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Record W3094250247 · doi:10.1145/3382494.3422165

On the use of C# Unsafe Code Context

2020· article· en· W3094250247 on OpenAlexaff
Ehsan Firouzi, Ashkan Sami, Foutse Khomh, Gias Uddin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSecurity and Verification in Computing
Canadian institutionsUniversity of CalgaryPolytechnique Montréal
Fundersnot available
KeywordsComputer scienceComputer securityContext (archaeology)Memory safetyDead codeInteroperabilityPointer (user interface)Unreachable codeSecure codingProgramming languageRedundant codeDatabaseOperating systemSoftwareCode generationSoftware security assuranceComputer hardwareInformation security

Abstract

fetched live from OpenAlex

Background. C# maintains type safety and security by not allowing direct dangerous pointer arithmetic. To improve performance for special cases, pointer arithmetic is provided via an unsafe context. Programmers can use the C# unsafe keyword to encapsulate a code block, which can use pointer arithmetic. In the Common Language Runtime (CLR), unsafe code is referred to as unverifiable code. It then becomes the responsibility of the programmer to ensure the encapsulated code snippet is not dangerous. Naturally, this raises concern on whether such trust is misused by programmers when they promote the use of C# unsafe context. Aim. We aim to analyze the prevalence and vulnerabilities of share code examples using C# unsafe keyword in Stack Overflow (SO) code sharing platform. Method. By using some regular expressions and manual checks, we extracted C# unsafe code relevant posts from SO and categorized them into some software development scenarios. Results. In the entire SO data dump of September 2018, we find 2,283 C# snippets with the unsafe keyword. Among those posts, 27% of posts are about Image processing, where unsafe codes are mainly used for performance reasons. The second most popular category by 21% of the codes in the posts is used for 'Interoperability' reasons. That is 'unsafe' is used to enable 'Interoperability' between C# managed codes and unmanaged codes. The 'stackalloc' operator is the third category with 9% of unsafe code posts. The stackalloc operator allocates a block of memory on the stack. Since C# 7.2, Microsoft recommends against using 'stackalloc' in unsafe context whenever possible. Manual inspection shows 67 code snippets with dangerous functions that can introduce vulnerability if not used with caution (e.g., buffer overflow). Finally, 35% of 'Interoperability' posts have 'P/Invoke' tag were used outside NativeMethods class, which is in contrast to Microsoft design suggestion. Conclusion. Our study leads to 7 main findings, and these findings show the importance of cautiously using this feature.

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)0.002
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.004
Science and technology studies0.0010.002
Scholarly communication0.0010.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.215
GPT teacher head0.271
Teacher spread0.056 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations3
Published2020
Admission routes1
Has abstractyes

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