.png){kind=icon}
返回
Dynamically discovering likely program invariants to support program evolution
.png){kind=icon}
PRE
AI
DOI：10.1109/32.908957.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Explicitly stated program invariants can help programmers by identifying program properties that must be preserved when modifying code. In practice, however, these invariants are usually implicit. An alternative to expecting programmers to fully annotate code with invariants is to automatically infer likely invariants from the program itself. This research focuses on dynamic techniques for discovering invariants from execution traces. This article reports three results. First, it describes techniques for dynamically discovering invariants, along with an implementation, named Daikon, that embodies these techniques. Second, it reports on the application of Daikon to two sets of target programs. In programs from Gries's work on program derivation, the system rediscovered predefined invariants. In a C program lacking explicit invariants, the system discovered invariants that assisted a software evolution task. These experiments demonstrate that, at least for small programs, invariant inference is both accurate and useful. Third, it analyzes scalability issues, such as invariant detection runtime and accuracy, as functions of test suites and program points instrumented.
Keyword:
program invariants
formal specification
software evolution
dynamic analysis
execution traces
logical inference
pattern recognition
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:
5.6
论文数:
2.8K
被引数:
1.1W
机构
暂无机构信息
引用论文
Consolidation patterns during initiation and evolution of a plate-boundary decollement zone: Northern Barbados accretionary prism.png)
Geology
GeologyIF0
PREAI
分享收藏分享收藏
分享收藏
查看更多内容