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Record W3014903559 · doi:10.1145/1449955.1449790

Enabling static analysis for partial java programs

2008· article· en· W3014903559 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 SIGPLAN Notices · 2008
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceProgramming languageJavaSource codeStatic analysisPartial evaluationClass (philosophy)Type inferenceTheoretical computer scienceInferenceArtificial intelligence

Abstract

fetched live from OpenAlex

Software engineering tools often deal with the source code of programs retrieved from the web or source code repositories. Typically, these tools only have access to a subset of a program's source code (one file or a subset of files) which makes it difficult to build a complete and typed intermediate representation (IR). Indeed, for incomplete object-oriented programs, it is not always possible to completely disambiguate the syntactic constructs and to recover the declared type of certain expressions because the declaration of many types and class members are not accessible. We present a framework that performs partial type inference and uses heuristics to recover the declared type of expressions and resolve ambiguities in partial Java programs. Our framework produces a complete and typed IR suitable for further static analysis. We have implemented this framework and used it in an empirical study on four large open source systems which shows that our system recovers most declared types with a low error rate, even when only one class is accessible.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.761
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.069
GPT teacher head0.304
Teacher spread0.235 · 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