UVL Sentinel: a tool for parsing and syntactic correction of UVL datasets
David Romero-Organvidez, Jose A. Galindo, David Benavides
TL;DR
The paper tackles the problem of maintaining UVL feature model datasets as UVL parsers evolve, which can introduce incompatibilities. It introduces UVL Sentinel, a tool that analyzes UVL datasets, executes a UVL parser to generate diagnostics, and applies regex-based corrections to common formatting errors to produce fixed datasets. In a large-scale evaluation on 1,479 models across 20 datasets, UVL Sentinel semi-automatically fixed 185 warnings and syntax errors, achieving about 86% improvement and proving the approach can decouple datasets from specific parser versions. The work enables parser-version agnostic dataset maintenance and highlights the need for broader error-pattern coverage and extensions to other UVL parsers in future work.
Abstract
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.