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Record W4389192441 · doi:10.22215/etd/2023-15705

Towards Security-Focused Developer Personas

2023· dissertation· en· W4389192441 on OpenAlexaff
Christos Karanassios

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicPersona Design and Applications
Canadian institutionsCarleton University
Fundersnot available
KeywordsPersonaUsabilitySoftware security assuranceComputer scienceSoftwareDimension (graph theory)EngineeringWorld Wide WebComputer securityInformation securityHuman–computer interactionSecurity service

Abstract

fetched live from OpenAlex

Usability issues are one of the reasons behind the lack of security testing tool adoption by software developers. The design of security testing tools by Security Software Creators (SSCs) can be strengthened through the use of security focused developer personas which allow an improved understanding of software developer behaviour. Despite the widespread use of personas in other fields, limited research exists that specifically tailor personas to software developers from a security perspective. This research uses an interview study with 20 participants to explore 18 security focused developer persona dimensions, as well as a survey study with 40 participants to identify a range of dimension-specific information. Based on the results from the two studies, we developed a security focused developer persona framework that provides SSCs with a structured approach to create security-focused developer personas.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.536
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.283
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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