arXiv subjects

Joost Noppen

Publications and source records attributed to Joost Noppen.

5 recordsLinked to original sources

arXiv2026

A Study of LLMs' Preferences for Libraries and Programming Languages

Despite the rapid progress of large language models (LLMs) in code generation, existing evaluations focus on functional correctness or syntactic validity, overlooking how LLMs make critical design choices such as which library or programming language to use. To fill this gap, we perform the first empirical study of LLMs' preferences for libraries and programming languages when generating code, covering eight diverse LLMs. We observe a strong tendency to overuse widely adopted libraries such as NumPy; in up to 45% of cases, this usage is not required and deviates from the ground-truth solutions. The LLMs we study also show a significant preference toward Python as their default language. For high-performance project initialisation tasks where Python is not the optimal language, it remains the dominant choice in 58% of cases, and Rust is not used once. These results highlight how LLMs prioritise familiarity and popularity over suitability and task-specific optimality; underscoring the need for targeted fine-tuning, data diversification, and evaluation benchmarks that explicitly measure language and library selection fidelity.

cs.SE↗

arXiv2026

Benchmarking and Evaluating VLMs for Software Architecture Diagram Understanding

Software architecture diagrams are important design artifacts for communicating system structure, behavior, and data organization throughout the software development lifecycle. Although recent progress in large language models has substantially advanced code-centric software engineering tasks such as code generation, testing, and maintenance, the ability of modern vision-language models (VLMs) to understand software architecture diagrams remains underexplored. To address this gap, we present SADU, a benchmark for Software Architecture Diagram Understanding that evaluates VLMs on architecture diagrams as structured software engineering artifacts rather than generic images. SADU contains 154 carefully curated diagrams spanning behavioral, structural, and ER diagrams, paired with structured annotations and 2,431 question-answer tasks covering counting and retrieval reasoning. We evaluate 11 state-of-the-art VLMs from the Gemini, Claude, GPT, and Qwen families. Our results show that software architecture diagram understanding remains challenging for current models: the best-performing model gemini-3-flash-preview achieves only 70.18\% accuracy, while gpt-4o-mini only achieves 17.77\% accuracy. The results further reveal the weaknesses in diagram reasoning and visual relation grounding, highlighting a gap between current VLMs and the needs of design-stage software engineering. SADU provides a foundation for future research on diagram-aware AI systems and more faithful AI-assisted software engineering workflows.

cs.SE↗

arXiv2025

The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Project

Generative AI (GenAI) has recently emerged as a groundbreaking force in Software Engineering, capable of generating code, identifying bugs, recommending fixes, and supporting quality assurance. While its use in coding tasks shows considerable promise, applying GenAI across the entire Software Development Life Cycle (SDLC) has not yet been fully explored. Critical uncertainties in areas such as reliability, accountability, security, and data privacy demand deeper investigation and coordinated action. The GENIUS project, comprising over 30 European industrial and academic partners, aims to address these challenges by advancing AI integration across all SDLC phases. It focuses on GenAI's potential, the development of innovative tools, and emerging research challenges, actively shaping the future of software engineering. This vision paper presents a shared perspective on the future of GenAI-driven software engineering, grounded in cross-sector dialogue as well as experiences and findings within the GENIUS consortium. The paper explores four central elements: (1) a structured overview of current challenges in GenAI adoption across the SDLC; (2) a forward-looking vision outlining key technological and methodological advances expected over the next five years; (3) anticipated shifts in the roles and required skill sets of software professionals; and (4) the contribution of GENIUS in realising this transformation through practical tools and industrial validation. This paper focuses on aligning technical innovation with business relevance. It aims to inform both research agendas and industrial strategies, providing a foundation for reliable, scalable, and industry-ready GenAI solutions for software engineering teams.

cs.SE↗

arXiv2021

Microservice Maturity of Organizations: towards an assessment framework

This early work aims to allow organizations to diagnose their capacity to properly adopt microservices through initial milestones of a Microservice Maturity Model (MiMMo). The objective is to prepare the way towards a general framework to help companies and industries to determine their microservices maturity. Organizations lean more and more on distributed web applications and Line of Business software. This is particularly relevant during the current Covid-19 crisis, where companies are even more challenged to offer their services online, targeting a very high level of responsiveness in the face of rapidly increasing and diverse demands. For this, microservices remain the most suitable delivery application architectural style. They allow agility not only on the technical application, as often considered, but on the enterprise architecture as a whole, influencing the actual financial business of the company. However, microservices adoption is highly risk-prone and complex. Before they establish an appropriate migration plan, first and foremost, companies must assess their degree of readiness to adopt microservices. For this, MiMMo, a Microservices Maturity Model framework assessment, is proposed to help companies assess their readiness for the microservice architectural style, based on their actual situation. MiMMo results from observations of and experience with about thirty organizations writing software. It conceptualizes and generalizes the progression paths they have followed to adopt microservices appropriately. Using the model, an organization can evaluate itself in two dimensions and five maturity levels and thus: (i) benchmark itself on its current use of microservices; (ii) project the next steps it needs to achieve a higher maturity level and (iii) analyze how it has evolved and maintain a global coherence between technical and business stakes.

cs.SE↗

arXiv2015

Sustainability in Software Product Lines: Report on Discussion Panel at SPLC 2014

Sustainability (defined as 'the capacity to keep up') encompasses a wide set of aims: ranging from energy efficient software products (environmental sustainability), reduction of software development and maintenance costs (economic sustainability), to employee and end-user wellbeing (social sustainability). In this report we explore the role that sustainability plays in software product line engineering (SPL). The report is based on the 'Sustainability in Software Product Lines' panel held at SPLC 2014.

cs.SE↗