Name
Abstract | ||
|---|---|---|
Dynamic analyses rely on the ability to identify points within or across executions. In spite of this being a core task for dynamic analyses, new solutions are frequently developed without an awareness of existing solutions, their strengths, their weaknesses, or their caveats. This paper surveys the existing approaches for identifying execution points and examines their analytical and empirical properties that researchers and developers should be aware of when using them within an analysis. In addition, based on limitations in precision, correctness, and efficiency for techniques that identify corresponding execution points across multiple executions, we designed and implemented a new technique, Precise Execution Point IDs. This technique avoids correctness and precision issues in prior solutions, enabling analyses that use our approach to also produce more correct results. Empirical comparison with the surveyed techniques shows that our approach has 25% overhead on average, several times less than existing solutions.
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Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ASE.2013.6693069 | Automated Software Engineering |
Keywords | Field | DocType |
system monitoring,dynamic analysis,execution points identification,precise execution point ID,precision | Empirical comparison,Data mining,Computer science,Correctness,Runtime verification,System monitoring,Spite | Conference |
ISSN | ISBN | Citations |
1527-1366 | 978-1-4799-0215-6 | 1 |
PageRank | References | Authors |
0.35 | 20 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| William N. Sumner | 1 | 19 | 1.00 |
| Xiangyu Zhang | 2 | 2857 | 151.00 |