.png){kind=icon}
返回
An empirical study on constraint optimization techniques for test generation
.png){kind=icon}
PRE
AI
DOI：10.1007/s11432-015-0450-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Constraint solving is a frequent, but expensive operation with symbolic execution to generate tests for a program. To improve the efficiency of test generation using constraint solving, four optimization techniques are usually applied to existing constraint solvers, which are constraint independence, constraint set simplification, constraint caching, and expression rewriting. In this paper, we conducted an empirical study, using these four constraint optimization techniques in a well known test generation tool KLEE with 77 GNU Coreutils applications, to systematically investigate how these optimization techniques affect the efficiency of test generation. The experimental results show that these constraint optimization techniques as well as their combinations cannot improve the efficiency of test generation significantly for ALL-SIZED programs. Moreover, we studied the constraint optimization techniques with respect to two static metrics, lines of code (LOC) and cyclomatic complexity (CC), of programs. The experimental results show that the constraint set simplification technique can improve the efficiency of test generation significantly for the programs with high LOC and CC values. The constraint caching optimization technique can improve the efficiency of test generation significantly for the programs with low LOC and CC values. Finally, we propose four hybrid optimization strategies and practical guidelines based on different static metrics.
Keyword:
test generation
symbolic execution
constraint solving
constraint optimization
static metric
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
4.9K
被引数:
8.9K
机构
引用论文
分享收藏Lighting to Make You Feel Better: Improving the Mood of Elderly People with Affective Ambiences.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏
查看更多内容