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Record W2016521958 · doi:10.1145/1094855.1094875

The crisis in systems code maintenance

2005· article· en· W2016521958 on OpenAlexaff
Rebeca Roe Dunn-Krahn, Jessica Maple, Yvonne Coady

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceMainstreamCode (set theory)Legacy systemSoftware engineeringWorld Wide WebComputer securityProgramming languageArtificial intelligencePolitical scienceLaw

Abstract

fetched live from OpenAlex

Linguistic support for modern programming paradigms has not been welcomed into most of today's mainstream operating systems. Linus Torvalds has decreed that Linux will never again entertain C++, saying In fact, in Linux we did try C++ once already, back in 1992. It sucks. Trust me, writing kernel code in C++ is a bloody stupid idea Similarly, Pantelis Antoniou, an embedded PowerPC kernel developer, has captured popular systems-sentiment about aspect-orientation, People like to live in denial; thinking that programming shouldn't be this hard right? There must be an easier way, if only those pesky developers followed fashionable_methodology_of_the_day As a consequence, though a number of systems have been progressively restructuring services to leverage higher-level paradigms, it is intentionally done without language support. This decoupling of paradigms and language mechanisms appears to suggest that conventional wisdom in the systems community prejudices modern programming methodologies because they may unnecessarily heavyweight manifestations of paradigms, and pollute otherwise elegant and optimized hand-crafted C code. Simply put, if it ain't broke, don't fix it. We believe there is a growing body of evidence to suggest that, if the systems community continues to refuse support for a paradigm shift, system evolution will slow down to an unacceptable level. Already, valuable code is not being integrated into systems in a timely fashion because the tools meant to facilitate this can, and often do, impede the process. Simply put, it is broke and we believe we know how to fix it.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.032
metaresearch head score (Gemma)0.109
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.167

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.109
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0040.013
Scholarly communication0.0100.027
Open science0.0030.007
Research integrity0.0070.013
Insufficient payload (model declined to judge)0.0090.006

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 head0.032
GPT teacher head0.288
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

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Citations1
Published2005
Admission routes1
Has abstractyes

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