Related papers: Impostor Phenomenon as Human Debt: A Challenge to the Future of Software Engineering

Impostor Phenomenon as Human Debt: A Challenge to the Future of Software Engineering

URL: http://arxiv.org/abs/2602.13767v1
Date: Sat, 14 Feb 2026 13:26:38 GMT
Title: Impostor Phenomenon as Human Debt: A Challenge to the Future of Software Engineering
Authors: Paloma Guenes, Rafael Tomaz, Maria Teresa Baldassarre, Alexander Serebrenik,
Abstract summary: The Impostor Phenomenon (IP) impacts a significant portion of the Software Engineering workforce.<n>Similar to technical debt, Human Debt accumulates due to gaps in psychological safety and inclusive support within socio-technical ecosystems.
Score: 46.44607910934403
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The Impostor Phenomenon (IP) impacts a significant portion of the Software Engineering workforce, yet it is often viewed primarily through an internal individual lens. In this position paper, we propose framing the prevalence of IP as a form of Human Debt and discuss the relation with the ICSE2026 Pre Survey on the Future of Software Engineering results. Similar to technical debt, which arises when short-term goals are prioritized over long-term structural integrity, Human Debt accumulates due to gaps in psychological safety and inclusive support within socio-technical ecosystems. We observe that this debt is not distributed equally, it weighs heavier on underrepresented engineers and researchers, who face compounded challenges within traditional hierarchical structures and academic environments. We propose cultural refactoring, transparency and active maintenance through allyship, suggesting that leaders and institutions must address the environmental factors that exacerbate these feelings, ensuring a sustainable ecosystem for all professionals.

Related papers

Artificial Intelligence in Open Source Software Engineering: A Foundation for Sustainability [0.0]
Review explores how artificial intelligence is being leveraged to address challenges to open-source software sustainability.<n>Paper identifies key applications of AI in this domain, including automated bug triaging and system maintenance.<n>Review also examines the limitations and ethical concerns that arise from applying AI in OSS contexts.
arXiv Detail & Related papers (2026-02-05T20:53:45Z)
Aligning Academia with Industry: An Empirical Study of Industrial Needs and Academic Capabilities in AI-Driven Software Engineering [45.09204791294318]
The rapid advancement of large language models (LLMs) is fundamentally reshaping software engineering (SE)<n>While top-tier SE venues continue to show sustained or emerging focus on areas like automated testing and program repair, the alignment of these academic advances with real industrial needs remains unclear.<n>This study aims to refocus academic attention on these important yet under-explored problems and to guide future SE research toward greater industrial impact.
arXiv Detail & Related papers (2025-12-17T07:29:18Z)
Towards a Taxonomy of Sustainability Requirements for Software Design [3.8168092489216385]
This research conducts a Systematic Literature Review to extract and organize sustainability requirements from the state-of-the-art.<n>For each identified category, we provide clear definitions, associated metrics, and measures.<n>This systematized reference assists both software developers and researchers in effectively formulating, managing, and reconciling trade-offs within sustainable software development.
arXiv Detail & Related papers (2025-10-10T04:20:11Z)
Perspectives, Needs and Challenges for Sustainable Software Engineering Teams: A FinServ Case Study [0.5097809301149341]
This research focuses on a financial services company (FinServCo) that invited us to investigate perceptions of sustainability in their IT function.<n>We conducted an exploratory qualitative case study using interviews and a focus group with six higher management employees and 16 software engineers.<n>Our study found a clear divergence in how sustainability is perceived between organisational levels.
arXiv Detail & Related papers (2025-09-15T21:21:02Z)
Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance [211.5823259429128]
We propose a comprehensive framework integrating technical and societal dimensions, structured around three interconnected pillars: Intrinsic Security, Derivative Security, and Social Ethics.<n>We identify three core challenges: (1) the generalization gap, where defenses fail against evolving threats; (2) inadequate evaluation protocols that overlook real-world risks; and (3) fragmented regulations leading to inconsistent oversight.<n>Our framework offers actionable guidance for researchers, engineers, and policymakers to develop AI systems that are not only robust and secure but also ethically aligned and publicly trustworthy.
arXiv Detail & Related papers (2025-08-12T09:42:56Z)
The AI Imperative: Scaling High-Quality Peer Review in Machine Learning [49.87236114682497]
We argue that AI-assisted peer review must become an urgent research and infrastructure priority.<n>We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making.
arXiv Detail & Related papers (2025-06-09T18:37:14Z)
A Road Less Travelled and Beyond: Towards a Roadmap for Integrating Sustainability into Computing Education [7.060399369099591]
We propose a research roadmap to integrate sustainability principles and essential skills into the crowded computing curriculum. We identify key topics for teaching sustainability, including core sustainability principles, values and ethics, systems thinking, impact measurement, soft skills, business value, legal standards, and advocacy. The paper is accepted at the 2030 Software Engineering workshop, which is co-located with the FSE'24 conference.
arXiv Detail & Related papers (2024-06-27T07:19:15Z)
Understanding the Building Blocks of Accountability in Software Engineering [3.521765725717803]
We investigate the factors that foster software engineers' individual accountability within their teams. Our findings recognize two primary forms of accountability shaping software engineers individual senses of accountability: institutionalized and grassroots.
arXiv Detail & Related papers (2024-02-02T21:53:35Z)
Survey on Foundation Models for Prognostics and Health Management in Industrial Cyber-Physical Systems [1.1034992901877594]
Large-scale foundation models (LFMs) like BERT and GPT signifies a significant advancement in AI technology. ChatGPT stands as a remarkable accomplishment within this research paradigm, harboring potential for General Artificial Intelligence. Considering the ongoing enhancement in data acquisition technology and data processing capability, LFMs are anticipated to assume a crucial role in the PHM domain of ICPS.
arXiv Detail & Related papers (2023-12-11T09:58:46Z)
Predictable Artificial Intelligence [77.1127726638209]
This paper introduces the ideas and challenges of Predictable AI.<n>It explores the ways in which we can anticipate key validity indicators of present and future AI ecosystems.<n>We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems.
arXiv Detail & Related papers (2023-10-09T21:36:21Z)
Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
arXiv Detail & Related papers (2021-01-15T10:43:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.