An Value Range Analysis Based on Abstract Interpretation and Generalized Monotone Data Flow Framework

Ji Mengluo, Wang Huaimin, Li Mengjun, Dong Wei, and Qi Zhichang

Journal of Computer Research and Development > 2006 > 43(11): 2020-2026.

Ji Mengluo, Wang Huaimin, Li Mengjun, Dong Wei, and Qi Zhichang. An Value Range Analysis Based on Abstract Interpretation and Generalized Monotone Data Flow Framework[J]. Journal of Computer Research and Development, 2006, 43(11): 2020-2026.

Citation:

PDF (587 KB)

An Value Range Analysis Based on Abstract Interpretation and Generalized Monotone Data Flow Framework

Ji Mengluo, Wang Huaimin, Li Mengjun, Dong Wei, and Qi Zhichang

(School of Computer Science, National University of Defense Technology, Changsha 410073)

More Information

Published Date: November 14, 2006

Graphical Abstract

Abstract

Abstract

Safe and accurate value range analysis is crucial for compiler optimization. Based on abstract interpretation and generalized monotone data flow framework, a complete framework for value range analysis is proposed in this paper. Different from other value range analysis methods, this framework includes complete definitions, analysis and correctness proofs. Compared with general theory about abstract interpretation, the method focuses on value range analysis, so the analysis and the proof of the analysis is straightforward.
- value range analysis,
- abstract interpretation,
- compiler optimization,
- software engineering

FullText(HTML)

References (0)

[1]	Zhou Peng, Wu Yanjun, Zhao Chen. A Programming Paradigm Combining Programmer and Neural Network to Promote Automated Program Generation[J]. Journal of Computer Research and Development, 2021, 58(3): 638-650. DOI: 10.7544/issn1000-1239.2021.20200298
[2]	Xu Lixin, Wu Huayao. Collective Intelligence Based Software Engineering[J]. Journal of Computer Research and Development, 2020, 57(3): 487-512. DOI: 10.7544/issn1000-1239.2020.20190626
[3]	Liu Hui, Xu Jinlong, Zhao Rongcai, Yao Jinyang. Compiler Optimization Sequence Selection Method Based on Learning Model[J]. Journal of Computer Research and Development, 2019, 56(9): 2012-2026. DOI: 10.7544/issn1000-1239.2019.20180789
[4]	Liu Fang, Li Ge, Hu Xing, Jin Zhi. Program Comprehension Based on Deep Learning[J]. Journal of Computer Research and Development, 2019, 56(8): 1605-1620. DOI: 10.7544/issn1000-1239.2019.20190185
[5]	Xiao Mingming, Yu Shunzheng. Protocol Reverse Engineering Using Grammatical Inference[J]. Journal of Computer Research and Development, 2013, 50(10): 2044-2058.
[6]	Chen Xiangping, Huang Gang, Sun Yanchun, and Mei Hong. A Software Architecture Model Supporting Analysis and Planning in Self-Adaptation[J]. Journal of Computer Research and Development, 2010, 47(7): 1156-1164.
[7]	Tao Qiuming, Zhao Chen, Wang Yongji. An Automated Method of Test Program Generation for Compiler Optimizations Based on Process Graph[J]. Journal of Computer Research and Development, 2009, 46(9): 1567-1577.
[8]	Lü Jinhu, Wang Hongchun, He Keqing. Complex Dynamical Networks and Their Applications in Software Engineering[J]. Journal of Computer Research and Development, 2008, 45(12): 2052-2059.
[9]	Mao Xinjun, Chang Zhiming, Wang Ji, and Wang Huaimin. Agent-Oriented Software Engineering: Status and Challenges[J]. Journal of Computer Research and Development, 2006, 43(10): 1782-1789.
[10]	Lei Ting, Li Xi, and Zhou Xuehai. Performance Lossless Voltage Scheduling for Low Energy Software[J]. Journal of Computer Research and Development, 2006, 43(6): 1090-1096.