Towards an understanding of memory leak patterns: an empirical study in Python
Authors: 
Jie Chen, Dongjin Yu, Haiyang HuAuthors Info & Claims
Pages 1303 - 1330
Abstract
Memory leaks, an important and difficult issue in software development, occur when an object is inadvertently retained longer than necessary. Programming languages provide a variety of dynamic memory management methods to support programmers in preventing the introduction of defects that cause memory leaks. However, it is not yet possible to completely free programmers from the work of memory management. Indeed, runtime leak detection is time consuming and usually done after the fact, while manual code inspection requires rich developer experience. Understanding the common patterns of memory leaks can help developers be mindful of leaks or avoid them at an earlier stage during the development process and may further inspire future research. Eight code patterns are found in our case study specifically for memory leaks caused by circular references in Python. The observed patterns can explain 91.64% of the memory leaks in the studied projects. Our work can guide important decisions about the possibility of identifying memory leaks with static code analysis.
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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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Kluwer Academic Publishers
United States
Publication History
Published: 17 June 2023
Accepted: 18 May 2023
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- National Science Foundation of China
- Zhejiang Provincial National Science Foundation of China
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