检测错误的构建规则

N. Licker, A. Rice
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引用次数: 9

摘要

软件工程师经常使用自动化构建系统,以尽量减少在对项目源文件进行增量更改后需要重新编译的对象的数量。为了实现高效和正确的构建,开发人员必须向构建工具提供项目的文件和模块之间的依赖信息,这些信息通常用特定于每个构建工具的宏语言表示。为了保证正确性,这些工具的作者负责枚举输出所依赖的所有文件的内容。不幸的是,这是一个乏味的过程,并且在实践中并不是所有的依赖都被捕获,这将导致不正确的构建。我们通过一种称为构建模糊的新方法自动发现这些缺失的依赖关系。通过修改项目中的文件、触发增量构建并将更改的文件集与预期的更改集进行比较,可以验证构建定义的正确性。这些集合是通过跟踪干净构建期间执行的系统调用推断出的依赖关系图来确定的。我们通过详尽地测试开源项目的构建规则来评估我们的方法,在其中31个项目中发现导致竞争条件和错误构建的问题。我们讨论了我们检测到的错误,识别了使用宏语言时的反模式。我们修复了一些项目中的问题,其中构建系统的特性允许一个干净的解决方案。
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Detecting Incorrect Build Rules
Automated build systems are routinely used by software engineers to minimize the number of objects that need to be recompiled after incremental changes to the source files of a project. In order to achieve efficient and correct builds, developers must provide the build tools with dependency information between the files and modules of a project, usually expressed in a macro language specific to each build tool. In order to guarantee correctness, the authors of these tools are responsible for enumerating all the files whose contents an output depends on. Unfortunately, this is a tedious process and not all dependencies are captured in practice, which leads to incorrect builds. We automatically uncover such missing dependencies through a novel method that we call build fuzzing. The correctness of build definitions is verified by modifying files in a project, triggering incremental builds and comparing the set of changed files to the set of expected changes. These sets are determined using a dependency graph inferred by tracing the system calls executed during a clean build. We evaluate our method by exhaustively testing build rules of open-source projects, uncovering issues leading to race conditions and faulty builds in 31 of them. We provide a discussion of the bugs we detect, identifying anti-patterns in the use of the macro languages. We fix some of the issues in projects where the features of build systems allow a clean solution.
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