Demo Data from EDBT 2026 / reviewers in the wild / expert
Yang Zhang 0157
dblp:06/6785-157
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0009-4165-9876ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
1 paper |
Software maintenance and evolution · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software maintenance and evolution › build systems
build dependency error detection |
0.8 | 1 | 2024 | Detecting Build Dependency Errors in Incremental Builds · ISSTA 2024 |
Software maintenance and evolution
build systems |
0.8 | 1 | 2024 | Detecting Build Dependency Errors in Incremental Builds · ISSTA 2024 |
Software maintenance and evolution › build systems
incremental build |
0.8 | 1 | 2024 | Detecting Build Dependency Errors in Incremental Builds · ISSTA 2024 |
Methods — techniques the papers use, named apart from their topics
static analysis · 0.8pre-processor directive analysis · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Detecting Build Dependency Errors in Incremental BuildsabstractIncremental and parallel builds performed by build tools such as Make are the heart of modern C/C++ software projects. Their correct and efficient execution depends on build scripts. However, build scripts are prone to errors. The most prevalent errors are missing dependencies (MDs) and redundant dependencies (RDs). The state-of-the-art methods for detecting these errors rely on clean builds (i.e., full builds of a subset of software configurations in a clean environment), which is costly and takes up to a few hours for large-scale projects. To address these challenges, we propose a novel approach called EChecker to detect build dependency errors in the context of incremental builds. The core idea of EChecker is to automatically update actual build dependencies by inferring them from C/C++ pre-processor directives and Makefile changes from new commits, which avoids clean builds when possible. EChecker achieves higher efficiency than the methods that rely on clean builds while maintaining effectiveness. We selected 12 representative projects, with their sizes ranging from small to large, with 240 commits (20 commits for each project), based on which we evaluated the effectiveness and efficiency of EChecker. We compared the evaluation results with a state-of-the-art build dependency error detection tool. The evaluation shows that the F-1 score of EChecker improved by 0.18 over the state-of-the-art method. EChecker increases the build dependency error detection efficiency by an average of 85.14 times (with a median of 16.30 times). The results demonstrate that EChecker can support practitioners in detecting build dependency errors efficiently. Shanshan Li 0002, He Zhang 0001, Yang Zhang 0157, Guoping Rong, Manuel Rigger |
ISSTA | 4 |