Effective Static Analysis to Find Concurrency Bugs in Java
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.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".