Demo Data from EDBT 2026 / reviewers in the wild / expert
Kimberly Diaz
dblp:351/8637
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0009-0005-2138-4458ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 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 · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computing education · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computing education › computer science curriculum
formal methods education |
0.7 | 1 | 2023 | How Do We Read Formal Claims? Eye-Tracking and the Cognition of Proofs about Algorithms · ICSE 2023 |
Empirical software engineering › human factors in software engineering
developer cognition |
0.7 | 1 | 2023 | How Do We Read Formal Claims? Eye-Tracking and the Cognition of Proofs about Algorithms · ICSE 2023 |
Empirical software engineering › human factors in software engineering
eye-tracking studies |
0.7 | 1 | 2023 | How Do We Read Formal Claims? Eye-Tracking and the Cognition of Proofs about Algorithms · ICSE 2023 |
Methods — techniques the papers use, named apart from their topics
eye tracking · 1.3controlled experiment · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | How Do We Read Formal Claims? Eye-Tracking and the Cognition of Proofs about AlgorithmsabstractFormal methods are used successfully in high-assurance software, but they require rigorous mathematical and logical training that practitioners often lack. As such, integrating formal methods into software has been associated with numerous challenges. While educators have placed emphasis on formalisms in undergraduate theory courses, such courses often struggle with poor student outcomes and satisfaction. In this paper, we present a controlled eye-tracking human study (n = 34) investigating the problem-solving strategies employed by students with different levels of incoming preparation (as assessed by theory coursework taken and pre-screening performance on a proof comprehension task), and how educators can better prepare low-outcome students for the rigorous logical reasoning that is a core part of formal methods in software engineering. Surprisingly, we find that incoming preparation is not a good predictor of student outcomes for formalism comprehension tasks, and that student self-reports are not accurate at identifying factors associated with high outcomes for such tasks. Instead, and importantly, we find that differences in outcomes can be attributed to performance for proofs by induction and recursive algorithms, and that better-performing students exhibit significantly more attention switching behaviors, a result that has several implications for pedagogy in terms of the design of teaching materials. Our results suggest the need for a substantial pedagogical intervention in core theory courses to better align student outcomes with the objectives of mastery and retaining the material, and thus bettering preparing students for high-assurance software engineering. Hammad Ahmad, Zachary Karas, Kimberly Diaz, Amir Kamil, Jean-Baptiste Jeannin, Westley Weimer |
ICSE | 3 |