quality assurance

Existing BPMN+DMN process models lack semantic-level automated verification; mainstream tools support only syntactic validation, while behavioral errors require manual execution and debugging, and model transformations remain opaque. Method: We propose the first end-to-end automated verification framework that (i) formally translates BPMN+DMN models into semantics-preserving Java programs; (ii) synthesizes interactive test plans via symbolic execution and input-domain disambiguation; and (iii) provides structured coverage analysis at both node and edge levels. Results: Evaluated on established benchmark processes from the literature, our approach significantly improves semantic defect detection, achieves an average test coverage of 89.3%, and accelerates verification by over 20× compared to manual methods.

This study investigates the practical prioritization of key quality attributes—such as fault detection capability, usability, and maintainability—in test cases and test suites, along with associated implementation challenges in industrial software testing. We designed a structured questionnaire grounded in a systematic literature review and deployed it across a large-scale, heterogeneous cohort of software testing practitioners on LinkedIn, yielding 354 valid responses. Mixed-method analysis (qualitative and quantitative) revealed significant contextual variations in attribute prioritization across domains—including agile, embedded, and web development—and identified three pervasive barriers: ambiguous attribute definitions, absence of actionable measurement metrics, and lack of formal review mechanisms. To our knowledge, this is the first empirical study to systematically characterize such cross-domain perceptual differences and practical impediments. The findings provide evidence-based guidance for refining test quality assessment frameworks and prioritizing engineering improvements in real-world testing practice.

Existing conformance checking approaches between process models and reference models suffer from limited semantic expressiveness and insufficient automation, hindering fine-grained compliance verification. This paper proposes a semantic consistency checking method grounded in causal dependency analysis of tasks and events, transcending traditional trajectory-based dependency modeling by formally encoding causal constraints at the semantic level. We establish a unified framework integrating causal dependency modeling, semantic representation, and formal verification, and design an automated conformance checking algorithm implemented in a prototype tool. Empirical evaluation demonstrates that our approach significantly outperforms state-of-the-art techniques in both accuracy and flexibility, achieving— for the first time—the fully automated, high-expressivity semantic conformance verification of process models against reference models.

This work addresses the challenge of balancing software quality, testability, and maintainability under rapid iteration and frequent requirement changes. It proposes Algorithm-Driven Development (ADD), a novel approach that unifies requirements specification and technical design by using algorithm flowcharts as a single, coherent artifact. This integration enables end-to-end modeling of requirements, architecture, and testing. Leveraging this model, the system automatically generates high-coverage acceptance tests and incorporates continuous integration with code coverage feedback. Industrial adoption at Dassault Systèmes demonstrates that ADD achieves over 95% code coverage, substantially reduces defect density, and ensures a stable delivery cadence, outperforming conventional test-driven development and test-after approaches.

Facing persistent declines in customer satisfaction among small- and medium-sized enterprises (SMEs) in the IT services sector—and intensifying competitive pressure from rivals delivering superior service at lower costs—this study proposes an integrated service quality assessment framework combining Lean Six Sigma and the SERVQUAL model. Methodologically, it systematically unifies the DMAIC methodology, SERVQUAL’s five-dimensional gap analysis, and Six Sigma’s data-driven statistical process control techniques to establish a quantifiable, traceable pathway for service improvement. Empirical validation demonstrates that the framework precisely identifies five root causes of customer dissatisfaction, increases customer satisfaction significantly, and reduces customer acquisition cost by 18.3%. This work bridges a critical methodological gap in service quality management: the absence of a quantitatively rigorous, end-to-end integrated approach—from diagnostic assessment to closed-loop optimization. It offers SME IT service providers a novel quality management paradigm that balances theoretical rigor with practical implementability.

This study addresses the challenges of implementing technical quality control in agile R&D projects under conditions of high technological uncertainty and experimental pressure. Through a mixed-methods approach combining survey data, quantitative statistical analysis, and qualitative content analysis, it examines the adoption, perceived effectiveness, and key obstacles related to technical quality practices—such as automated testing, code reviews, and continuous integration—among Scrum teams in technology organizations based in Manaus, Brazil. As the first exploratory investigation focused on this regional innovation ecosystem, the research establishes a baseline for understanding technical quality management in agile R&D contexts. It reveals critical issues including inconsistent practice implementation, insufficient monitoring of technical quality metrics, and a lack of effective mechanisms to evaluate technical debt from a business-value perspective.

This study addresses the uncontrolled quality of quality engineering (QE) artifacts—such as requirements specifications, test cases, and Behavior-Driven Development (BDD) scenarios—automatically generated by large language models (LLMs). We propose an iterative optimization framework integrating forward generation, backward generation, and rubric-guided scoring to enhance artifact quality along four dimensions: clarity, completeness, consistency, and testability. Our approach enables automated, quantitative, and reproducible quality assessment and improvement. Evaluated across 12 real-world projects, the method significantly improves output stability: it preserves high quality under high-quality inputs and substantially outperforms baselines under low-quality inputs. The core contribution is the first integration of backward generation with structured rubric-based guidance, establishing a closed-loop, artifact-centric quality enhancement paradigm for QE.

This work addresses the problem of implementation drift in evolving distributed systems, where runtime behavior gradually deviates from the original design. To tackle this issue, the paper proposes a design conformance assessment method based on distributed tracing data. It introduces, for the first time in the domain of distributed systems, conformance checking techniques from process mining, leveraging runtime traces collected via the OpenTelemetry standard and automatically comparing them against behavioral models defined at design time to quantify their alignment. The key contribution lies in establishing persistent, monitorable conformance metrics that enable continuous, automated evaluation of deviations between system implementation and design. This approach is readily applicable to modern distributed systems widely adopting OpenTelemetry for observability.

Ad hoc SQL development lacks engineering rigor, leading to data silos, logical redundancy, and ineffective data governance. Method: This paper proposes a DataOps-driven CI/CD framework for analytical SQL warehouses, featuring a novel five-stage automated pipeline—Lint, Optimize, Parse, Validate, Observe—that embeds quality assurance and enables end-to-end lifecycle governance. Contribution/Results: We introduce the DataOps Controls Scorecard and a requirements traceability matrix, explicitly mapping 12 governance criteria to CI/CD stages to ensure control completeness and scalability. The framework integrates Agile, Lean, and DevOps principles with static analysis, syntactic parsing, optimization recommendations, validation testing, and observability. Empirical evaluation demonstrates significant improvements in data quality, development transparency, and cross-functional collaboration, providing a sustainable, production-ready pathway for large-scale analytical systems.

This study addresses the persistent challenge organizations face in aligning DevOps automation initiatives with strategic objectives such as waste reduction, delivery predictability, cross-team collaboration, and customer-perceived quality. To bridge this gap, the authors propose a unified VSM–GQM–DevOps framework that integrates Value Stream Mapping (VSM), the Goal-Question-Metric (GQM) approach, and DevOps practices. The framework enables identification of delivery bottlenecks, construction of decision-oriented measurement models, and implementation of maturity-aligned, reversible automation interventions, thereby establishing an auditable and traceable pathway for automation investment. Validated through a multi-site longitudinal study employing DORA metrics, interrupted time series analysis, and mixed-methods evaluation, the framework demonstrates significant improvements in delivery performance and project management outcomes, fostering continuous, strategy-aligned improvement.