Technical Note
Version 1
This version is not peer-reviewed
UE4 Redundant Asset Detection Method Based on Pointer Analysis
Version 1
: Received: 14 September 2024 / Approved: 16 September 2024 / Online: 18 September 2024 (05:05:40 CEST)
How to cite: Liu, T. UE4 Redundant Asset Detection Method Based on Pointer Analysis. Preprints 2024, 2024091236. https://doi.org/10.20944/preprints202409.1236.v1 Liu, T. UE4 Redundant Asset Detection Method Based on Pointer Analysis. Preprints 2024, 2024091236. https://doi.org/10.20944/preprints202409.1236.v1
Abstract
In the process of game development, identifying and deleting currently abandoned assets as content iterates can effectively reduce the size of game packages. But the built-in reference checking tool in UE4 can only check static references of assets and cannot identify dynamically referenced resources in the program. We have developed a static analysis tool for analyzing unused assets in the UE4 project to address this issue. This tool checks the function parameters of all loading points, analyzes the value range of the parameter string based on the data dependency relationship of the actual parameter variable, and considers that the assets that match within the range are referenced. Due to the fact that in some cases, the exact data stream of the actual parameter variable is not computable (resulting in false negatives), the reverse analysis tool supports manually marking the parameter range at the loading point. This tool can generate asset collections that are dynamically referenced. The union of its results with the built-in reference checking tool in UE4 is the set of all referenced assets. The difference set between all asset sets and the referenced asset set is the abandoned asset set. This achieves a more complete cleaning of redundant resources and reduces the size of game packages.
Keywords
program analysis; static analysis; pointer analysis; data flow analysis; flow sensitivity; game develop
Subject
Computer Science and Mathematics, Software
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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