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Automated Patching for Unreproducible Builds

Zhilei Ren, Shiwei Sun, Jifeng Xuan, Xiaochen Li, Zhide Zhou, He Jiang

2022Year
18Citations
4Top-tier citations

Abstract

Software reproducibility plays an essential role in establishing trust between source code and the built artifacts, by comparing compilation outputs acquired from independent users. Although the testing for unreproducible builds could be automated, fixing unreproducible build issues poses a set of challenges within the reproducible builds practice, among which we consider the localization granularity and the historical knowledge utilization as the most significant ones. To tackle these challenges, we propose a novel approach RepFix that combines tracing-based fine-grained localization with history-based patch generation mechanisms. On the one hand, to tackle the localization granularity challenge, we adopt system-level dynamic tracing to capture both the system call traces and user-space function call information. By integrating the kernel probes and user-space probes, we could determine the location of each executed build command more accurately. On the other hand, to tackle the historical knowledge utilization challenge, we design a similarity based relevant patch retrieving mechanism, and generate patches by applying the edit operations of the existing patches. With the abundant patches accumulated by the reproducible builds practice, we could generate patches to fix the unreproducible builds automatically. To evaluate the usefulness of RepFix, extensive experiments are conducted over a dataset with 116 real-world packages. Based on RepFix, we successfully fix the unreproducible build issues for 64 packages. Moreover, we apply RepFix to the Arch Linux packages,

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