Feature models have become a de facto standard for representing variability in software product lines. UVL (Universal Variability Language) is a language which expresses the features, dependencies, and constraints between them. This language is written in plain text and follows a syntactic structure that needs to be processed by a parser. This parser is software with specific syntactic rules that the language must comply with to be processed correctly. Researchers have datasets with numerous feature models. The language description form of these feature models is tied to a version of the parser language. When the parser is updated to support new features or correct previous ones, these feature models are often no longer compatible, generating incompatibilities and inconsistency within the dataset. In this paper, we present UVL Sentinel. This tool analyzes a dataset of feature models in UVL format, generating error analysis reports, describing those errors and, eventually, a syntactic processing that applies the most common solutions. This tool can detect the incompatibilities of the feature models of a dataset when the parser is updated and tries to correct the most common syntactic errors, facilitating the management of the dataset and the adaptation of their models to the new version of the parser. Our tool was evaluated using a dataset of 1,479 UVL models from different sources and helped semi-automatically fix 185 warnings and syntax errors.

翻译：特征模型已成为软件产品线中表达可变性的事实标准。UVL（通用可变性语言）是一种用于描述特征、特征间依赖关系及约束的语言。该语言以纯文本形式编写，遵循特定的语法结构，需通过解析器进行处理。解析器作为具备特定语法规则的软件，要求语言必须严格遵循这些规则方能被正确解析。研究人员拥有包含大量特征模型的数据集，这些特征模型的语言描述形式与解析器的特定版本紧密关联。当解析器为支持新特性或修复旧问题而更新时，原有特征模型往往不再兼容，导致数据集中产生不兼容性与不一致性。本文提出UVL Sentinel工具：该工具可分析UVL格式的特征模型数据集，生成错误分析报告以描述各类错误，并最终通过语法处理应用最常见的解决方案。当解析器更新时，该工具能检测数据集中特征模型的不兼容性，并尝试修正最常见的语法错误，从而简化数据集管理及模型对解析器新版本的适配过程。我们采用来自不同来源的1,479个UVL模型数据集对工具进行评测，该工具成功辅助半自动修复了185项警告与语法错误。