arXiv subjects

Victoria Jackson

Publications and source records attributed to Victoria Jackson.

13 recordsLinked to original sources

arXiv2026

A Preliminary Study on the Impact of AI in the Creativity and Collaboration in Software Teams

Generative artificial intelligence (GenAI) tools are reshaping software engineering work, increasingly acting as cognitive partners rather than purely instrumental aids. Most AI tooling remains optimized for individual use, yet software development is inherently collaborative and creative, raising open questions about how AI affects team-based work. This paper examines how software engineering professionals perceive and integrate AI tools focusing on creativity and collaboration within teams. We conducted semi-structured interviews with 13 software professionals from four companies, each representing a distinct team. Our analysis identifies three central themes - AI Use, Consequences of AI, and Collaboration and Team Dynamics, alongside Emotions as a cross-cutting dimension. Key findings include: AI broadens developers' creative repertoires but may narrow independent ideation; developers increasingly consult AI instead of colleagues, weakening peer learning and mentoring; and teams are developing emerging triadic collaboration patterns in which developers, colleagues, and AI reason together.

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arXiv2026

From Early Adoption to Sustained Use: Understanding GenAI Usage Among Software Developers in Italian SMEs

Generative AI tools are rapidly transforming software development practice, prompting unprecedented research interest. However, existing studies have predominantly examined initial adoption rather than sustained use. Understanding what drives developers to continue using these tools after initial adoption remains underexplored, particularly in small and medium-sized enterprises where resource constraints shape technology decisions differently than in large organisations. This study investigates factors associated with developers' intentions to continue using GenAI tools, adapting the UTAUT2 framework to post-adoption professional contexts. We employed a two-phase mixed-methods design. Phase 1 comprised a six-month longitudinal pilot study at an Italian software company combining surveys and interviews with 17 developers to explore how perceptions of GenAI evolve as experience accumulates. These insights informed a structural model tested in Phase 2 through a cross-sectional survey of 154 developers across Italian SMEs, analysed using PLS-SEM. The model explained substantial variance in continued use intention (R2 = 0.647), with individual-level perceptions, particularly around productivity, enjoyment, and ease of use, driving sustained adoption, whereas social and organisational factors played no significant role. These findings suggest that, for GenAI tools, post-adoption behaviour differs from initial adoption patterns: in voluntary professional contexts, sustained use is driven primarily by individual-level factors rather than by social and organisational support.

cs.SE↗

arXiv2026

ChatGPT: Friend or Foe When Comprehending and Changing Unfamiliar Code

A rapidly growing body of research is examining how LLMs influence developers when they code. To date, this research has tended to focus on productivity and code quality outcomes, rather than the underlying cognitive processes involved in programming. To address this gap, we report on the results of an exploratory laboratory study of ten advanced student developers (five with support from AI and five without) who had to make a non-trivial extension to a sizable software system. Leveraging Polya's four problem-solving phases and 25 inductively-generated codes detailing distinct problem-solving behaviors as the primary lenses, we examined: (1) how AI impacted the problem-solving approach the developers used to solve the programming task, and (2) how AI impacted their progress when they became stuck. For the analysis, we triangulated data across multiple sources (e.g., think-aloud, code changes, web searches, and LLM prompts). Unexpectedly, while developers in the AI group repeatedly turned to the AI tool to offload certain aspects of the process, all detailed problem-solving behaviors appeared in both groups. We also found that nine out of ten participants found themselves stuck in their work, but with key differences in how they became stuck and unstuck. We highlight seven distinct causes for being stuck and highlight how AI in some cases helped and in other cases hindered becoming unstuck.

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arXiv2026

Exploring Creativity in Human-Human-LLM Collaborative Software Design

While the use of Large Language Models (LLMs) in programming has been extensively studied, there is limited understanding of how LLMs support collaborative work where creativity plays a central role. Software design, as a collaborative and creative activity, provides a valuable context for exploring the influence of LLMs on creativity. This study investigates how and where creativity naturally emerges when software designers collaborate with an LLM during a design task. In a laboratory setting simulating a workplace environment, 18 pairs of software professionals with design experience were asked to complete a design task. Each pair had 90 minutes to produce a software design based on a set of requirements, with optional access to a custom LLM interface. Pairs were not primed to be creative. We find that creativity was present in all pairs in design processes, with 13 producing design documents containing creativity. We primarily attribute creativity to the human designers, driven by traits such as prior experience, empathy, and the use of analogies. The LLM contributed by producing novel ideas and elaborating human ideas. However, in some cases, the LLM appeared to hinder creativity by suggesting complex solutions or adding to unproductive digressions. LLMs can support creativity in collaborative software design, but human insights remain central. To effectively augment human creativity, designers must be intentional in their engagement with LLMs.

cs.SE↗

arXiv2026

The Role of LLMs in Collaborative Software Design

While much prior work examines Large Language Models (LLMs) for solo development tasks (e.g., coding), far less is known about how LLMs shape collaborative group work in software engineering. This study focuses on one such collaborative task, namely software design. It presents the results of an exploratory laboratory study of 18 pairs of software professionals who could use an LLM however they saw fit, to design a University campus bicycle parking application. Our findings reveal that introducing an LLM leads to distinct patterns of joint use: shared-instance use facilitated shared understanding, whereas parallel use across separate instances sometimes led to ''context drift''. We also observe wide variation in reliance, from non-use to treating the LLM as an information source or producer. Across these modes, professionals scrutinized and reflected on LLM responses, often yielding design insights; however, early anchoring sometimes curtailed exploration. We provide implications for tools to aid designers while retaining the human-centricity important to design.

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arXiv2025

"Game Changer" or "Overenthusiastic Drunk Acquaintance"? Generative AI Use by Blind and Low Vision Software Professionals in the Workplace

The software development workplace poses numerous technical and collaborative accessibility challenges for blind and low vision software professionals (BLVSPs). Though Generative AI (GenAI) is increasingly adopted within the software development industry and has been a rapidly growing topic of interest in research, to date, the unique perspectives of BLVSPs have yet to be consulted. We report on a qualitative study involving 39 semi-structured interviews with BLVSPs about what the introduction of GenAI has meant for their work. We found that BLVSPs used GenAI for many software development tasks, resulting in benefits such as increased productivity and accessibility. However, significant costs were also accompanied by GenAI use as they were more vulnerable to hallucinations than their sighted colleagues. Sometimes, organizational policies prevented use. Based on our findings, we discuss the higher-risks and higher-returns that BLVSPs had to carefully weigh when deciding whether and when to use GenAI tools for work.

cs.SE↗

arXiv2025

Building Software by Rolling the Dice: A Qualitative Study of Vibe Coding

Large language models (LLMs) are reshaping software engineering by enabling "vibe coding," in which developers build software primarily through prompts rather than writing code. Although widely publicized as a productivity breakthrough, little is known about how practitioners actually define and engage in these practices. To shed light on this emerging phenomenon, we conducted a grounded theory study of 20 vibe-coding videos, including 7 live-streamed coding sessions (about 16 hours, 254 prompts) and 13 opinion videos (about 5 hours), supported by additional analysis of activity durations and prompt intents. Our findings reveal a spectrum of behaviors: some vibe coders rely almost entirely on AI without inspecting code, while others examine and adapt generated outputs. Across approaches, all must contend with the stochastic nature of generation, with debugging and refinement often described as "rolling the dice." Further, divergent mental models, shaped by vibe coders' expertise and reliance on AI, influence prompting strategies, evaluation practices, and levels of trust. These findings open new directions for research on the future of software engineering and point to practical opportunities for tool design and education.

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arXiv2025

"Can you feel the vibes?": An exploration of novice programmer engagement with vibe coding

Emerging alongside generative AI and the broader trend of AI-assisted coding, the term "vibe coding" refers to creating software via natural language prompts rather than direct code authorship. This approach promises to democratize software development, but its educational implications remain underexplored. This paper reports on a one-day educational hackathon investigating how novice programmers and mixed-experience teams engage with vibe coding. We organized an inclusive event at a Brazilian public university with 31 undergraduate participants from computing and non-computing disciplines, divided into nine teams. Through observations, an exit survey, and semi-structured interviews, we examined creative processes, tool usage patterns, collaboration dynamics, and learning outcomes. Findings reveal that vibe coding enabled rapid prototyping and cross-disciplinary collaboration, with participants developing prompt engineering skills and delivering functional demonstrations within time constraints. However, we observed premature convergence in ideation, uneven code quality requiring rework, and limited engagement with core software engineering practices. Teams adopted sophisticated workflows combining multiple AI tools in pipeline configurations, with human judgment remaining essential for critical refinement. The short format (9 hours) proved effective for confidence-building among newcomers while accommodating participants with limited availability. We conclude that vibe coding hackathons can serve as valuable low-stakes learning environments when coupled with explicit scaffolds for divergent thinking, critical evaluation of AI outputs, and realistic expectations about production quality.

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arXiv2025

Using Generative AI in Software Design Education: An Experience Report

With the rapid adoption of Generative AI (GenAI) tools, software engineering educators have grappled with how best to incorporate them into the classroom. While some research discusses the use of GenAI in the context of learning to code, there is little research that explores the use of GenAI in the classroom for other areas of software development. This paper provides an experience report on introducing GenAI into an undergraduate software design class. Students were required to use GenAI (in the form of ChatGPT) to help complete a team-based assignment. The data collected consisted of the ChatGPT conversation logs and students' reflections on using ChatGPT for the assignment. Subsequently, qualitative analysis was undertaken on the data. Students identified numerous ways ChatGPT helped them in their design process while recognizing the need to critique the response before incorporating it into their design. At the same time, we identified several key lessons for educators in how to deploy GenAI in a software design class effectively. Based on our experience, we believe students can benefit from using GenAI in software design education as it helps them design and learn about the strengths and weaknesses of GenAI.

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arXiv2025

Creative Problem-Solving: A Study with Blind and Low Vision Software Professionals

Background: Software engineering requires both technical skills and creative problem-solving. Blind and low-vision software professionals (BLVSPs) encounter numerous workplace challenges, including inaccessible tools and collaboration hurdles with sighted colleagues. Objective: This study explores the innovative strategies employed by BLVSPs to overcome these accessibility barriers, focusing on their custom solutions and the importance of supportive communities. Methodology: We conducted semi-structured interviews with 30 BLVSPs and used reflexive thematic analysis to identify key themes. Results: Findings reveal that BLVSPs are motivated to develop creative and adaptive solutions, highlighting the vital role of collaborative communities in fostering shared problem-solving. Conclusion: For BLVSPs, creative problem-solving is essential for navigating inaccessible work environments, in contrast to sighted peers, who pursue optimization. This study enhances understanding of how BLVSPs navigate accessibility challenges through innovation.

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arXiv2025

The Dilemma of Building Do-It-Yourself (DIY) Solutions for Workplace Accessibility

Existing commercial and in-house software development tools are often inaccessible to Blind and Low Vision Software Professionals (BLVSPs), hindering their participation and career growth at work. Building on existing research on Do-It-Yourself (DIY) Assistive Technologies and customized tools made by programmers, we shed light on the currently unexplored intersection of how DIY tools built and used by BLVSPs support accessible software development. Through semi-structured interviews with 30 BLVSPs, we found that such tools serve many different purposes and are driven by motivations such as desiring to maintain a professional image and a sense of dignity at work. These tools had significant impacts on workplace accessibility and revealed a need for a more centralized community for sharing tools, tips, and tricks. Based on our findings, we introduce the "Double Hacker Dilemma" and highlight a need for developing more effective peer and organizational platforms that support DIY tool sharing.

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arXiv2024

Creativity, Generative AI, and Software Development: A Research Agenda

Creativity has always been considered a major differentiator to separate the good from the great, and we believe the importance of creativity for software development will only increase as GenAI becomes embedded in developer tool-chains and working practices. This paper uses the McLuhan tetrad alongside scenarios of how GenAI may disrupt software development more broadly, to identify potential impacts GenAI may have on creativity within software development. The impacts are discussed along with a future research agenda comprising six connected themes that consider how individual capabilities, team capabilities, the product, unintended consequences, society, and human aspects can be affected.

cs.SE↗

arXiv2024

Understanding the Career Mobility of Blind and Low Vision Software Professionals

Context: Scholars in the software engineering (SE) research community have investigated career advancement in the software industry. Research topics have included how individual and external factors can impact career mobility of software professionals, and how gender affects career advancement. However, the community has yet to look at career mobility from the lens of accessibility. Specifically, there is a pressing need to illuminate the factors that hinder the career mobility of blind and low vision software professionals (BLVSPs). Objective: This study aims to understand aspects of the workplace that impact career mobility for BLVSPs. Methods: We interviewed 26 BLVSPs with different roles, years of experience, and industry sectors. Thematic analysis was used to identify common factors related to career mobility. Results: We found four factors that impacted the career mobility of BLVSPs: (1) technical challenges, (2) colleagues' perceptions of BLVSPs, (3) BLVSPs' own perceptions on managerial progression, and (4) BLVSPs' investment in accessibility at the workplace. Conclusion: We suggest implications for tool designers, organizations, and researchers towards fostering more accessible workplaces to support the career mobility of BLVSPs.

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