Analyzing non-compilable C/C++ submodules without a resolved build environment remains a critical bottleneck for industrial software evolution. Traditional static analysis tools often fail in these scenarios due to their reliance on successful compilation, while Large Language Models (LLMs) lack the structural context necessary to reason about complex program logic. We introduce ATLAS, a Python-based Command-Line Interface (CLI). It is characterized by: (i) inter-procedural, type-aware analysis that captures dependencies across function boundaries; (ii) support for both full-project and partial analysis of non-compilable projects; (iii) graph optimizations, such as variable collapsing and node blacklisting; and (iv) synchronized multi-view graphs that align syntax, execution paths, and data-flow logic. Evaluating ATLAS with DeepSeek V3.2 for automated test generation demonstrates a 34.71% increase in line coverage and 32.66% in branch coverage, achieving performance on par with and even exceeding the industry-standard symbolic execution tool KLEE on complex projects. With polynomial scalability, ATLAS provides a robust infrastructure for generating the information-dense datasets required by next-generation, graph-aware Machine Learning for Software Engineering (ML4SE) models. Video demonstration: https://youtu.be/xEJDNYkiD4s Tool github repository: https://github.com/jaid-monwar/ATLAS-multi-view-code-representation-tool.git

翻译：在未解析构建环境的情况下分析不可编译的C/C++子模块，始终是工业软件演进的关键瓶颈。传统静态分析工具因依赖成功编译而在这些场景中往往失效，而大型语言模型（LLMs）又缺乏理解复杂程序逻辑所需的结构化上下文。我们提出ATLAS，一个基于Python的命令行接口（CLI）工具。其特性包括：（i）跨过程、类型感知的分析能力，可捕获跨函数边界的依赖关系；（ii）支持对不可编译项目进行全项目分析与部分分析；（iii）图优化技术，如变量折叠与节点黑名单；（iv）同步多视图图结构，对齐语法、执行路径与数据流逻辑。使用DeepSeek V3.2对ATLAS进行自动化测试生成的评估显示，其行覆盖率提升34.71%，分支覆盖率提升32.66%，在复杂项目上达到与行业标准符号执行工具KLEE相当甚至更优的性能。ATLAS具备多项式级可扩展性，为生成面向下一代图感知软件工程机器学习（ML4SE）模型所需的信息密集型数据集提供了鲁棒的基础设施。视频演示：https://youtu.be/xEJDNYkiD4s 工具GitHub仓库：https://github.com/jaid-monwar/ATLAS-multi-view-code-representation-tool.git