EvoGraph turns linear AI-assisted programming into a manipulable graph of branching histories, reducing cognitive load and enabling better iteration according to a user study with 20 developers.
Liang, Chenyang Yang, and Brad A
13 Pith papers cite this work, alongside 130 external citations. Polarity classification is still indexing.
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Novices performed better and reported lower workload with GitHub Copilot than with human partners, but human partners produced more positive emotions and a smaller drop in retest performance after one week.
Developers using AI assistants exhibit more stable emotions and greater focus on code creation, evaluation, and verification, captured in a new four-dimensional S-IASE model from retrospective labeling of screen recordings, surveys, and interviews.
In a real UK law-enforcement deployment, crime analysts selectively used AI linkage predictions and validated them against behavioural matrices, attending most to MO similarity and geography.
LLMs generate verifiable MaxSAT encodings from natural language, achieving over 80% acceptance rates on preference tasks where direct LLM reasoning fails.
Empirical analysis of 338 PRs with self-admitted ChatGPT usage shows low full integration (median 25%), selective adaptation patterns, and broader influence on developer reasoning during reviews.
Mixed-methods study of 27 developers characterizes five Copilot chat interaction modes and ten needs linked to problem-solving styles and experience levels.
Mixed-methods study adapting UTAUT2 shows individual-level perceptions predict continued GenAI use in Italian SME developers (R²=0.647) while social and organisational factors do not.
Longitudinal surveys show AI coding assistants reduce time on code writing but increase supervisory verification tasks, with stable productivity perceptions yet rising reports of worsened developer experience.
Interviews reveal a four-stage vibe coding workflow that accelerates prototyping while introducing tensions between quick efficiency and reflective design intention, plus asymmetries in trust and ownership.
User study reveals nine LLM failure categories in SE tasks and quantifies abandonment factors from 26 participants.
Novice programmers completed more tasks with lower workload using GitHub Copilot versus a human partner, but reported significantly more positive and arousing emotions with the human teammate.
The paper describes ongoing efforts to characterize developer diversity in cognition and context and to use personalization to make LLM-based conversational programming assistants more inclusive.
citing papers explorer
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Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph
EvoGraph turns linear AI-assisted programming into a manipulable graph of branching histories, reducing cognitive load and enabling better iteration according to a user study with 20 developers.
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Fast and Forgettable: A Controlled Study of Novices' Performance, Learning, Workload, and Emotion in AI-Assisted and Human Pair Programming Paradigms
Novices performed better and reported lower workload with GitHub Copilot than with human partners, but human partners produced more positive emotions and a smaller drop in retest performance after one week.
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How Do Developers Interact with AI? An Exploratory Study on Modeling Developer Programming Behavior
Developers using AI assistants exhibit more stable emotions and greater focus on code creation, evaluation, and verification, captured in a new four-dimensional S-IASE model from retrospective labeling of screen recordings, surveys, and interviews.
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How Analysts Use AI in High-Stakes Crime Linkage: An Industrial Study
In a real UK law-enforcement deployment, crime analysts selectively used AI linkage predictions and validated them against behavioural matrices, attending most to MO similarity and geography.
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Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability
LLMs generate verifiable MaxSAT encodings from natural language, achieving over 80% acceptance rates on preference tasks where direct LLM reasoning fails.
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PatchTrack: A Comprehensive Analysis of ChatGPT's Influence on Pull Request Outcomes
Empirical analysis of 338 PRs with self-admitted ChatGPT usage shows low full integration (median 25%), selective adaptation patterns, and broader influence on developer reasoning during reviews.
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No Two Developers Think Alike: How Problem-Solving Styles and Experience Shape Needs in Conversational Interaction with Copilot
Mixed-methods study of 27 developers characterizes five Copilot chat interaction modes and ten needs linked to problem-solving styles and experience levels.
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From Early Adoption to Sustained Use: Understanding GenAI Usage Among Software Developers in Italian SMEs
Mixed-methods study adapting UTAUT2 shows individual-level perceptions predict continued GenAI use in Italian SME developers (R²=0.647) while social and organisational factors do not.
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The Impact of AI Coding Assistants on Software Engineering: A Longitudinal Study
Longitudinal surveys show AI coding assistants reduce time on code writing but increase supervisory verification tasks, with stable productivity perceptions yet rising reports of worsened developer experience.
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Vibe Coding in Product Teams: Reconfiguring AI-Assisted Workflows, Prototyping, and Collaboration
Interviews reveal a four-stage vibe coding workflow that accelerates prototyping while introducing tensions between quick efficiency and reflective design intention, plus asymmetries in trust and ownership.
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"Should I Give Up Now?" Investigating LLM Pitfalls in Software Engineering
User study reveals nine LLM failure categories in SE tasks and quantifies abandonment factors from 26 participants.
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OGER: A Robust Offline-Guided Exploration Reward for Hybrid Reinforcement Learning
Novice programmers completed more tasks with lower workload using GitHub Copilot versus a human partner, but reported significantly more positive and arousing emotions with the human teammate.
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Personalizing LLM-Based Conversational Programming Assistants
The paper describes ongoing efforts to characterize developer diversity in cognition and context and to use personalization to make LLM-based conversational programming assistants more inclusive.