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Jyh-Shiarn Yur

dblp:76/286 · DBLP profile ↗
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4ranked-venue papers
2as first author
0since 2021 · last 1999
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 2 · 2 first-authorSystems, architecture and hardware · 1Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
2 papers
Program analysis · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Program analysis › static analysis
incremental analysis
0.021999
An Incremental Flow- and Context-Sensitive Pointer Aliasing Analysis · ICSE 1999
Incremental Analysis of Side Effects for C Software System · ICSE 1997
Program analysis › static analysis
pointer analysis
0.011999
An Incremental Flow- and Context-Sensitive Pointer Aliasing Analysis · ICSE 1999
Program analysis › effect analysis
side-effect analysis
0.011997
Incremental Analysis of Side Effects for C Software System · ICSE 1997
Program analysis › data flow analysis
flow-sensitive analysis
0.011999
An Incremental Flow- and Context-Sensitive Pointer Aliasing Analysis · ICSE 1999
Program analysis › static analysis
interprocedural analysis
0.011997
Incremental Analysis of Side Effects for C Software System · ICSE 1997

Methods — techniques the papers use, named apart from their topics

incremental algorithm · 0.0hybrid method · 0.0
YearPublicationVenuePosition
1999 An Incremental Flow- and Context-Sensitive Pointer Aliasing Analysis
abstract
Pointer aliasing analysis is used to determine if two object names containing dereferences and/or field selectors, (e.g., *p,q->t), may refer to the same location during execution.Such information is necessary for applications such as dataflow-based testers, program understanding tools, and debuggers, but is expensive to calculate with acceptable precision.Incremental algorithms update data flow information after a program change rather than recomputing it from scratch, under the assumption that the change impact will be limited.Two versions of a practical incremental pointer aliaaing algorithm have been developed, based on Land&Ryder flow-and context-sensitive alias analysis.Empirical results attest to the time savings over exhaustive analysis (a six-fold speedup on average), and the precision of the approximate solution obtained (on average same solution as exhaustive algorithm for 75% of the tests.)
Jyh-Shiarn Yur, Barbara G. Ryder, William Landi
ICSE1
1997 Incremental Analysis of Side Effects for C Software System
abstract
Incremental static analysis seeks to efficiently update semantic information about an evolving software system, without recomputing “from scratch.” Interprocedural modification side effect analysis (MOD) calculates the set of variables possibly modified by execution of a procedure or a statement. We introduce a partial incrementalization of MOD for C systems using the hybrid method and present results of a study of 27 C programs, that predicts that our incremental MOD analysis will be substantially cheaper than exhaustive analysis for many program changes.
Jyh-Shiarn Yur, Barbara G. Ryder, William Landi, Phil Stocks
ICSE1
1993 Optimal assignment of task modules with precedence in distributed computing systems
Gen-Huey Chen, Jyh-Shiarn Yur
Inf. Sci.2
1990 A Branch-and-Bound-with-Underestimates Algorithm for the Task Assignment Problem with Precedence Constraint
abstract
The problem of finding an optimal assignment of task modules with a precedence relationship in a distributed computing system is considered. The objective of task assignment is to minimize the task turnaround time. The problem is known to be NP-complete for more than three processors. To solve the problem, a well-known state-space reduction technique, branch-and-bound-with-underestimates, is applied, and two underestimate functions are defined. Through experiments, their effectiveness is shown by comparing the proposed algorithm with both Wang and Tsai's (1988) algorithm and the A* algorithm with h(x)=0.>
Gen-Huey Chen, Jyh-Shiarn Yur
ICDCS2