Evidence map›Paper›PMID 41953787›Full record

ArticleTechnology, mind, and behavior2023

Recognizing the Known Unknowns; the Interaction Between Reflective Thinking and Optimism for Uncertainty Among Software Developer's Security Perceptions.

Matthew Ivory, John Towse, Miriam Sturdee, Mark Levine, Bashar Nuseibeh

Abstract read
In one paragraph

Article in Technology, mind, and behavior, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Matthew IvoryDepartment of Psychology, Lancaster University.ORCID 0000-0002-5296-5897
John TowseDepartment of Psychology, Lancaster University.
Miriam SturdeeSchool of Computer Science, St. Andrews University.
Mark LevineDepartment of Psychology, Lancaster University.
Bashar NuseibehDepartment of Computer Science and Information, LERO (Irish Software Research Centre), Limerick, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Software development is a complex process requiring aspects of social, cognitive, and technical skills. Software engineers face high levels of uncertainty and risk during functional and security decision making. This preregistered study investigates behavioral measures of cognitive reflection, risk aversion, and optimism bias among professional freelance software developers and computer science students, to expose relationships between uncertainty-associated language and risk sensitivity. We employ content analysis with a mixed-effect model to understand how psychological dimensions influence risk sensitivity in secure software development. We show an interaction between cognitive reflection and optimism bias in the proportion of uncertainty-related language used. Overly optimistic outlooks combined with higher cognitive reflection drives up expressions of uncertainty, while pessimistic or realistic individuals reduce uncertainty as cognitive reflection increases. Software engineers who hold average or pessimistic views on the security of their code are more likely to speak more intuitively about security and risk. We discuss the potential of our findings in relation to understanding how to leverage language used by engineers as markers of risk aversion. Encouraging increased discourse could be used as a catalyst for increased cognitive reflection and grounding optimistic behaviors, leading to more careful decisions.

Indexed as

cognitive reflectionoptimism biassoftware engineer

Identifiers

PMID41953787
PMCPMC13054958

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.