trust and ethics in AI. AI & SOCIETY 38 (05 2022), 1–13. https://doi.org/10.1007/s00146-022-01473-4 [33]Jane Cleland-Huang, Theodore Chambers, Sebastián Zudaire, Muhammed Taw !q Chowdhury, Ankit Agrawal, and Michael Vierhauser

    2022 doi

  6. [26]

    ACM Trans

    Human-machine Teaming with Small Unmanned Aerial Systems in a MAPE-K Environment. ACM Trans. Auton. Adapt. Syst. 19, 1 (2024), 3:1–3:35. https://doi.org/10.1145/3618001 [34]Paul Clements and Len Bass

    2024 doi

  7. [28]

    The Journal of Financial Data Science 3, 4 (2021), 33–64

    Fairness Measures for Machine Learning in Finance. The Journal of Financial Data Science 3, 4 (2021), 33–64. https://doi.org/10.3905/jfds.2021.1.075 arXiv:https://jfds.pm- research.com/content/3/4/33.full.pdf [37]Louise A. Dennis, Martin Mose Bentzen, Felix Lindner, and Michael Fisher

    2021 doi

  8. [29]

    Proceedings of the AAAI Conference on Arti "cial Intelligence 35, 13 (May 2021), 11470–11478

    Veri !able Machine Ethics in Changing Contexts. Proceedings of the AAAI Conference on Arti "cial Intelligence 35, 13 (May 2021), 11470–11478. https://doi.org/10.1609/aaai.v35i13.17366 [38]Swaib Dragule, Thorsten Berger, Claudio Menghi, and Patrizio Pelliccione

    2021 doi

  9. [30]

    A survey on the design space of end-user-oriented languages for specifying robotic missions. Softw. Syst. Model. 20, 4 (aug 2021), 1123–1158. https://doi.org/10.1007/s10270-020-00854-x [39]Swaib Dragule, Sergio García Gonzalo, Thorsten Berger, and Patrizio Pelliccione

    2021 doi

  10. [31]

    Springer International Publishing, Cham, 377–411

    Languages for Specifying Missions of Robotic Applications . Springer International Publishing, Cham, 377–411. https://doi.org/10.1007/978-3- 030-66494-7_12 [40]Bastien Durand, Karen Godary-Dejean, Lionel Lapierre, and Didier Crestani

    doi

  11. [33]

    Robotics and Autonomous Systems 116 (2019), 162–180

    Cobot programming for collaborative industrial tasks: An overview. Robotics and Autonomous Systems 116 (2019), 162–180. [43]Neil Ernst, Julien Delange, and Rick Kazman

    2019

  12. [34]

    Available at:https://bit.ly/2R8siwl

    Europe 2020 A European strategy for smart, sustainable and inclusive growth. Available at:https://bit.ly/2R8siwl . [45]European Commission, Directorate-General for Research and Innovation, Breque, M., De Nul, L., Petridis

    2020

  13. [35]

    [47]Yliès Falcone, Sr%an Krsti&, et al.2021

    The ethics guidelines for trustworthy arti !cial intelligence. [47]Yliès Falcone, Sr%an Krsti&, et al.2021. A taxonomy for classifying runtime veri !cation tools.International Journal on Software Tools for Technology Transfer 23, 2 (2021). [48]Nick Feng, Lina Marsso, Sinem Get...

    2021

  14. [36]

    Association for Computing Machinery, New York, NY, USA, Article 214, 12 pages

    Analyzing and Debugging Normative Requirements via Satis !ability Checking(ICSE ’24). Association for Computing Machinery, New York, NY, USA, Article 214, 12 pages. https: //doi.org/10.1145/3597503.3639093 [49]Luciano Floridi

  15. [37]

    Philosophy & Technology 31, 1 (2018)

    Soft ethics and the governance of the digital. Philosophy & Technology 31, 1 (2018). [50]Sergio García, Daniel Strüber, Davide Brugali, Alessandro Di Fava, Patrizio Pelliccione, and Thorsten Berger

    2018

  16. [38]

    Empirical Softw

    Software variability in service robotics. Empirical Softw. Engg. 28, 2 (Dec. 2022), 67 pages. https://doi.org/10.1007/ s10664-022-10231-5 [51]Gillespie, Nicole, Lockey, Steven, Curtis, Caitlin, Pool, Javad, and Ali Akbari

    2022

  17. [39]

    The University of Queensland; KPMG Australia, Available at: https://doi.org/10.14264/00d3c94

    Trust in Arti !cial Intelligence: A global study. The University of Queensland; KPMG Australia, Available at: https://doi.org/10.14264/00d3c94 . [52]Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi

    doi

  18. [40]

    ACM Comput

    A Survey of Methods for Explaining Black Box Models. ACM Comput. Surv. 51, 5 (2019), 93:1–93:42. https: //doi.org/10.1145/3236009 [53]Rogardt Heldal, Ngoc-Thanh Nguyen, Ana Moreira, Patricia Lago, Leticia Duboc, Stefanie Betz, Vlad C. Coroama, Birgit Penzenstadler, Jari Porras...

    2019 doi

  19. [41]

    Sustainability competencies and skills in software engineering: An industry perspective. J. Syst. Softw.211 (2024), 111978. https: , Vol. 1, No. 1, Article . Publication date: December

    2024

  20. [42]

    Henderson and H

    Engineering Digital Systems for Humanity: a Research Roadmap 31 //doi.org/10.1016/J.JSS.2024.111978 [54]John C. Henderson and H. Venkatraman

    2024

  21. [44]

    https://doi.org/10.1016/j.chbah.2024

    Trust in arti !cial intelligence: Literature review and main path analysis.Computers in Human Behavior: Arti "cial Humans2, 1 (2024), 100043. https://doi.org/10.1016/j.chbah.2024. 100043 [56]Katharine Henry, Rachel Korn !eld, Anirudh Sridharan, Robert C. Linton, Catherine Groh...

    2024 doi

  22. [45]

    npj Digit

    Human-machine teaming is key to AI adoption: clinicians’ experiences with a deployed machine learning system. npj Digit. Medicine 5 (2022). https://doi.org/10.1038/S41746-022-00597-7 [57]Alan R. Hevner and Veda C. Storey

    2022 doi

  23. [46]

    Externalities of Design Science Research: Preparation for Project Success . Vol. 12807 LNCS. Springer International Publishing. [58]High-Level Expert Group on AI. 2019, last update 31 January

    2019

  24. [47]

    In2021 IEEE International Conference on Robotics and Automation (ICRA)

    World-in-the-Loop Simulation for Autonomous Systems Validation. In2021 IEEE International Conference on Robotics and Automation (ICRA) . 10912–10919. https://doi.org/10.1109/ ICRA48506.2021.9561240 [60]Carl Hildebrandt, Meriel von Stein, and Sebastian Elbaum

    2021

  25. [48]

    InProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (Seattle, W A, USA)(ISSTA 2023)

    PhysCov: Physical Test Coverage for Autonomous Vehicles. InProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (Seattle, W A, USA)(ISSTA 2023). Association for Computing Machinery, New York, NY, USA, 449–461. https://doi.org/10. 1145/35...

    2023

  26. [49]

    https://doi.org/10.48550/ARXIV.2207.07068 [62]Sihan Huang, Baicun Wang, Xingyu Li, Pai Zheng, Dimitris Mourtzis, and Lihui Wang

    Bias Mitigation for Machine Learning Classi!ers: A Comprehensive Survey. https://doi.org/10.48550/ARXIV.2207.07068 [62]Sihan Huang, Baicun Wang, Xingyu Li, Pai Zheng, Dimitris Mourtzis, and Lihui Wang

    doi arXiv

  27. [50]

    Journal of Manufacturing Systems 64 (2022), 424–428

    Industry 5.0 and Society 5.0—Comparison, complementation and co-evolution. Journal of Manufacturing Systems 64 (2022), 424–428. https://doi.org/10.1016/j.jmsy.2022.07.010 [63]Prabu David Hyesun Choung and Arun Ross

    2022 doi

  28. [51]

    International Journal of Human–Computer Interaction 39, 9 (2023), 1727–1739

    Trust in AI and Its Role in the Acceptance of AI Technologies. International Journal of Human–Computer Interaction 39, 9 (2023), 1727–1739. https://doi.org/10.1080/10447318.2022. 2050543 [64]Patrizio Pelliccione Ian Gorton, Alessio Bucaioni

    2023

  29. [52]

    Technical Credit. Commun. ACM (2024). [65]Paola Inverardi

    2024

  30. [53]

    The European perspective on responsible computing. Commun. ACM 62, 4 (mar 2019),

    2019

  31. [55]

    Systematic review on privacy categorisation. Comput. Sci. Rev. 49 (2023), 100574. https://doi.org/10.1016/J.COSREV.2023.100574 [67]ISO25000

    2023

  32. [56]

    Computers and Education: Arti "cial Intelligence 6 (2024), 100225

    AI literacy and its implications for prompt engineering strategies. Computers and Education: Arti "cial Intelligence 6 (2024), 100225. https://doi.org/10. 1016/j.caeai.2024.100225 [69]Sri Kurniawan

    2024

  33. [58]

    Springer

    Perspectives on digital humanism . Springer. https://doi.org/10.1007/978-3-030-86144-5 [71]Jiewu Leng, Weinan Sha, Baicun Wang, Pai Zheng, Cunbo Zhuang, Qiang Liu, Thorsten Wuest, Dimitris Mourtzis, and Lihui Wang

    doi

  34. [59]

    Journal of Manufacturing Systems 65 (2022), 279–295

    Industry 5.0: Prospect and retrospect. Journal of Manufacturing Systems 65 (2022), 279–295. https://doi.org/10.1016/j.jmsy.2022.09.017 [72]G. Li, B. Liu, and H. Zhang

    2022 doi

  35. [60]

    Computer56, 04 (2023), 28–37

    Quality Attributes of Trustworthy Arti !cial Intelligence in Normative Documents and Secondary Studies: A Preliminary Review. Computer56, 04 (2023), 28–37. https://doi.org/10.1109/MC.2023.3240730 [73]Nianyu Li, Javier Cámara, David Garlan, Bradley Schmerl, and Zhi Jin

    2023

  36. [61]

    In 2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

    Hey! Preparing Humans to do Tasks in Self-adaptive Systems. In 2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). 48–58. https://doi.org/10.1109/SEAMS51251.2021.00017 [74]Michael C. Loui, Nigel Bosch, Anita Say Chan, Jenny L. D...

    2021

  37. [62]

    Arti!cial Intelligence, Social Responsibility, and the Roles of the University. Commun. ACM 67, 8 (2024), 22–25. https://doi.org/10.1145/3640541 [75]Qinghua Lu, Liming Zhu, Jon Whittle, and Xiwei Xu

    2024 doi

  38. [63]

    In Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI, CAIN 2022, Pittsburgh, Pennsylvania, May 16-17, 2022 , Ivica Crnkovic (Ed.)

    Towards a roadmap on software engineering for responsible AI. In Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI, CAIN 2022, Pittsburgh, Pennsylvania, May 16-17, 2022 , Ivica Crnkovic (Ed.). ACM, 101–112. https://doi.org/10. 1145/...

    2022

  39. [64]

    https://doi.org/10.1145/3311783 [66]Paola Inverardi, Patrizio Migliarini, and Massimiliano Palmiero

    doi

  40. [65]

    Arti !cial intelligence act. https://www.europarl.europa.eu/RegData/etudes/BRIE/ 2021/698792/EPRS_BRI(2021)698792_EN.pdf [78]Silverio Martínez-Fernández, Justus Bogner, Xavier Franch, Marc Oriol, Julien Siebert, Adam Trendowicz, Anna Maria Vollmer, and Stefan Wagner

    2021

  41. [66]

    ACM Trans

    Software Engineering for AI-Based Systems: A Survey. ACM Trans. Softw. Eng. Methodol.31, 2 (2022), 37e:1–37e:59. https://doi.org/10.1145/3487043 [79]Meaghan Tobin

    2022 doi

  42. [67]

    Pioneers Call for Protections Against ‘Catastrophic Risks’

    A.I. Pioneers Call for Protections Against ‘Catastrophic Risks’. https://www.nytimes.com/ 2024/09/16/business/china-ai-safety.html [80]Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan

    2024

  43. [68]

    ACM Comput

    A Survey on Bias and Fairness in Machine Learning. ACM Comput. Surv. 54, 6 (2022), 115:1–115:35. https://doi.org/10.1145/3457607 [81]Mashal Afzal Memon, Gian Luca Scoccia, Marco Autili, and Paola Inverardi

    2022 doi

  44. [70]

    An Architecture for Ethics-Based Negotiation in the Decision-Making of Intelligent Autonomous Systems. In 2024 IEEE 21st International Conference on Software Architecture (ICSA) .https://doi.org/10.1109/ICSA-C63560.2024.00016 [83]Mashal Afzal Memon, Gian Luca Scoccia, Paola In...

    2024

  45. [71]

    cial Intelligence (Frontiers in Arti

    Don’t You Agree with My Ethics? Let’s Negotiate!. InHHAI 2023: Augmenting Human Intellect - Proceedings of the 2nd International Conference on Hybrid Human-Arti"cial Intelligence (Frontiers in Arti "cial Intelligence and Applications, Vol

    2023

  46. [72]

    IEEE Transactions on Software Engineering 49, 4 (2023), 2741–2760

    Mission Speci !cation Patterns for Mobile Robots: Providing Support for Quantitative Properties. IEEE Transactions on Software Engineering 49, 4 (2023), 2741–2760. https://doi.org/10.1109/TSE.2022.3230059 [85]Claudio Menghi, Christos Tsigkanos, Patrizio Pelliccione, Carlo Ghez...

    2023

  47. [73]

    IEEE Transactions on Software Engineering 47, 10 (Oct

    Speci !cation Patterns for Robotic Missions . IEEE Transactions on Software Engineering 47, 10 (Oct. 2021), 2208–2224. https: //doi.org/10.1109/TSE.2019.2945329 [86]moodys.com/kyc

    2021

  48. [74]

    [88]Vincent C Müller

    Adjustable autonomy: a systematic literature review.Arti"cial Intelligence Review 51, 2 (2019). [88]Vincent C Müller

    2019

  49. [75]

    The Stanford Encyclopedia of Philosophy (Summer 2021 Edition), Edward N

    Ethics of arti !cial intelligence and robotics. The Stanford Encyclopedia of Philosophy (Summer 2021 Edition), Edward N. Zalta (ed.) (2021). [89]Mohammad Naiseh, Caitlin M. Bentley, and Sarvapali D. Ramchurn

    2021

  50. [76]

    In IEEE Global Engineering Education Conference, EDUCON 2022, Tunis, Tunisia, March 28-31, 2022 , Ilhem Kallel, Habib M

    Trustworthy Autonomous Systems (TAS): Engaging TAS experts in curriculum design. In IEEE Global Engineering Education Conference, EDUCON 2022, Tunis, Tunisia, March 28-31, 2022 , Ilhem Kallel, Habib M. Kammoun, and Lobna Hsairi (Eds.). IEEE, 901–905. https://doi.org/10.1109/ED...

    2022

  51. [77]

    Journal of Systems and Software 207 (2024), 111860

    Architecting ML-enabled systems: Challenges, best practices, and design decisions. Journal of Systems and Software 207 (2024), 111860. https://doi.org/10.1016/j.jss.2023. 111860 [91]Neelofar and Aldeida Aleti

    2024 doi

  52. [78]

    In Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, ICSE 2024, Lisbon, Portugal, April 14-20, 2024

    Towards Reliable AI: Adequacy Metrics for Ensuring the Quality of System-level Testing of Autonomous Vehicles. In Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, ICSE 2024, Lisbon, Portugal, April 14-20, 2024 . ACM, 68:1–68:12. https://doi.or...

    2024

  53. [79]

    Robotics and Computer-Integrated Manufacturing 68 (2021), 102085

    MEGURU: a gesture-based robot program builder for Meta-Collaborative workstations. Robotics and Computer-Integrated Manufacturing 68 (2021), 102085. https://doi.org/10.1016/j.rcim.2020.102085 [93]OECD.AI Policy Observatory. 2019 updated May

    2021

  54. [80]

    International Journal of Human-Computer Studies 131 (2019), 120–130

    End-user development for personalizing applications, things, and robots. International Journal of Human-Computer Studies 131 (2019), 120–130. https://doi.org/10.1016/j.ijhcs.2019.06.002 50 years of the International Journal of Human-Computer Studies. Re #ections on the past, p...

    2019 doi

  55. [81]

    IEEE Reliability Magazine 1, 1 (2024), 10–14

    Insights From the Software Reliability Research Community. IEEE Reliability Magazine 1, 1 (2024), 10–14. https://doi.org/10.1109/MRL.2024.3358736 [97]Alberto Petrucci, Francesco Basciani, and Patrizio Pelliccione

    2024

  56. [82]

    IEEE Software(2024), 1–8

    AI/ML for safety-critical software: the case of the space domain. IEEE Software(2024), 1–8. https://doi.org/10.1109/MS.2024.3412406 [98]Mauro Pezzè, Mauro Ciniselli, Luca Di Grazia, Niccolò Puccinelli, and Ketai Qiu

    2024

  57. [83]

    https://www.inf.usi.ch/faculty/pezze/media/SE2030SENreport.pdf

    The Trailer of the ACM 2030 Roadmap for Software Engineering. https://www.inf.usi.ch/faculty/pezze/media/SE2030SENreport.pdf . [Online; accessed 23-September-2024]. [99]Davide Di Ruscio, Paola Inverardi, Patrizio Migliarini, and Phuong T. Nguyen

    2024

  58. [84]

    Leveraging privacy pro !les to empower users in the digital society. Autom. Softw. Eng. 31, 1 (2024),

    2024

  59. [85]

    InProceedings of 17th International Conference on the Quality of Information and Communications Technology (QUATIC 2024)

    Do modern systems require new quality dimen- sions?. InProceedings of 17th International Conference on the Quality of Information and Communications Technology (QUATIC 2024). [101]Tiziano Santilli, Patrizio Pelliccione, Rebekka Wohlrab, and Ali Shahrokni

    2024

  60. [86]

    IEEE Software41, 4 (2024), 134–142

    Continuous Compliance in the Automotive Industry. IEEE Software41, 4 (2024), 134–142. https://doi.org/10.1109/MS.2023.3342974 [102]Inc. Scaled agile. [n. d.]. Scaled agile framework. https://www.scaledagileframework.com/agile-architecture/ . [Online; accessed 01-December-2020]...

    2024

  61. [87]

    Ada Lett.43, 2 (June 2024), 43–51

    Towards a Catalog of Prompt Patterns to Enhance the Discipline of Prompt Engineering. Ada Lett.43, 2 (June 2024), 43–51. https://doi.org/10.1145/3672359. 3672364 [104]Gian Luca Scoccia, Marco Autili, Giovanni Stilo, and Paola Inverardi

    2024 doi

  62. [88]

    In 9th IEEE/ACM International Conference on Mobile Software Engineering and Systems, MobileSoft@ICSE 2022

    An empirical study of privacy labels on the Apple iOS mobile app store. In 9th IEEE/ACM International Conference on Mobile Software Engineering and Systems, MobileSoft@ICSE 2022 . IEEE, 114–124. https://doi.org/10.1145/3524613.3527813 [105]Amanda Sharkey

    2022

  63. [90]

    ACM Trans

    Self-Adaptive Testing in the Field. ACM Trans. Auton. Adapt. Syst.19, 1, Article 4 (Feb. 2024), 37 pages. https://doi.org/10.1145/3627163 [107]ISO 25000 Software and Data Quality. [n. d.]. ISO/IEC 25010 Standard. https://iso25000.com/index.php/en/iso-25000- standards/iso-25010...

    2024 doi

  64. [91]

    [109]Luke Stark and Jesse Hoey

    Gartner Says Digital Ethics is at the Peak of In #ated Expectations in the 2021 Gartner Hype Cycle for Privacy. [109]Luke Stark and Jesse Hoey

    2021

  65. [92]

    In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency

    The ethics of emotion in arti !cial intelligence systems. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency . 782–793. [110]Constantine Stephanidis, Margherita Antona, and Stavroula Ntoa

    2021

  66. [93]

    Handbook of human factors and ergonomics (2021), 1058–1084

    Human factors in ambient intelligence environments. Handbook of human factors and ergonomics (2021), 1058–1084. [111]Daniel Susser

    2021

  67. [94]

    In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, AIES 2019, Honolulu, HI, USA, January 27-28, 2019, Vincent Conitzer, Gillian K

    Invisible In #uence: Arti!cial Intelligence and the Ethics of Adaptive Choice Architectures. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, AIES 2019, Honolulu, HI, USA, January 27-28, 2019, Vincent Conitzer, Gillian K. Had !eld, and Shannon Vallor ...

    2019 doi

  68. [95]

    Proceedings of the AAAI Conference on Arti "cial Intelligence 35, 13 (May 2021), 11657–11665

    Ethically Compliant Sequential Decision Making. Proceedings of the AAAI Conference on Arti "cial Intelligence 35, 13 (May 2021), 11657–11665. https://doi.org/10.1609/ aaai.v35i13.17386 [113]Damian A. Tamburri, Philippe Kruchten, Patricia Lago, and Hans van Vliet

    2021

  69. [96]

    Journal of Internet Services and Applications 6, 1 (2015), 10:1–10:17

    Social debt in software engineering - insights from industry. Journal of Internet Services and Applications 6, 1 (2015), 10:1–10:17. https://doi.org/10.1186/ S13174-015-0024-6 DBLP’s bibliographic metadata records provided through http://dblp.org/search/publ/api are distribute...

    2015

  70. [97]

    IEEE Softw.40, 3 (2023), 29–33

    Explainable AI for SE: Challenges and Future Directions. IEEE Softw.40, 3 (2023), 29–33. https://doi.org/10.1109/MS.2023.3246686 [115]Beverley A. Townsend, Colin Paterson, T. T. Arvind, Gabriel Nemirovsky, Radu Calinescu, Ana Cavalcanti, Ibrahim Habli, and Alan Thomas

    2023

  71. [98]

    Minds Mach.32, 4 (2022), 683–715

    From Pluralistic Normative Principles to Autonomous-Agent Rules. Minds Mach.32, 4 (2022), 683–715. https://doi.org/10.1007/S11023-022-09614-W [116]Nicolas Troquard, Martina De Sanctis, Paola Inverardi, Patrizio Pelliccione, and Gian Luca Scoccia

    2022 doi

  72. [99]

    cial Intelligence, AAAI 2024, Thirty-Sixth Conference on Innovative Applications of Arti

    Social, Legal, Ethical, Empathetic, and Cultural Rules: Compilation and Reasoning. In Thirty-Eighth AAAI Conference on Arti"cial Intelligence, AAAI 2024, Thirty-Sixth Conference on Innovative Applications of Arti "cial Intelligence, IAAI 2024, Fourteenth Symposium on Education...

    2024 doi

  73. [100]

    Available at: https://unstats.un.org/ sdgs/!les/report/2024/SG-SDG-Progress-Report-2024-advanced-unedited-version.pdf

    Progress towards the Sustainable Development Goals. Available at: https://unstats.un.org/ sdgs/!les/report/2024/SG-SDG-Progress-Report-2024-advanced-unedited-version.pdf . [119]United States Government

    2024

  74. [101]

    https://www.whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf [120]USA

    Blueprint for an AI Bill of Rights Making Automated Systems Work for the American People. https://www.whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf [120]USA

    2022

  75. [102]

    Verhagen, Mark A

    Executive Order on the Safe, Secure, and Trustworthy Development and Use of Arti !cial Intelli- gence.https://www.whitehouse.gov/brie !ng-room/presidential-actions/2023/10/30/executive-order-on-the-safe- secure-and-trustworthy-development-and-use-of-arti !cial-intelligence/ [1...

    2023

  76. [103]

    In Explainable and Transparent AI and Multi-Agent Systems: Third International Workshop, EXTRAAMAS 2021, Virtual Event, May 3–7, 2021, Revised Selected Papers

    A Two-Dimensional Explanation Framework to Classify AI as Incomprehensible, Interpretable, or Understandable. In Explainable and Transparent AI and Multi-Agent Systems: Third International Workshop, EXTRAAMAS 2021, Virtual Event, May 3–7, 2021, Revised Selected Papers . Spring...

    2021 doi

  77. [104]

    In 2019 10th International Workshop on Empirical Software Engineering in Practice (IWESEP)

    Studying software engineering patterns for designing machine learning systems. In 2019 10th International Workshop on Empirical Software Engineering in Practice (IWESEP) . IEEE, 49–495. [123]Adam Waytz, Joy Heafner, and Nicholas Epley

    2019

  78. [105]

    Journal of Experimental Social Psychology 52 (2014), 113–117

    The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle. Journal of Experimental Social Psychology 52 (2014), 113–117. https://doi.org/10.1016/j.jesp. 2014.01.005 [124]H. Werthner, C. Ghezzi, J. Kramer, J. Nida-Rümelin, B. Nuseibeh, E. Prem, and A. Stanger

    2014 doi

  79. [106]

    Computer56, 1 (2023), 138–142

    Digital Humanism: The Time Is Now. Computer56, 1 (2023), 138–142. https://doi.org/10.1109/MC.2022.3219528 [127]Roel Wieringa

    2023

  80. [107]

    Toolkit for speci!cation, validation and veri !cation of social, legal, ethical, empathetic and cultural requirements for autonomous agents. Sci. Comput. Program. 236 (2024), 103118. https://doi.org/10.1016/J.SCICO.2024.103118 [129]Stéphane Zieba, Philippe Polet, and Vanderhae...

    2024

  81. [354]

    IOS Press, 128–142

    , Stefan Schlobach, María Pérez-Ortiz, and Myrthe Tielman (Eds.). IOS Press, 128–142. https://doi.org/10.3233/FAIA220194 [7]Rani Lill Anjum and Mumford Stephen

    doi

  82. [1993]

    IBM Systems Journal 32, 1 (1993), 472–484

    Strategic alignment: Leveraging information technology for trans- forming organizations. IBM Systems Journal 32, 1 (1993), 472–484. https://doi.org/10.1147/sj.382.0472 [55]Bruno Miranda Henrique and Eugene Santos

    1993 doi

  83. [2003]

    7 (01 2003)

    Adjustable Autonomy and Human-Agent Teamwork in Practice: An Interim Report on Space Applications. 7 (01 2003). https://doi.org/10.1007/978-1-4419-9198-0_11 [25]N. Brown, Y. Cai, Y. Guo, R. Kazman, M. Kim, P. Kruchten ad E. Lim, A. MacCormack, R. Nord, I. Ozkaya, R. Sangwan, C...

    2003 doi

  84. [2004]

    Universal Access in the Information Society 3 (2004), 289–289

    Interaction design: Beyond human–computer interaction by Preece, Sharp and Rogers (2001), ISBN 0471492787. Universal Access in the Information Society 3 (2004), 289–289. [70]Edward A. Lee, Carlo Ghezzi, Erich Prem, and Hannes Werthner (Eds.)

    2001

  85. [2005]

    BITAM: an engineering-principled method for managing misalignments between business and IT architectures. Sci. Comput. Program. 57, 1 (jul 2005), 5–26. https://doi.org/10. 1016/j.scico.2004.10.002 [31]China

    2005

  86. [2009]

    (04 2009)

    Inconsistencies Evaluation Mechanisms for an Hybrid Control Architecture with Adaptive Autonomy. (04 2009). [41]Rudresh Dwivedi, Devam Dave, Het Naik, Smiti Singhal, Rana Omer, Pankesh Patel, Bin Qian, Zhenyu Wen, Tejal Shah, Graham Morgan, et al .2023. Explainable AI (XAI): C...

    2023

  87. [2010]

    In Proceedings of the 2010 ICSE Workshop on Sharing and Reusing Architectural Knowledge (Cape Town, South Africa) (SHARK ’10)

    Business goals as architectural knowledge. In Proceedings of the 2010 ICSE Workshop on Sharing and Reusing Architectural Knowledge (Cape Town, South Africa) (SHARK ’10). Association for Computing Machinery, New York, NY, USA, 9–12. https://doi.org/10.1145/1833335.1833337 [35]P...

    2010

  88. [2011]

    Information Sciences181 (02 2011), 379–397

    Using adjustable autonomy and human–machine cooperation to make a human–machine system resilient – Application to a ground robotic system. Information Sciences181 (02 2011), 379–397. https://doi.org/10.1016/j.ins.2010.09.035 , Vol. 1, No. 1, Article . Publication date: December 2024

    2011 doi

  89. [2012]

    ect.Nw. U. L. Rev. 1107 (2012). https://scholarship.law.columbia.edu/faculty_ scholarship/271 [24]Je

    The Brussels E "ect.Nw. U. L. Rev. 1107 (2012). https://scholarship.law.columbia.edu/faculty_ scholarship/271 [24]Je"rey Bradshaw, Maarten Sierhuis, Alessandro Acquisti, Paul J. Feltovich, Robert Ho "man, Renia Je"ers, Debbie Prescott, Niranjan Suri, Andrzej Uszok, and Ron Hoof

    2012

  90. [2013]

    Designing Interactive Systems: A Comprehensive Guide to HCI, UX and Interaction Design . Pearson. https://books.google.it/books?id=HeW6mgEACAAJ [20]Antonia Bertolino, Pietro Braione, et al .2021. A survey of !eld-based testing techniques. ACM Computing Surveys (CSUR)54, 5 (202...

    2021

  91. [2014]

    [106]Samira Silva, Patrizio Pelliccione, and Antonia Bertolino

    Robots and human dignity: a consideration of the e "ects of robot care on the dignity of older people.Ethics and Information Technology 16, 1 (2014). [106]Samira Silva, Patrizio Pelliccione, and Antonia Bertolino

    2014

  92. [2015]

    IEEE Trans

    Aligning Qualitative, Real- Time, and Probabilistic Property Speci !cation Patterns Using a Structured English Grammar. IEEE Trans. Software Eng.41, 7 (2015), 620–638. https://doi.org/10.1109/TSE.2015.2398877 , Vol. 1, No. 1, Article . Publication date: December

    2015

  93. [2018]

    RoboMAX: Robotic Mission Adaptation eXemplars

    What Tends to Be: The Philosophy of Dispositional Modality .https: //doi.org/10.4324/9781351009805 [8]Mehrnoosh Askarpour, Christos Tsigkanos, Claudio Menghi, Radu Calinescu, Patrizio Pelliccione, Sergio García, Ricardo Caldas, Tim J von Oertzen, Manuel Wimmer, Luca Berardinel...

    doi

  94. [2019]

    IEEE Access7 (2019), 62011–62021

    A Software Exoskeleton to Protect and Support Citizen’s Ethics and Privacy in the Digital World. IEEE Access7 (2019), 62011–62021. https: //doi.org/10.1109/ACCESS.2019.2916203 [10]Marco Autili, Lars Grunske, Markus Lumpe, Patrizio Pelliccione, and Antony Tang

    2019

  95. [2020]

    In 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)

    Understanding Uncertainty in Self-adaptive Systems. In 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). 242–251. https://doi.org/10.1109/ACSOS49614.2020.00047 [29]Joymallya Chakraborty, Suvodeep Majumder, and Tim Menzies

    2020

  96. [2021]

    54, 8, Article 164 (2021), 36 pages

    A Survey on End-User Robot Programming. 54, 8, Article 164 (2021), 36 pages. https://doi.org/10.1145/3466819 [5]Costanza Al!eri, Martina De Sanctis, Donatella Donati, and Paola Inverardi

    2021 doi

  97. [2022]

    cial Intelligence, Amsterdam, The Netherlands, 13-17 June 2022 (Frontiers in Arti

    Exosoul: Ethical Pro !ling in the Digital World. In HHAI 2022: Augmenting Human Intellect - Proceedings of the First International Conference on Hybrid Human-Arti"cial Intelligence, Amsterdam, The Netherlands, 13-17 June 2022 (Frontiers in Arti "cial Intelligence and Applications, Vol

    2022

  98. [2023]

    In 45th IEEE/ACM International Conference on Software Engineering: Software Engineering in Society, SEIS@ICSE 2023

    Values@Runtime: An Adaptive Framework for Operationalising Values. In 45th IEEE/ACM International Conference on Software Engineering: Software Engineering in Society, SEIS@ICSE 2023 . IEEE, 175–179. https://doi.org/10.1109/ICSE-SEIS58686.2023.00024 [19]D. Benyon

    2023

  99. [2024]

    On Specifying for Trustworthiness. Commun. ACM 67, 1 (2024), 98–109. https://doi.org/10.1145/3624699 [4]Gopika Ajaykumar, Maureen Steele, and Chien-Ming Huang

    2024 doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.