An empirical assessment indicating that DSAR supports comprehension, architectural reasoning, and communication; qualitative evidence on the strengths and limitations of the generated explanatory artefacts; and recommendations for practitioners and researchers on adopting and further developing DSAR for architectural sense-making are presented.
Abstract
Understanding large, complex software architectures is difficult and time-consuming. Software architecture recovery aims to extract explanatory artefacts from code, but existing methods often lack generalisability. To address this, we previously proposed Deductive Software Architecture Recovery (DSAR) to extend current techniques. This paper presents a qualitative evaluation of DSAR in an industrial setting. Using a large language model-assisted prototype, we examined the recovered architecture's content quality, presentation quality, and usefulness for understanding and maintaining real-world systems. To ensure practical relevance, we conducted an in-depth judgement study with software engineering teams at ASML applying DSAR to 17 Java repositories across diverse teams. We contribute: an empirical assessment indicating that DSAR supports comprehension, architectural reasoning, and communication; qualitative evidence on the strengths and limitations of the generated explanatory artefacts; and recommendations for practitioners and researchers on adopting and further developing DSAR for architectural sense-making.
It was concluded that Clean Architecture cannot be viewed solely as an ally or a villain, as its effectiveness depends on project characteristics, team experience, and the proportionate application of its principles.
Algorithm-Driven Development is introduced, a methodology developed from industrial practice to address recurring challenges in translating requirements into reliable, testable, and maintainable software behavior that provides systematic coverage of functional scenarios from the outset of development.
Philippe Jawish, Pierre Evrard, Alexandre Lemerle et al.· Journal of Systems and Softw...· 0 citations
A Domain-Specific Language (DSL), named RI language, designed for the declarative description of microservices, along with a supporting tool, TSE (Toolbox Service Executor), implemented in Python and based on the RI grammar, enables the representation of structural and operational aspects of services in a technology-agnostic manner.
Cássio Ritse, Machado Dos, Santos Silva et al.· 0 citations
This work presents the first cross-task empirical evaluation of LLMs spanning five RE-related activities, as well as replication materials supporting reproducibility, and a broader understanding of the capabilities, limitations, and practical readiness of current LLMs for RE.
Jacek Dabrowski, Manjeshwar Aniruddh Mallya, Alessio Ferrari et al.· 0 citations
Software architecture quality evaluation is an important means of assessing architectural soundness and informing the design, maintenance, and evolution of complex software systems. However, existing architecture quality evaluation approaches are largely source-agnostic, and mixed-source-specific quality concerns remain underexplored in architecture quality evaluation. They do not explicitly capture the source-related quality concerns introduced by the coexistence of autonomous and reused code. In mixed-source architectures, autonomous and reused entities often differ in how they are organized, evolved, and constrained, giving rise to concerns related to source interweaving, reduced dependency autonomy, and license-induced architectural constraints. As a result, architectures with substantially different mixed-source configurations may still receive similar quality evaluation results under source-agnostic methods. To address this limitation, we propose MSAQM, a mixed-source architecture quality model that introduces explicit source awareness into architecture evaluation. MSAQM formalizes mixed-source-specific quality concerns through three complementary attributes: Source Interweaving Degree (SID), Path Source Interference (PSI), and License Constraint Severity (LCS). Rather than replacing existing evaluators, MSAQM is designed as a plug-in layer that enhances them with mixed-source awareness while preserving their original general architecture quality evaluation capabilities. We evaluate MSAQM on representative mixed-source architecture changes. The results show that MSAQM improves the ability of existing evaluators to capture mixed-source-specific architecture quality changes while maintaining high consistency with their original assessment behavior.
Chunguang Zhang, Bixin Li, Hao Chen· International journal of sof...· 0 citations
Understanding the architecture is crucial for effectively maintaining and managing large software systems. However, discrepancies often exist between the designed and implemented architectures, which can pose significant risks. To identify these discrepancies, architects need to extract the architecture from the system implementation, which is both time-consuming and error-prone. To simplify this procedure, many automatic architecture recovery techniques have been developed. Yet, their accuracy is often limited. Architects must still invest significant effort in refining recovery results to ensure they accurately reflect the implemented architecture. To reduce such manual effort, we introduce SemRef, a framework that combines LLMs with dependency analysis to automatically refine architectures recovered by existing architecture recovery tools. By leveraging the LLM’s semantic understanding capabilities and integrating structural dependencies, SemRef enhances both the accuracy and the comprehension of recovered architectures. To evaluate SemRef, we tested on 9 projects with published ground-truth architectures and 10 state-of-the-art architecture recovery tools. 5 commonly used metrics are adopted to evaluate the effectiveness of SemRef. The results show that SemRef improves accuracy across various metrics, with normalized gains ranges from 17.72% to 43.35%. Specifically, for MoJoFM and a2aadj metrics, SemRef achieves relative improvements of 118.57% and 100.41%, respectively. Moreover, SemRef is highly scalable. It maintains stable performance across projects ranging from thousands to trillions of lines of code with the cost scale linearly with project size. Further, we test SemRef on various LLMs to demonstrate its generalizability across different models. Beyond improving accuracy, the integration of LLMs enables SemRef to provide a structured module hierarchy and hierarchical module summaries, which further enhance the comprehensibility of recovered architectures.
Yiran Zhang, Chengwei Liu, Yuqiang Sun et al.· arXiv.org· 1 citation
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