Detecting Build Dependency Errors by Dynamic Analysis of Build Execution Against Declaration

Jun Lyu, Shanshan Li, Bohan Liu, He Zhang, Guoping Rong, Chenxing Zhong, Xiaodong Liu

Abstract: Incompletely declared build dependencies in MAKE-based build scripts can result in incorrect or inefficient incremental builds and parallel builds for C/C++ projects. In this sense, developing MAKE-based build scripts (e.g., Makefile) is a nontrivial task, since practitioners need to manually enumerate the dependencies between the parts involved in one build, which may result in serious dependency errors such as missing dependencies or redundant dependencies. To tackle this challenge, the software engineering community has invested considerable effort in dependency error detection. However, due to issues such as incomplete or even missing static dependencies (i.e., dependencies by users declared in Makefile), existing solutions either miss certain critical dependency errors or consume significant time when parsing build dependencies, posing a major challenge to ensure both detection effectiveness and efficiency. We propose a novel approach called BuildChecker to detect the above two critical types of dependency errors in MAKE dependencies that leverages a dynamically generated build execution-declaration model to improve error detection performance and reduce detection time. We evaluate BuildChecker with state-of-the-art tools (Mkcheck, Buildfs, VeriBuild, and VirtualBuild) on 30 projects. The experimental results show that BuildChecker is able to detect a total of 13,579 dependency errors with only 29 false positives, fewer than all the state-of-the-art tools. In terms of detection efficiency, BuildChecker outperforms Buildfs by 1.38 times and Mkcheck by 66.24 times. All dependency errors had been submitted to the practitioners and maintainers of these projects. At the time of writing this article, we received responses from the maintainers of four projects, who confirmed our error reports and fixes. BuildChecker demonstrates a great potential to support practitioners effectively detect build dependency errors.