Selecting Context-Sensitivity Modularly for Accelerating Object-Sensitive Pointer 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
Object-sensitive pointer analysis (denoted <i>k</i> <small>obj</small> under <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -limiting) for an object-oriented program can be accelerated if context-sensitivity can be selectively applied to only some precision-critical variables/objects in a program. Existing pre-analyses for making such selections, which are performed as whole-program analyses to a program, are developed based on two broad approaches. One approach preserves the precision of object-sensitive pointer analysis but achieves limited speedups by reasoning about all the possible value flows in the program conservatively, while the other approach achieves greater speedups but sacrifices precision (often unduly) by examining only some but not all the value flows in the program heuristically. In this paper, we introduce a new pre-analysis approach, <small>Turner</small> <inline-formula><tex-math notation="LaTeX">$^{\mathcal{m}}$</tex-math></inline-formula> (where <inline-formula><tex-math notation="LaTeX">$\mathcal {m}$</tex-math></inline-formula> stands for modularity), that represents a sweet spot between these two existing ones, as it is designed to enable <i>k</i> <small>obj</small> to run significantly faster than the former approach and achieve significantly better precision than the latter approach. <small>Turner</small> <inline-formula><tex-math notation="LaTeX">$^{\mathcal{m}}$</tex-math></inline-formula> is simple, lightweight yet effective due to two novel aspects in its design. First, we exploit a key observation that some precision-uncritical objects in the program can be approximated based on the object-containment relationship pre-established (from Andersen's analysis). In practice, this approximation introduces only a small degree of imprecision into <i>k</i> <small>obj</small> . Second, leveraging this initial approximation, we apply a novel object reachability analysis to the program by pre-analyzing its methods according to a reverse topological order of its call graph. When pre-analyzing each method, we make use of a simple DFA (Deterministic Finite Automaton) to reason about object reachability intra-procedurally from its entry to its exit along all the possible value flows established by its statements to identify its precision-critical variables/objects. In practice, this new modular object reachability analysis, which runs linearly in terms of the number of statements in the program, introduces again only a small loss of precision into <i>k</i> <small>obj</small> . We have validated <small>Turner</small> <inline-formula><tex-math notation="LaTeX">$^{\mathcal{m}}$</tex-math></inline-formula> with an open-source implementation in <small>Soot</small> (already publicly available) against the state of the art by using a set of 12 widely used Java benchmarks and applications.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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