MétaCan
Menu
Back to cohort
Record W4231261295 · doi:10.32920/ryerson.14663895

Whole program analysis of Java programs for virtual calls and exception handling

2021· preprint· en· W4231261295 on OpenAlexaff
Simrandeep Nagra

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCall stackJavaComputer scienceException handlingStack (abstract data type)Static analysisOperating systemVirtual machineProgram analysisCode (set theory)strictfpProgramming languageReal time Java

Abstract

fetched live from OpenAlex

Java Programs suffer performance degradation due to the presence of virtual calls and the lack of an efficient exception handling mechanism. In this dissertation, we show how virtual calls can be statically resolved to one or two target methods. The resolved calls can then be potentially inlined and hence improve the performance of the program. Analyzing the whole program (including the Java runtime library) instead of only user code has a positive effect on the performance of the program. We present two exception handling mechanisms, Direct Path Analysis and Display Catch Exception Handling, that improve the performance of programs as compared to the existing popular techniques, Stack Unwinding and Stack Cutting. The first analysis shows that the number of the stack frames needed to be unwound is lower in our analysis than Stack Unwinding. In the second analysis, we propose the Display Catch Exception Handling mechanism which is better than Stack Cutting in terms of operations required to catch exceptions.

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 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: Methods
Teacher disagreement score0.902
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.031
GPT teacher head0.310
Teacher spread0.279 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicParallel Computing and Optimization TechniquesFrench-language works237,207