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Record W3133304533 · doi:10.1109/tse.2021.3058985

A Study of C/C++ Code Weaknesses on Stack Overflow

2021· article· en· W3133304533 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

VenueIEEE Transactions on Software Engineering · 2021
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsQueen's UniversityPolytechnique MontréalUniversity of ManitobaConcordia UniversityHuawei Technologies (Canada)
Fundersnot available
KeywordsNotationComputer scienceCode (set theory)Stack (abstract data type)Programming languageMathematical notationSoftwareTheoretical computer scienceMathematicsArithmetic

Abstract

fetched live from OpenAlex

Stack Overflow hosts millions of solutions that aim to solve developers’ programming issues. In this crowdsourced question answering process, Stack Overflow becomes a code hosting website where developers actively share its code. However, code snippets on Stack Overflow may contain security vulnerabilities, and if shared carelessly, such snippets can introduce security problems in software systems. In this paper, we empirically study the prevalence of the <i>Common Weakness Enumeration</i> – CWE, in code snippets of C/C++ related answers. We explore the characteristics of <inline-formula><tex-math notation="LaTeX">$Code_w$</tex-math></inline-formula> , i.e., code snippets that have CWE instances, in terms of the types of weaknesses, the evolution of <inline-formula><tex-math notation="LaTeX">$Code_w$</tex-math></inline-formula> , and who contributed such code snippets. We find that: 1) 36 percent (i.e., 32 out of 89) CWE types are detected in <inline-formula><tex-math notation="LaTeX">$Code_w$</tex-math></inline-formula> on Stack Overflow. Particularly, CWE-119, i.e., <i>improper restriction of operations within the bounds of a memory buffer</i> , is common in both answer code snippets and real-world software systems. Furthermore, the proportion of <inline-formula><tex-math notation="LaTeX">$Code_w$</tex-math></inline-formula> doubled from 2008 to 2018 after normalizing by the total number of C/C++ snippets in each year. 2) In general, code revisions are associated with a reduction in the number of code weaknesses. However, the majority of <inline-formula><tex-math notation="LaTeX">$Code_w$</tex-math></inline-formula> had weaknesses introduced in the first version of the code, and these <inline-formula><tex-math notation="LaTeX">$Code_w$</tex-math></inline-formula> were never revised since then. Only 7.5 percent of users who contributed C/C++ code snippets posted or edited code with weaknesses. Users contributed less code with CWE weakness when they were more active (i.e., they either revised more code snippets or had a higher reputation). We also find that some users tended to have the same CWE type repeatedly in their various code snippets. Our empirical study provides insights to users who share code snippets on Stack Overflow so that they are aware of the potential security issues. To understand the community feedback about improving code weaknesses by answer revisions, we also conduct a qualitative study and find that 62.5 percent of our suggested revisions are adopted by the community. Stack Overflow can perform CWE scanning for all the code that is hosted on its platform. Further research is needed to improve the quality of the crowdsourced knowledge on Stack Overflow.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.837
Threshold uncertainty score1.000

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 head0.024
GPT teacher head0.265
Teacher spread0.241 · 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