Software, Volume 5, Issue 3 (September 2026) – 7 articles

The incorporation of human values into software engineering processes is increasingly recognised as crucial, yet there is still a lack of practical methods to ensure their inclusion. A key challenge in this area is to identify a representative set of stakeholders while maintaining equality and diversity throughout the development process. This study aims to leverage stakeholder salience—defined by the attributes of power, legitimacy, and urgency—to partition stakeholders using a quantile-based statistical approach, establishing stakeholder groups that prevent discrimination and marginalization in requirements engineering processes. We propose a quantile-based statistical method for systematic stakeholder partitioning to ensure that diverse interests are considered and formalize equality and diversity through quantitative metrics in order to operationalize fairness. We applied the proposed method to the RALIC dataset to evaluate its applicability, considering two and three levels of granularity (binary medians and ternary terciles) for each salience component to assess its effectiveness in maintaining equality and diversity. Integrating quantile-based stakeholder salience partitioning into the identification process provides a structured approach to incorporating human values into software engineering, demonstrating that tercile partitioning significantly optimizes group balance and spatial coverage, ultimately contributing to more inclusive and equitable decision-making in requirements gathering. Full article

Domain-Specific Languages (DSLs) are essential in software engineering for safely expressing complex domain logic. However, Large Language Models (LLMs) struggle to generate syntactically and semantically correct code for niche DSLs due to sparse representation in pre-training corpora. While Grammar-Constrained Decoding (GCD) resolves syntactic hallucinations by masking logits through a formal Context-Free Grammar (CFG), empirical evidence shows that strict GCD disrupts the autoregressive Chain-of-Thought (CoT) reasoning of modern models, frequently forcing them into irreversible semantic dead-ends. To overcome the friction between internal neural reasoning and external symbolic constraints, we propose a Dual-Phase Cascaded Neurosymbolic framework. In the first phase, the model is provided with dynamically injected grammar rules and is permitted to reason unconstrained, producing an optimistic code draft. If the draft fails native compiler checks, the system enters a second phase: it preserves the successful semantic reasoning from Phase 1 but re-generates the code under strict GCD enforcement. This cascaded architecture utilizes the formal FSM not as an adversarial constraint, but as a localized syntax repair engine guided by the model’s own prior reasoning. We construct a comprehensive benchmark of 100 MiniZinc constraint programming tasks and evaluate our approach using a $pass@k$ metric with a strict semantic LLM judge. Our findings demonstrate that this dual-phase “think-then-constrain” approach significantly outperforms zero-shot, pure few-shot, and pure GCD baselines, achieving highly reliable, training-free code generation for unseen DSLs. Full article

Microservice-based systems evolve continuously through API changes, service splits and merges, dependency churn, and deployment-topology drift driven by DevOps practices. This rapid evolution erodes the assumptions encoded in formal models, test suites, and architectural descriptions, leaving practitioners with limited guidance on how to keep verification artifacts aligned with evolving implementations. Prior surveys cover microservices design, deployment, performance, and isolated verification techniques, but, to our knowledge, none consolidate the intersection of evolution and formal/rigorous verification, nor map how repository-derived signals and CI/CD pipelines support continuous verification of microservice-based systems. This paper addresses that gap through a systematic literature review (SLR). Following the Kitchenham et al. guidelines and PRISMA-style reporting, we defined a review protocol with four research questions covering (i) formal modeling and verification approaches, (ii) the impact of architectural and API evolution on verifiability, (iii) repository-derived signals for updating formal artifacts, and (iv) continuous and incremental verification in CI/CD pipelines. Structured searches in IEEE Xplore, ACM Digital Library, SpringerLink, and Scopus returned 1187 records, which were screened in seven stages and reduced to 18 included studies (13 primary studies that supply evidence and five background studies retained for transparency and framing) through inclusion/exclusion criteria and a weighted 15-item quality-assessment instrument. We synthesized the included studies thematically and compared them along five dimensions: verification technique, evolution dimension, automation level, empirical evidence, and tool/artifact availability. The synthesis shows that actor-based model checking, TLA⁺ control-plane verification, static code analysis, continuous certification, and runtime/self-adaptive testing form five complementary clusters, but empirical evaluation is dominated by small academic case studies, only a minority of approaches automate model updates from repository signals, and few are integrated end-to-end into CI/CD. We discuss methodological limitations of the body of work, including a lack of industrial-scale validation and shared datasets, contrast intrinsic (build-time, model-driven) and extrinsic (recovery-based) verification strategies, and outline a research agenda toward repository-aware, pipeline-integrated verification for evolving microservices. The present synthesis distils a set of immediate, evidence-based actions for practitioners together with a focused agenda of open research challenges. Full article

Autonomous Cyber–Physical Systems (CPS) must jointly satisfy energy efficiency, accuracy, and real-time constraints, which are typically treated separately in existing methods. This paper proposes a verifiable service-oriented CPS framework for energy-aware autonomous navigation using a high-fidelity cyber–physical twin. The approach integrates physics-based Model Predictive Control (MPC) with explicit power modeling ($P=F·v$) and Dubins curve-based trajectory generation under the 5C (connection, conversion, cyber, cognition, and configuration) architecture using CARLA for synchronized cyber–physical interaction. The proposed method achieves 30.7% reduction in mean power consumption and 12.5% reduction in total energy usage while maintaining sub-centimeter tracking error (<0.05 m). Mission duration increases by 26.3% with only 7% computational overhead, confirming real-time feasibility. The framework provides a verifiable CPS methodology that unifies physics-based control, digital twin synchronization, and service-oriented design for energy-aware autonomous navigation. Full article

Vision–Language Models (VLMs) have emerged as powerful tools for image understanding tasks, yet their practical deployment remains hindered by significant architectural heterogeneity across model families. This paper introduces UVLM (Unified Vision–Language Model), a pip-installable Python (v3.9+) package that provides a unified interface for loading, configuring, and running multiple VLM architectures on custom image analysis tasks. UVLM currently supports two major model families which differ fundamentally in their vision encoding, tokenization, and decoding strategies: LLaVA-NeXT and Qwen2.5-VL. The package abstracts these differences behind a single inference function and eliminates all architecture-specific code from the user’s workflow. UVLM is organized as eight modular Python components (model loading, dual-backend inference, response parsing, consensus validation, batch processing, prompt assembly, model registry, and utilities) and can be deployed in three modes: Google Colab for zero-install cloud access, local Jupyter notebooks for on-premises GPU use, and as a programmatic API for integration into automated pipelines. Key features include a multi-task prompt builder supporting four response types (numeric, category, boolean, text), a consensus validation mechanism based on majority voting, a flexible token budget (up to 1500 tokens) for custom reasoning strategies, and built-in truncation detection. The package is designed for extensibility: adding a new VLM family requires implementing one backend-specific inference section and adding entries to the model registry, without modifying any other module. An illustrative example on 120 street-view images across 16 model configurations is provided to demonstrate the software’s evaluation workflow. Full article

Public sector organizations are under increasing pressure to modernize service delivery while preserving transparency, interoperability, accountability, and citizen trust. Blockchain technology offers relevant capabilities for these objectives, particularly through shared ledgers, cryptographic verification, and programmable rules. However, its adoption in public sector contexts remains constrained by the lack of architectural guidance tailored to inter-organizational services. This study proposes BRA-PS, a Blockchain Reference Architecture for Public Sector Citizen-Centric Applications, developed from a real-world digitalization project in Quebec, Canada. The architecture organizes components into six layers (presentation, business, communication, smart contract, blockchain, and data) with cross-cutting concerns addressing governance, access control, security, and monitoring. A key design principle is the public–private workflow separation, which enables inter-organizational collaboration while preserving each organization’s operational autonomy and data confidentiality. We validated the architecture through a case study involving a vehicle registration process between two public agencies, supported by a proof-of-concept implementation using Hyperledger Fabric. An Architecture Trade-off Analysis Method (ATAM) evaluation, conducted with a panel of five domain experts, identified six architectural risks, including InterPlanetary File System (IPFS) confidentiality exposure and smart contract inflexibility, six non-risks, six sensitivity points, and six trade-offs across three key quality attributes: autonomy, collaboration, and functional suitability. The results show that BRA-PS can support implementation decisions, clarify stakeholder responsibilities, and expose relevant architectural trade-offs. The recommendations derived from the evaluation provide practical guidance for the adoption of blockchain in citizen-centric public sector services. Full article

We present OptimSolution, an open-source, cross-platform framework for the systematic benchmarking and analysis of continuous optimisation methods, available for Windows, Linux and macOS and operable via both a command-line interface (CLI) and a Qt-based Graphical User Interface (GUI). The framework supports four execution modes: Single mode for single method–problem runs with convergence and distribution analysis; Batch mode for automated multi-method, multi-problem experimentation with aggregated statistical summaries; Method Sensitivity Analysis for quantifying the effect of algorithmic hyperparameters on solution quality; and Problem Sensitivity Analysis for assessing how problem-defining parameters influence instance difficulty and inter-method rankings. Complexity analysis is provided along two-axes method scalability across problem dimensionalities and problem difficulty profiles across method portfolios. Statistical validation is embedded natively through Wilcoxon signed-rank and Friedman tests with post-hoc pairwise analysis, complemented by rank tables and box-plot visualisations. All outputs are exportable as CSV files or publication-ready PNG figures. The framework is designed for extensibility: new methods and benchmark problems can be registered and activated through the GUI without modifications to the core codebase, supporting rapid experimental iteration. Full article