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Record W4291414058 · doi:10.3390/software1030014

An Automated Tool for Upgrading Fortran Codes

2022· article· en· W4291414058 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

VenueSoftware · 2022
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsSimon Fraser UniversityBritish Columbia Institute of TechnologyLangara College
Fundersnot available
KeywordsPython (programming language)Computer scienceFortranCode refactoringSoftware portabilityProgramming languageSoftwareCoding (social sciences)Software engineering

Abstract

fetched live from OpenAlex

With archaic coding techniques, there will be a time when it will be necessary to modernize vulnerable software. However, redeveloping out-of-date code can be a time-consuming task when dealing with a multitude of files. To reduce the amount of reassembly for Fortran-based projects, in this paper, we develop a prototype for automating the manual labor of refactoring individual files. ForDADT (Fortran Dynamic Autonomous Diagnostic Tool) project is a Python program designed to reduce the amount of refactoring necessary when compiling Fortran files. In this paper, we demonstrate how ForDADT is used to automate the process of upgrading Fortran codes, process the files, and automate the cleaning of compilation errors. The developed tool automatically updates thousands of files and builds the software to find and fix the errors using pattern matching and data masking algorithms. These modifications address the concerns of code readability, type safety, portability, and adherence to modern programming practices.

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)0.000
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.727
Threshold uncertainty score0.536

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)0.0000.000

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 head0.019
GPT teacher head0.301
Teacher spread0.283 · 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