Application for Tracking Bugs in Software Development: Improving Project Management Efficiency

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.

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

An integrated reference architecture is proposed that combines TDD's fine-grained unit-level feedback loop with BDD's stakeholder-readable acceptance criteria inside a single continuous integration and continuous delivery (CI/CD) pipeline.

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 . }

ML-Enhanced Code Refactoring Recommendations for Improving Software Maintainability

Software maintainability is a critical quality attribute influencing the long-term sustainability, scalability, and cost-effectiveness of software systems. Traditional refactoring approaches often rely on manual inspection or rule-based static analysis, which can be time-consuming, inconsistent, and limited in capturing deeper code quality issues. Recent advances in machine learning (ML) provide new opportunities to automate and enhance refactoring recommendations by learning from large codebases, identifying complex patterns, and predicting optimal refactoring strategies. This paper investigates ML-driven approaches for generating code refactoring recommendations aimed at improving maintainability. We review existing techniques, propose an ML-based framework capable of detecting maintainability hotspots and suggesting targeted refactorings, and evaluate its effectiveness through empirical experiments on real-world repositories. Results show that ML-enhanced recommendations outperform traditional methods in accuracy, relevance, and impact on maintainability metrics. The findings highlight the potential of integrating ML into modern development practices to support developers in producing cleaner, more maintainable software systems.

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

IMPLEMENTATION GUIDE OF SOFTWARE DEVELOPMENT BEST PRACTICES BASED ON DEVOPS