IDE <sup> <i>al</i> </sup> : efficient and precise alias-aware dataflow analysis
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
Program analyses frequently track objects throughout a program, which requires reasoning about aliases. Most dataflow analysis frameworks, however, delegate the task of handling aliases to the analysis clients, which causes a number of problems. For instance, custom-made extensions for alias analysis are complex and cannot easily be reused. On the other hand, due to the complex interfaces involved, off-the-shelf alias analyses are hard to integrate precisely into clients. Lastly, for precision many clients require strong updates, and alias abstractions supporting strong updates are often relatively inefficient. In this paper, we present IDEal, an alias-aware extension to the framework for Interprocedural Distributive Environment (IDE) problems. IDEal relieves static-analysis authors completely of the burden of handling aliases by automatically resolving alias queries on-demand, both efficiently and precisely. IDEal supports a highly precise analysis using strong updates by resorting to an on-demand, flow-sensitive, and context-sensitive all-alias analysis. Yet, it achieves previously unseen efficiency by propagating aliases individually, creating highly reusable per-pointer summaries. We empirically evaluate IDEal by comparing TSf, a state-of-the-art typestate analysis, to TSal, an IDEal-based typestate analysis. Our experiments show that the individual propagation of aliases within IDEal enables TSal to propagate 10.4x fewer dataflow facts and analyze 10.3x fewer methods when compared to TSf. On the DaCapo benchmark suite, TSal is able to efficiently compute precise results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.003 |
| 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