Preprocessor directives such as #ifdef have historically been the predominant mechanism for managing variability in legacy$C / C++$codebases. While they offer a simple way to include or exclude code at compilation time, they lack explicit variability representation and make it difficult to reason about features beyond conditional inclusion. Over time, these directives tend to become scattered, redundant, and obsolete resulting in tangled and rigid implementations that hinder maintenance, reuse, and long-term evolution. With the growing maturity of Software Product Line Engineering (SPLE) and the emergence of industrial tools such as Mobioos Forge (MF), it is now possible to rethink the variability management in legacy systems. MF enables the specification of variability through feature models, the building of feature-to-code mappings, and the static derivation of variants without relying on the compiler. These capabilities pave the way for modernizing configurable codebases by making variability explicit, analyzable, and maintainable. In this paper, we present an integrated toolchain for the automated recovery of Software Product Lines from legacy C/C++ systems. Our approach identifies features and their interactions, synthesizes a hierarchical feature model, and generates traceability links to locate feature-specific code fragments. Built as an extension to MF, our approach can also leverages its static derivation engine to safely remove obsolete features and generate concrete product variants. We validate our approach on large-scale, real-world open-source systems, including Chromium, which contains over$\mathbf{2 5 0 0}$distinct features. The results demonstrate the effectiveness of our method in revealing hidden variability structures, enabling safe variant generation, and supporting the modernization of legacy configurable systems through SPLE principles.

Assessing code quality is crucial for effective software maintenance and evolution. Traditional tools like SonarQube offer valuable insights at the application level but lack the granularity needed for detailed, feature-specific analysis. This paper emphasizes the importance of feature-oriented code quality analysis, often overlooked by mainstream tools due to the challenge of correlating high-level feature descriptions with low-level code implementations. To tackle this issue, we leverage existing feature location techniques to introduce a novel approach enabling granular analysis tailored to specific application features. We discuss the motivations for this approach, highlighting its potential to improve precision in enhancement and maintenance strategies. Additionally, this paper introduces a tool-based approach known as InsightMapper. We also present a study demonstrating the benefits of this method through the analysis of two case studies, featuring a recognized benchmark in the feature location domain.

The adoption of Software Product Lines in the industry remains a major challenge. This paper presents an experience report focused on the application of a novel tool-based approach called Mobioos Forge. We introduce the vision and operational activities of Mobioos Forge, emphasizing its significance through an examination of the complex process of migrating the ArgoUML application - an open-source codebase exceeding 400KLOCs. We highlight the achieved feature model and detail the feature-to-source code mapping. Additionally, we explain the derivation process used to generate the source code for multiple variants. We discuss the time and effort expended on this migration, showcasing that, even with no prior familiarity with ArgoUML, it took less than 11 hours to successfully migrate the entire application into an SPL.