CHI

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\acmConference[CHI '26]{CHI Conference on Human Factors in Computing Systems}{April 2026}{City, Country}
\acmISBN{978-1-4503-XXXX-X/26/04}
\acmDOI{10.1145/XXXXXXX.XXXXXXX}
\setcopyright{acmlicensed}
\copyrightyear{2026}
\acmYear{2026}

\begin{document}

\title{How Programmers Use LLM Coding Assistants\\on Unfamiliar Codebases}

\author{First Last}
\affiliation{\institution{University of Example}\country{Country}}
\email{[email protected]}
\author{Jane Doe}
\affiliation{\institution{Example Research Labs}\country{Country}}
\email{[email protected]}
\author{John Smith}
\affiliation{\institution{University of Example}\country{Country}}
\email{[email protected]}
\renewcommand{\shortauthors}{Last et al.}

\begin{abstract}
LLM coding assistants have transformed how programmers write code, but
little is known about how they are used on unfamiliar codebases. We
conducted a mixed-methods study with 32 professional developers
completing five tasks in an unfamiliar repository. Our findings reveal
three usage patterns, two systematic trust failures, and design
implications for next-generation assistants.
\end{abstract}

\begin{CCSXML}
<ccs2012><concept><concept_id>10003120.10003121</concept_id>
<concept_desc>Human-centered computing~HCI</concept_desc></concept></ccs2012>
\end{CCSXML}
\ccsdesc[500]{Human-centered computing~HCI}
\keywords{LLM assistants, software engineering, mixed methods}
\maketitle

\section{Introduction}
Developers increasingly use LLM coding assistants on codebases they did
not write. How do they calibrate trust? When do they succeed? When do
they silently fail?

\section{Related Work}
Copilot adoption studies, pair programming, program comprehension.

\section{Method}
We recruited 32 professional developers (6+ years experience). Each
completed five tasks in an unfamiliar 120k-LOC repository. Sessions
were screen-recorded and followed by semi-structured interviews.

\section{Findings}
\subsection{Three Usage Patterns}
Participants exhibited three patterns: \emph{orienteer} (mental-model
building), \emph{executor} (delegating known tasks), and
\emph{collaborator} (joint proposal and critique).

\subsection{Trust Failures}
We identified two systematic failure modes: silent acceptance of API
hallucinations in well-known libraries, and over-reliance on
syntactically-correct-but-semantically-wrong explanations.

\section{Quantitative Results}
\begin{table}[t]
\centering\small
\begin{tabular}{lcc}
\toprule
Pattern & Completion (\%) & Quality (1--7) \\
\midrule
Orienteer     & 84 & 5.8 \\
Executor      & 78 & 4.9 \\
Collaborator  & 91 & 6.4 \\
\bottomrule
\end{tabular}
\caption{Task outcomes by usage pattern.}
\end{table}

\section{Discussion}
Assistants that expose \emph{how} they answer, not just \emph{what},
would improve trust calibration.

\section{Conclusion}
Developers work differently on unfamiliar code; assistants should adapt.

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