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Synthesizing Object State Transformers for Dynamic Software Updates.

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Názov: Synthesizing Object State Transformers for Dynamic Software Updates.
Autori: Zelin Zhao, Yanyan Jiang, Chang Xu, Tianxiao Gu, Xiaoxing Ma
Zdroj: ICSE: International Conference on Software Engineering; 5/22/2021, p1111-1122, 12p
Predmety: ARTIFICIAL intelligence, SOFTWARE engineers, SOFTWARE maintenance, SOFTWARE reengineering, PROGRAMMING languages
Abstrakt: There is an increasing demand for evolving software systems to deliver continuous services of no restart. Dynamic software update (DSU) aims to achieve this goal by patching the system state on the fly but is currently hindered from practice due to non-trivial cross-version object state transformations. This paper revisits this problem through an in-depth empirical study of over 190 class changes from Tomcat 8. The study produced an important finding that most non-trivial object state transformers can be constructed by reassembling existing old/new version code snippets. This paper presents a domain-specific language and an efficient algorithm for synthesizing non-trivial object transformers over code reuse. We experimentally evaluated our tool implementation PASTA with real-world software systems, reporting PASTA's effectiveness in succeeding in 7.5_ non-trivial object transformation tasks compared with the best existing DSU techniques. [ABSTRACT FROM AUTHOR]
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Abstrakt:There is an increasing demand for evolving software systems to deliver continuous services of no restart. Dynamic software update (DSU) aims to achieve this goal by patching the system state on the fly but is currently hindered from practice due to non-trivial cross-version object state transformations. This paper revisits this problem through an in-depth empirical study of over 190 class changes from Tomcat 8. The study produced an important finding that most non-trivial object state transformers can be constructed by reassembling existing old/new version code snippets. This paper presents a domain-specific language and an efficient algorithm for synthesizing non-trivial object transformers over code reuse. We experimentally evaluated our tool implementation PASTA with real-world software systems, reporting PASTA's effectiveness in succeeding in 7.5_ non-trivial object transformation tasks compared with the best existing DSU techniques. [ABSTRACT FROM AUTHOR]
DOI:10.1109/ICSE43902.2021.00103