Test-Driven Development and Behaviour-Driven Development: An Integrated Framework for Enterprise Software Quality Assurance

Algorithm-driven Development: A proactive approach to improving software quality and reducing defects

Ensuring software quality while meeting deadlines and adapting to evolving requirements is a persistent challenge in software engineering practice. This paper introduces Algorithm-Driven Development (ADD), a methodology developed from industrial practice to address recurring challenges in translating requirements into reliable, testable, and maintainable software behavior. ADD translates requirements into algorithmic flowcharts from which acceptance tests are systematically derived. These flowcharts serve both as specification artifacts and as technical blueprints, supporting shared understanding between stakeholders and developers. By linking requirement modeling with automated test generation, ADD provides systematic coverage of functional scenarios, including edge cases, from the outset of development. The approach was evaluated over a four-year period within an industrial project at Dassault Syst\`emes, involving two development teams, 22,444 lines of production code for Team 1, and 157 APIs analyzed for Team 2. The evaluation combined longitudinal quality and delivery indicators with a comparative analysis of ADD, TDD, and test-last development practices across API functions of different complexity levels. Empirical data collected from internal lifecycle management and CI/CD systems show that ADD supported sustained code coverage above 95%, low defect density in both QA and post-release phases, and a stable delivery cadence. These findings provide evidence of ADD's potential to strengthen the connection between requirements, testing, and implementation in industrial software development contexts.

IMPLEMENTATION GUIDE OF SOFTWARE DEVELOPMENT BEST PRACTICES BASED ON DEVOPS

Empirical Evaluation of DevOps Implementation in Optimizing the Software Life Cycle for Cloud Platforms

Today, IT organizations, in particular, have to provide quick, reliable and high-quality software solutions for meeting the changing market requirements. While the development process has been structured by traditional software engineering paradigms (Waterfall, Agile and Spiral Models), traditional workflows often involve operational bottlenecks. In particular, the lack of communication and coordination between development and operations can lead to delivery delays. DevOps has come about as a transformative approach that fuses software design and IT operations into a single, streamlined and automated process that aims to overcome these systemic inefficiencies. DevOps is used to streamline the software delivery pipeline, when paired with Cloud Computing infrastructure, including SaaS, PaaS, and IaaS solutions from AWS, Azure, and GCP. In this project, one will be working on building a Continuous Integration and Continuous Deployment (CI/CD) pipeline using Microsoft Azure to automate software delivery and improve overall efficiency

From Metrics to Improvement: A Lifecycle-Aware LLM Feedback Framework for Research Software Quality

Research software is increasingly central to scientific workflows, yet it is often developed by researchers with limited software engineering expertise. This can lead to quality issues that hinder maintainability, reproducibility, reuse, and sustainability. Existing static analysis tools can identify such issues, but their outputs often require expert interpretation and provide limited support for translating quality assessments into actionable improvements. To address this gap, we propose a lifecycle-aware framework that integrates quantitative software quality assessment with Large Language Model (LLM)-based code refinement. The framework comprises two stages. First, a lifecycle-aware Quality Model is developed from established software quality standards and practitioner requirements. The model defines five quality dimensions and 25 candidate metrics, of which 14 are operationalized using existing analysis tools and custom measurements. Second, the resulting quality diagnostics are used as structured feedback within an iterative LLM-based refinement process, enabling generated improvements to be repeatedly reassessed against the Quality Model. We evaluate the framework on notebook-centric research software using multiple LLMs and compare iterative structured feedback with single-step feedback and unstructured prompting. The results show improvements in specific quality attributes, particularly code duplication and structural quality, while also revealing trade-offs among maintainability, code size, documentation, and complexity. These findings demonstrate the potential of metric-driven LLM feedback for research software quality improvement while highlighting its inherently multi-objective nature \footnote{The source code and experimental data are publicly available at https://github.com/QCDIS/Software_Quality_Control_LLM . }

DevOps Beyond Software: Establishing CI/CD Frameworks across Semiconductor and Cloud Engineering Lifecycles

DevOps has changed the way we build and deploy software, focusing on collaboration, automation, continuous integration and continuous delivery (CI/CD). This allows enterprises to provide software faster, with higher quality and greater operational efficiency. While these ideas are frequently employed in traditional software engineering, their use in other technological fields is still limited, despite increasing complexity and increasing demands of speed and dependability. In this post we discuss how CI/CD approaches may be used outside of software development to semiconductor and cloud engineering life-cycles. Disconnected workflows, lengthy validation cycles, varied toolchains and segregated teams can hinder productivity and creativity. Semiconductor creation entails complex procedures of design, simulation, verification and fabrication, while cloud engineering demands constant provisioning of infrastructure, managing configurations, ensuring security and deploying services in ever-changing settings. Such issues point to the necessity for a unified methodology that can integrate automation, traceability and constant feedback across many engineering disciplines. In this work, we propose a comprehensive CI/CD solution, which merges software, semiconductor and cloud engineering processes in one DevOps-driven approach. This framework brings together test automation, versioning, artifact management, infrastructure orchestration, design validation and continuous monitoring into a single scalable engineering environment. This paper presents a realistic case study showing how the implementation in cross-functional teams, with the help of the standardized pipelines and integrated automation, facilitates cooperation, reduces cycle times, enhances quality assurance and accelerates delivery outcomes. The results suggest that the use of DevOps principles in non-traditional domains can yield significant benefits in terms of operational consistency, reduction of human work, and end-to-end visibility along the product life cycle. This study adds to the knowledge of enterprise-wide DevOps adoption by providing a viable roadmap to organizations that aspire to integrate engineering processes, break down domain silos and realize continuous innovation across physical and digital technology ecosystems.

DevSecOpsUX: An Integrated Framework for Security, Quality Assurance, and User Experience in Continuous Software Delivery

The increasing complexity of modern software systems and the growing demand for continuous software delivery have accelerated the adoption of DevOps practices. However, existing approaches remain largely fragmented, providing limited integration of security, quality assurance (QA), and user experience (UX), thereby creating challenges related to software security, quality, and user acceptance. This study proposes DevSecOpsUX, a reference framework that integrates security, quality assurance, and user experience into a unified model for continuous software delivery. The research follows a mixed-method approach, combining a systematic mapping study of 118 publications with the conceptual design and expert validation of the proposed framework. The framework is structured around four interconnected pillars—SecUX, SecDev, SecQA, and SecOps—and defines thirteen security milestones embedded throughout the software lifecycle that support continuous validation, traceability, and cross-functional integration. The framework was evaluated by thirteen cybersecurity professionals, achieving agreement levels exceeding 90% across the assessed dimensions of relevance, clarity, conceptual coherence, and applicability. The expert evaluation suggests that integrating security, quality assurance, and user experience within a unified lifecycle model may improve the consistency of software validation processes, reduce fragmentation across DevSecOps practices, and enhance user trust in security mechanisms. These findings should be interpreted as an exploratory assessment based on expert judgment rather than evidence of effectiveness in industrial environments. The proposed framework provides an adaptable and measurable reference model for secure and user-centered software delivery while offering practical guidance for organizations seeking to integrate security, quality assurance, and user experience throughout continuous software delivery lifecycles. Future research should focus on empirical validation through industrial case studies and real-world DevSecOps implementations.