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Samantha McLoughlin

dblp:426/3730 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Empirical software engineering · 50% Program synthesis and code generation · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Program synthesis and code generation
code summarization
0.912025
Programmers' Visual Attention on Function Call Graphs During Code Summarization · ASE 2025
Empirical software engineering
developer studies
0.912025
Programmers' Visual Attention on Function Call Graphs During Code Summarization · ASE 2025

Methods — techniques the papers use, named apart from their topics

graph-based metrics · 0.9eye tracking · 0.9
YearPublicationVenuePosition
2025 Programmers' Visual Attention on Function Call Graphs During Code Summarization
abstract
This paper studies programmer visual attention on code as it relates to underlying function call graphs during code summarization. Programmer visual attention refers to where people look when performing a software engineering task, and code summarization is the task of writing a natural language description about a section of source code. Prior work has studied programmers’ visual attention during code summarization, with the vast majority of research effort placed on details in single functional units of code. There have not been any techniques developed to understand code comprehension at the project level due to the difficulty of this task, despite the nature of most real-world methods as embedded within complex project context. This paper focuses on the visual attention paid to the call graph context in which a method sits. We analyze visual attention coverage of call graphs with graph-based metrics, such as the depth that programmers traverse or the amount of coverage they attain. We use these metrics, among other means, to reevaluate an existing dataset from a previous eye-tracking study of programmers (n = 10) that considered basic properties of programmer visual attention in a project context. We then created a new dataset (n = 12) using the same procedures specifically for this paper, resulting in a total of 88 hours of recorded visual behavior on source code. We used our proposed metrics to analyze how participants’ visual strategies correlated with their code summary quality, and confidence in their summaries. Interestingly, we found that higher coverage of the call graph was associated with decreases in both summary quality and participants’ confidence.
Samantha McLoughlin, Zachary Karas, Robert Wallace, Aakash Bansal, Collin McMillan, Yu Huang 0015
ASE1