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Record W1977244105 · doi:10.1145/2559936

Automated cookie collection testing

2014· article· en· W1977244105 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACM Transactions on Software Engineering and Methodology · 2014
Typearticle
Languageen
FieldComputer Science
TopicSoftware Testing and Debugging Techniques
Canadian institutionsUniversity of AlbertaThe King's University
Fundersnot available
KeywordsComputer scienceWeb testingWeb applicationWeb application securitySecurity testingSoftware engineeringWorld Wide WebWeb developmentWeb serviceOperating systemCloud computing

Abstract

fetched live from OpenAlex

Cookies are used by over 80% of Web applications utilizing dynamic Web application frameworks. Applications deploying cookies must be rigorously verified to ensure that the application is robust and secure. Given the intense time-to-market pressures faced by modern Web applications, testing strategies that are low cost and automatable are required. Automated Cookie Collection Testing (CCT) is presented, and is empirically demonstrated to be a low-cost and highly effective automated testing solution for modern Web applications. Automatable test oracles and evaluation metrics specifically designed for Web applications are presented, and are shown to be significant diagnostic tests. Automated CCT is shown to detect faults within five real-world Web applications. A case study of over 580 test results for a single application is presented demonstrating that automated CCT is an effective testing strategy. Moreover, CCT is found to detect security bugs in a Web application released into full production.

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.

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)0.001
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.951
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.087
GPT teacher head0.310
Teacher spread0.223 · 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