Context-, flow-, and field-sensitive data-flow analysis using synchronized Pushdown systems
Bibliographic record
Abstract
Precise static analyses are context-, field- and flow-sensitive. Context- and field-sensitivity are both expressible as context-free language (CFL) reachability problems. Solving both CFL problems along the same data-flow path is undecidable, which is why most flow-sensitive data-flow analyses over-approximate field-sensitivity through k -limited access-path, or through access graphs. Unfortunately, as our experience and this paper show, both representations do not scale very well when used to analyze programs with recursive data structures. Any single CFL-reachability problem is efficiently solvable, by means of a pushdown system. This work thus introduces the concept of synchronized pushdown systems (SPDS). SPDS encode both procedure calls/returns and field stores/loads as separate but “synchronized” CFL reachability problems. An SPDS solves both individual problems precisely, and approximation occurs only in corner cases that are apparently rare in practice: at statements where both problems are satisfied but not along the same data-flow path. SPDS are also efficient: formal complexity analysis shows that SPDS shift the complexity from | F | 3 k under k -limiting to | S || F | 2 , where F is the set of fields and S the set of statements involved in a data-flow. Our evaluation using DaCapo shows this shift to pay off in practice: SPDS are almost as efficient as k -limiting with k =1 although their precision equals k =∞. For a typestate analysis SPDS accelerate the analysis up to 83× for data-flows of objects that involve many field accesses but span rather few methods. We conclude that SPDS can provide high precision and further improve scalability, in particularly when used in analyses that expose rather local data flows.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".