Abstract
We present a sparse evaluation technique that is effectively applicable to a set of elaborate semantic-based static analyses. Existing sparse evaluation techniques are effective only when the underlying analyses have comparably low precision. For example, if a pointer analysis precision is not affected by numeric statements like x:=1 then existing sparse evaluation techniques can remove the statement, but otherwise, the statement cannot be removed. Our technique, which is a fine-grained sparse evaluation technique, is effectively applicable even to elaborate analyses. A key insight of our technique is that, even though a statement is relevant to an analysis, it is typical that analyzing the statement involves only a tiny subset of its input abstract memory and the most are irrelevant. By exploiting this sparsity, our technique transforms the original analysis into a form that does not involve the fine-grained irrelevant semantic behaviors. We formalize our technique within the abstract interpretation framework. In experiments with a C static analyzer, our technique improved the analysis speed by on average 14x.
| Original language | English |
|---|---|
| Pages (from-to) | 99-111 |
| Number of pages | 13 |
| Journal | Computer Languages, Systems and Structures |
| Volume | 40 |
| Issue number | 3-4 |
| DOIs | |
| Publication status | Published - 2014 Oct 1 |
| Externally published | Yes |
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Keywords
- Abstract interpretation
- Data-flow analysis
- Sparse evaluation
- Static analysis
ASJC Scopus subject areas
- Software
- Computer Networks and Communications
Cite this
A sparse evaluation technique for detailed semantic analyses. / Ko, Yoonseok; Heo, Kihong; Oh, Hakjoo.
In: Computer Languages, Systems and Structures, Vol. 40, No. 3-4, 01.10.2014, p. 99-111.Research output: Contribution to journal › Article
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TY - JOUR
T1 - A sparse evaluation technique for detailed semantic analyses
AU - Ko, Yoonseok
AU - Heo, Kihong
AU - Oh, Hakjoo
PY - 2014/10/1
Y1 - 2014/10/1
N2 - We present a sparse evaluation technique that is effectively applicable to a set of elaborate semantic-based static analyses. Existing sparse evaluation techniques are effective only when the underlying analyses have comparably low precision. For example, if a pointer analysis precision is not affected by numeric statements like x:=1 then existing sparse evaluation techniques can remove the statement, but otherwise, the statement cannot be removed. Our technique, which is a fine-grained sparse evaluation technique, is effectively applicable even to elaborate analyses. A key insight of our technique is that, even though a statement is relevant to an analysis, it is typical that analyzing the statement involves only a tiny subset of its input abstract memory and the most are irrelevant. By exploiting this sparsity, our technique transforms the original analysis into a form that does not involve the fine-grained irrelevant semantic behaviors. We formalize our technique within the abstract interpretation framework. In experiments with a C static analyzer, our technique improved the analysis speed by on average 14x.
AB - We present a sparse evaluation technique that is effectively applicable to a set of elaborate semantic-based static analyses. Existing sparse evaluation techniques are effective only when the underlying analyses have comparably low precision. For example, if a pointer analysis precision is not affected by numeric statements like x:=1 then existing sparse evaluation techniques can remove the statement, but otherwise, the statement cannot be removed. Our technique, which is a fine-grained sparse evaluation technique, is effectively applicable even to elaborate analyses. A key insight of our technique is that, even though a statement is relevant to an analysis, it is typical that analyzing the statement involves only a tiny subset of its input abstract memory and the most are irrelevant. By exploiting this sparsity, our technique transforms the original analysis into a form that does not involve the fine-grained irrelevant semantic behaviors. We formalize our technique within the abstract interpretation framework. In experiments with a C static analyzer, our technique improved the analysis speed by on average 14x.
KW - Abstract interpretation
KW - Data-flow analysis
KW - Sparse evaluation
KW - Static analysis
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U2 - 10.1016/j.cl.2014.05.001
DO - 10.1016/j.cl.2014.05.001
M3 - Article
VL - 40
SP - 99
EP - 111
JO - Journal of Computer Languages
JF - Journal of Computer Languages
SN - 2665-9182
IS - 3-4
ER -