VLDB 2026 Research / reviewers in the wild / expert
Catherine M. Hicks
dblp:159/3670
· DBLP profile ↗
4ranked-venue papers
2as first author
2since 2021 · last 2025
0009-0007-5657-1661ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2 · 2 since 2021Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | No silver bullets: Why understanding software cycle time is messy, not magic
John C. Flournoy, Carol S. Lee, Maggie Wu, Catherine M. Hicks |
Empir. Softw. Eng. | 4 |
| 2024 | Understanding and effectively mitigating code review anxietyabstractAbstract Anxiety about giving and receiving code reviews has been documented as a common occurrence that leads to developers avoiding code reviews by procrastinating and limiting their cognitive engagement with them. This avoidance not only increases anxiety in the long term, but also prevents developers, their teams, and their organizations from accessing the technical and sociocognitive benefits of effective and efficient code reviews. However, software research has not yet empirically examined code review anxiety, and from this, tractable intervention targets and strategies for mitigating code review anxiety. In this study, we present an empirical framework for understanding the factors maintaining and exacerbating code review anxiety. Utilizing a randomized waitlist control trial, we also tested the effectiveness of a novel single-session cognitive-behavioral workshop intervention. Our results show evidence that positive impact can be obtained from a brief intervention and suggest code review anxiety can be successfully mitigated by targeting developers’ cost bias, anxiety self-efficacy, and self-compassion. Carol S. Lee, Catherine M. Hicks |
Empir. Softw. Eng. | 2 |
| 2016 | Framing Feedback: Choosing Review Environment Features that Support High Quality Peer AssessmentabstractPeer assessment is rapidly growing in online learning, as it presents a method to address scalability challenges. However, research suggests that the benefits of peer review are obtained inconsistently. This paper explores why, introducing three ways that framing task goals significantly changes reviews. Three experiments manipulated features in the review environment. First, adding a numeric scale to open text reviews was found to elicit more explanatory, but lower quality reviews. Second, structuring a review task into short, chunked stages elicited more diverse feedback. Finally, showing reviewers a draft along with finished work elicited reviews that focused more on the work's goals than aesthetic details. These findings demonstrate the importance of carefully structuring online learning environments to ensure high quality peer reviews. Catherine M. Hicks, Vineet Pandey, C. Ailie Fraser, Scott R. Klemmer |
CHI | 1 |
| 2015 | Do Numeric Ratings Impact Peer Reviewers?abstractResearch suggests that online peer review can provide critical help to learners who would otherwise not be given individualized feedback on their work. However, little is known about how different characteristics of review systems impact reviewers. This extended abstract presents preliminary results from an online experiment examining how explicit numeric ratings change peer reviews. A between-subject experiment found that peer reviewers who were asked to generate a numeric rating as well as general feedback gave significantly more explanations and made more positive comments compared with reviewers who were asked to give general feedback only. These exploratory findings suggest the need to further examine how online peer review systems' affordances can impact the reviews given in these systems. Catherine M. Hicks, C. Ailie Fraser, Purvi Desai, Scott R. Klemmer |
L@S | 1 |