Effective Static Analysis to Find Concurrency Bugs in Java
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.
Bibliographic record
Abstract
Multithreading and concurrency are core features of the Java language. However, writing a correct concurrent program is notoriously difficult and error prone. Therefore, developing effective techniques to find concurrency bugs is very important. Existing static analysis techniques for finding concurrency bugs either sacrifice precision for performance, leading to many false positives, or require sophisticated analysis that incur significant overhead. In this paper, we present a precise and efficient static concurrency bugs detector building upon the Eclipse JDT and the open source WALA toolkit (which provides advanced static analysis capabilities). Our detector uses different implementation strategies to consider different types of concurrency bugs. We either utilize JDT to syntactically examine source code, or leverage WALA to perform interprocedural data flow analysis. We describe a variety of novel heuristics and enhancements to existing analysis techniques which make our detector more practical, in terms of accuracy and performance. We also present an effective approach to create inter-procedural data flow analysis using WALA for complex analysis. Finally we justify our claims by presenting the results of applying our detector to a range of real-world applications and comparing our detector with other tools.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it