Tootfinder

Hey @… - do you have any Infos about bed length? I would love to travel more at night, but with 1,98m it is annoying 🫥
I would love to be able to find suitable connections

#Phantastikprompts 26.09.:
Welcher Ratschlag für Autor*innen hat dir bisher am meisten weitergeholfen?
Der ist eher für Künstler*innen allgemein, aber "Trust the process" - es gibt bei Kunstwerken oft die Phase, in der das eigene Kunstwerk vermasselt wirkt.
Meist ist das aber kurz vor den finalen paar Schritten, die das ganze Werk in voller Schönheit erstrahl…

Organically Uncut Podcast
Great Australian Pods Podcast Directory: #GreatAusPods

plant_pol_vazquez: Vazquez & Simberloff plant-pollinator webs
Eight bipartite networks of plants and pollinators, from the Nahuel Huapi National Park and surrounding areas in Rio Negro, Argentina, from September 1999 to Feburary 2000. Edge weights represent the frequency of species interaction, and there is a common set of vertices across all eight webs.
This network has 144 nodes and 51 edges.
Tags: Biological, Food web, Weighted, Multilayer

🚯 Postcard from California: Plastics ‘recycling’ – out of sight, out of mind
https://www.thenewlede.org/2025/07/postcard-from-california-plastics-recycling-out-of-sight-out-of-mind/

Postcard from California: Plastics ‘recycling’ – out of sight, out of mind
By Bill Walker Last year California shipped more than 156 million pounds of plastic waste to poor countries, with almost three-fourths sent to Mexico and another 44 million pounds sent to developing nations in Asia, Latin America, and Africa. That’s the reality behind the blue recycling bins Californians fill with plastic waste for curbside pickup. We may think we’re recycling, but we’re really sending much of our waste out of sight, out of mind.

Torsten Schrade and Sarah Pittroff from the Academy of Sciences and Literature in Mainz are opening the 5th NFDI4Culture Community Plenary in Mainz. The next two days will be packed with presentations of services and lively discussions, celebrating 5 years of fruitful collaboration in @…
@…

Learning from Few Samples: A Novel Approach for High-Quality Malcode Generation
Haijian Ma, Daizong Liu, Xiaowen Cai, Pan Zhou, Yulai Xie
https://arxiv.org/abs/2508.18148 https:…

Learning from Few Samples: A Novel Approach for High-Quality Malcode Generation
Intrusion Detection Systems (IDS) play a crucial role in network security defense. However, a significant challenge for IDS in training detection models is the shortage of adequately labeled malicious samples. To address these issues, this paper introduces a novel semi-supervised framework \textbf{GANGRL-LLM}, which integrates Generative Adversarial Networks (GANs) with Large Language Models (LLMs) to enhance malicious code generation and SQL Injection (SQLi) detection capabilities in few-sampl…

Fault-Tolerant Spacecraft Attitude Determination using State Estimation Techniques
B. Chidambaram, A. Hilbert, M. Silva
https://arxiv.org/abs/2506.21016 ht…

Fault-Tolerant Spacecraft Attitude Determination using State Estimation Techniques
The extended and unscented Kalman filter, and the particle filter provide a robust framework for fault-tolerant attitude estimation on spacecraft. This paper explores how each filter performs for a large satellite in a low earth orbit. Additionally, various techniques, built on these filters, for fault detection, isolation and recovery from erroneous sensor measurements, are analyzed. Key results from this analysis include filter performance for various fault modes.

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Byam: Fixing Breaking Dependency Updates with Large Language Models
Frank Reyes, May Mahmoud, Federico Bono, Sarah Nadi, Benoit Baudry, Martin Monperrus

macaques: Japanese macaques (1976)
Dominance interactions among a group of adult female Japanese macaques (Macaca fuscata fuscata), observed during a non-mating season in 1976. Edge weights represent the frequency of interaction.
This network has 62 nodes and 1187 edges.
Tags: Social, Animal, Weighted
https://networks.s…

🪩 HUSH: Holistic panoramic 3D scene understanding using spherical harmonics
#ai

HUSH: Holistic panoramic 3D scene understanding using spherical harmonics
An advanced artificial intelligence (AI) technology has been developed that can extract three-dimensional (3D) spatial structure and object information within indoor environments using just a single 360-degree panoramic photograph. This breakthrough is expected to significantly impact fields requiring precise spatial understanding, including augmented reality (AR), mixed reality (MR), and digital twin applications.

🪴 New 'gene gun' design boosts efficiency of plant genetic modification
#plants

New 'gene gun' design boosts efficiency of plant genetic modification
Plant scientists have used a standard "gene gun" since 1988 to genetically modify crops for better yield, nutrition, pest resistance and other valuable traits.

Crosslisted article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- R1-Fuzz: Specializing Language Models for Textual Fuzzing via Reinforcement Learning
Jiayi Lin, Liangcai Su, Junzhe Li, Chenxiong Qian

🔫 Extortion: Mexico’s new battle against a deeply rooted crime
https://english.elpais.com/international/2025-07-08/extortion-mexicos-new-battle-against-a-deeply-rooted-crime.html

Extortion: Mexico’s new battle against a deeply rooted crime
García Harfuch has presented a police and intelligence initiative targeting the criminal charges imposed on both large and small businesses

🦿 China's humanoid robots generate more soccer excitement than their human counterparts
https://techxplore.com/news/2025-06-china-humanoid-robots-generate-soccer.html

China's humanoid robots generate more soccer excitement than their human counterparts
While China's men's soccer team hasn't generated much excitement in recent years, humanoid robot teams have won over fans in Beijing based more on the AI technology involved than any athletic prowess shown.

Semantic Clustering of Civic Proposals: A Case Study on Brazil's National Participation Platform
Ronivaldo Ferreira, Guilherme da Silva, Carla Rocha, Gustavo Pinto
https://arxiv.org/abs/2509.21292 …

Semantic Clustering of Civic Proposals: A Case Study on Brazil's National Participation Platform
Promoting participation on digital platforms such as Brasil Participativo has emerged as a top priority for governments worldwide. However, due to the sheer volume of contributions, much of this engagement goes underutilized, as organizing it presents significant challenges: (1) manual classification is unfeasible at scale; (2) expert involvement is required; and (3) alignment with official taxonomies is necessary. In this paper, we introduce an approach that combines BERTopic with seed words a…

🦠 The multitasking microbe that turns CO₂ into minerals
#microbes

The multitasking microbe that turns CO₂ into minerals
At more than 470 times the atmospheric concentration of CO₂, a humble soil bacterium does something extraordinary: it turns gas into stone.

She’s Got Game: Sarah Spain ’02 Is a Star of Sports Journalism
#sports

She’s Got Game: Sarah Spain ’02 Is a Star of Sports Journalism - Cornellians | Cornell University
The ESPN veteran hosts the first daily podcast on women’s athletics—and just published her first book

Fine-Tuning LLMs to Analyze Multiple Dimensions of Code Review: A Maximum Entropy Regulated Long Chain-of-Thought Approach
Yongda Yu, Guohao Shi, Xianwei Wu, Haochuan He, XueMing Gu, Qianqian Zhao, Kui Liu, Qiushi Wang, Zhao Tian, Haifeng Shen, Guoping Rong
https://arxiv.org/abs/2509.21170

Fine-Tuning LLMs to Analyze Multiple Dimensions of Code Review: A Maximum Entropy Regulated Long Chain-of-Thought Approach
Large Language Models (LLMs) have shown great potential in supporting automated code review due to their impressive capabilities in context understanding and reasoning. However, these capabilities are still limited compared to human-level cognition because they are heavily influenced by the training data. Recent research has demonstrated significantly improved performance through fine-tuning LLMs with code review data. However, compared to human reviewers who often simultaneously analyze multip…

✊ Bangladesh struggles to balance reform efforts and calls for elections after the revolution
https://globalvoices.org/2025/06/29/bangladesh-struggles-to-balance-reform-efforts-and-calls-for-elections-after-the-…

Bangladesh struggles to balance reform efforts and calls for elections after the revolution
The narratives describe a tense and uncertain political transition, with the interim government aiming for reforms before elections in April 2026, while key political parties push for earlier polls by December 2025.

🥵 Heat wave duration is accelerating faster than global warming, researchers find
#climate

Heat wave duration is accelerating faster than global warming, researchers find
New research finds that not only will climate change make heat waves hotter and longer, but the lengthening of heat waves will accelerate with each additional fraction of a degree of warming.

An Improved Quantum Software Challenges Classification Approach using Transfer Learning and Explainable AI
Nek Dil Khan, Javed Ali Khan, Mobashir Husain, Muhammad Sohail Khan, Arif Ali Khan, Muhammad Azeem Akbar, Shahid Hussain
https://arxiv.org/abs/2509.21068

An Improved Quantum Software Challenges Classification Approach using Transfer Learning and Explainable AI
Quantum Software Engineering (QSE) is a research area practiced by tech firms. Quantum developers face challenges in optimizing quantum computing and QSE concepts. They use Stack Overflow (SO) to discuss challenges and label posts with specialized quantum tags, which often refer to technical aspects rather than developer posts. Categorizing questions based on quantum concepts can help identify frequent QSE challenges. We conducted studies to classify questions into various challenges. We extrac…

🏎️ Tiny cars, big stakes: These remote control racers aren't hobbyists. They're pros
https://www.latimes.com/california/story/2025-07-07/professional-radio-control-racers-california-race
🆓

Tiny cars, big stakes: These remote control racers aren't hobbyists. They're pros
For these Southern California residents, radio-control racing is more than a hobby; it's a full-time job.

Designing for Novice Debuggers: A Pilot Study on an AI-Assisted Debugging Tool
Oka Kurniawan, Erick Chandra, Christopher M. Poskitt, Yannic Noller, Kenny Tsu Wei Choo, Cyrille Jegourel
https://arxiv.org/abs/2509.21067

Designing for Novice Debuggers: A Pilot Study on an AI-Assisted Debugging Tool
Debugging is a fundamental skill that novice programmers must develop. Numerous tools have been created to assist novice programmers in this process. Recently, large language models (LLMs) have been integrated with automated program repair techniques to generate fixes for students' buggy code. However, many of these tools foster an over-reliance on AI and do not actively engage students in the debugging process. In this work, we aim to design an intuitive debugging assistant, CodeHinter, that c…

🛸 High-Quality Manufacturing with Electrochemical Jet Machining (ECJM) for Processing Applications: A Comprehensive Review, Challenges, and Future Opportunities
#manufacturing

🎾 The psychological use and abuse of timeouts in professional tennis
#tennis

The psychological use and abuse of timeouts in professional tennis
Data analysis by a University of Manchester psychologist has confirmed the suspicion that tennis players who take a bathroom break are likely to gain an advantage over their opponent.

PseudoBridge: Pseudo Code as the Bridge for Better Semantic and Logic Alignment in Code Retrieval
Yixuan Li, Xinyi Liu, Weidong Yang, Ben Fei, Shuhao Li, Mingjie Zhou, Lipeng Ma
https://arxiv.org/abs/2509.20881

PseudoBridge: Pseudo Code as the Bridge for Better Semantic and Logic Alignment in Code Retrieval
Code search aims to precisely find relevant code snippets that match natural language queries within massive codebases, playing a vital role in software development. Recent advances leverage pre-trained language models (PLMs) to bridge the semantic gap between unstructured natural language (NL) and structured programming languages (PL), yielding significant improvements over traditional information retrieval and early deep learning approaches. However, existing PLM-based methods still encounter…

🐛 What a bumble bee chooses to eat may not match its ideal diet
#insects

What a bumble bee chooses to eat may not match its ideal diet
Humans may not be the only species that struggles to eat the right amounts of the ideal foods. A new study led by researchers at Penn State suggests that what bumble bees choose to eat may not line up with their ideal nutritional needs.

Verification Limits Code LLM Training
Srishti Gureja, Elena Tommasone, Jingyi He, Sara Hooker, Matthias Gall\'e, Marzieh Fadaee
https://arxiv.org/abs/2509.20837 https://

Verification Limits Code LLM Training
Large language models for code generation increasingly rely on synthetic data, where both problem solutions and verification tests are generated by models. While this enables scalable data creation, it introduces a previously unexplored bottleneck: the verification ceiling, in which the quality and diversity of training data are fundamentally constrained by the capabilities of synthetic verifiers. In this work, we systematically study how verification design and strategies influence model perfo…

Exploring Engagement in Hybrid Meetings
Daniela Grassi, Fabio Calefato, Darja Smite, Nicole Novielli, Filippo Lanubile
https://arxiv.org/abs/2509.20780 https://

Exploring Engagement in Hybrid Meetings
Background. The widespread adoption of hybrid work following the COVID-19 pandemic has fundamentally transformed software development practices, introducing new challenges in communication and collaboration as organizations transition from traditional office-based structures to flexible working arrangements. This shift has established a new organizational norm where even traditionally office-first companies now embrace hybrid team structures. While remote participation in meetings has become co…

🏃🏿‍♀️‍➡️ Researchers find that individual practice is the secret to maintaining high team performance over time
https://phys.org/news/2025-07-individual-secret-high-team.html

Researchers find that individual practice is the secret to maintaining high team performance over time
When it comes to learning and retaining complex skills, a new study from Texas A&M University uncovered a surprising finding: in the context of teams, having employees practice skills alone may be the best way to help the team succeed.

Design, Implementation and Evaluation of a Novel Programming Language Topic Classification Workflow
Michael Zhang, Yuan Tian, Mariam Guizani
https://arxiv.org/abs/2509.20631 htt…

Design, Implementation and Evaluation of a Novel Programming Language Topic Classification Workflow
As software systems grow in scale and complexity, understanding the distribution of programming language topics within source code becomes increasingly important for guiding technical decisions, improving onboarding, and informing tooling and education. This paper presents the design, implementation, and evaluation of a novel programming language topic classification workflow. Our approach combines a multi-label Support Vector Machine (SVM) with a sliding window and voting strategy to enable fi…

Enhancing LLM-based Fault Localization with a Functionality-Aware Retrieval-Augmented Generation Framework
Xinyu Shi, Zhenhao Li, An Ran Chen
https://arxiv.org/abs/2509.20552 ht…

Enhancing LLM-based Fault Localization with a Functionality-Aware Retrieval-Augmented Generation Framework
Fault localization (FL) is a critical but time-consuming task in software debugging, aiming to identify faulty code elements. While recent advances in large language models (LLMs) have shown promise for FL, they often struggle with complex systems due to the lack of project-specific knowledge and the difficulty of navigating large projects. To address these limitations, we propose FaR-Loc, a novel framework that enhances method-level FL by integrating LLMs with retrieval-augmented generation (R…

Enhancing Python Programming Education with an AI-Powered Code Helper: Design, Implementation, and Impact
Sayed Mahbub Hasan Amiri, Md Mainul Islam
https://arxiv.org/abs/2509.20518

Enhancing Python Programming Education with an AI-Powered Code Helper: Design, Implementation, and Impact
This is the study that presents an AI-Python-based chatbot that helps students to learn programming by demonstrating solutions to such problems as debugging errors, solving syntax problems or converting abstract theoretical concepts to practical implementations. Traditional coding tools like Integrated Development Environments (IDEs) and static analyzers do not give robotic help while AI-driven code assistants such as GitHub Copilot focus on getting things done. To close this gap, our chatbot c…

PromptDebt: A Comprehensive Study of Technical Debt Across LLM Projects
Ahmed Aljohani, Hyunsook Do
https://arxiv.org/abs/2509.20497 https://arxiv.org/pdf/…

PromptDebt: A Comprehensive Study of Technical Debt Across LLM Projects
Large Language Models (LLMs) are increasingly embedded in software via APIs like OpenAI, offering powerful AI features without heavy infrastructure. Yet these integrations bring their own form of self-admitted technical debt (SATD). In this paper, we present the first large-scale empirical study of LLM-specific SATD: its origins, prevalence, and mitigation strategies. By analyzing 93,142 Python files across major LLM APIs, we found that 54.49% of SATD instances stem from OpenAI integrations and…

AI-Specific Code Smells: From Specification to Detection
Brahim Mahmoudi, Naouel Moha, Quentin Stievenert, Florent Avellaneda
https://arxiv.org/abs/2509.20491 https://

AI-Specific Code Smells: From Specification to Detection
The rise of Artificial Intelligence (AI) is reshaping how software systems are developed and maintained. However, AI-based systems give rise to new software issues that existing detection tools often miss. Among these, we focus on AI-specific code smells, recurring patterns in the code that may indicate deeper problems such as unreproducibility, silent failures, or poor model generalization. We introduce SpecDetect4AI, a tool-based approach for the specification and detection of these code smel…

Formal Verification of Legal Contracts: A Translation-based Approach
Reiner H\"ahnle, Cosimo Laneve, Adele Veschetti
https://arxiv.org/abs/2509.20421 https://

Formal Verification of Legal Contracts: A Translation-based Approach
Stipula is a domain-specific programming language designed to model legal contracts with enforceable properties, especially those involving asset transfers and obligations. This paper presents a methodology to formally verify the correctness of Stipula contracts through translation into Java code annotated with Java Modeling Language specifications. As a verification backend, the deductive verification tool KeY is used. Both, the translation and the verification of partial and total correctness…

Online-Optimized RAG for Tool Use and Function Calling
Yu Pan, Xiaocheng Li, Hanzhao Wang
https://arxiv.org/abs/2509.20415 https://arxiv.org/pdf/2509.20415…

Online-Optimized RAG for Tool Use and Function Calling
In many applications, retrieval-augmented generation (RAG) drives tool use and function calling by embedding the (user) queries and matching them to pre-specified tool/function descriptions. In this paper, we address an embedding misalignment issue that often arises in practical applications due to imperfect embedding models or noisy descriptions; such misalignment may lead to incorrect retrieval and task failure. We introduce Online-Optimized RAG, a deployment-time framework that continually a…

Towards Systematic Specification and Verification of Fairness Requirements: A Position Paper
Qusai Ramadan, Jukka Ruohonen, Abhishek Tiwari, Adam Alami, Zeyd Boukhers
https://arxiv.org/abs/2509.20387

Towards Systematic Specification and Verification of Fairness Requirements: A Position Paper
Decisions suggested by improperly designed software systems might be prone to discriminate against people based on protected characteristics, such as gender and ethnicity. Previous studies attribute such undesired behavior to flaws in algorithmic design or biased data. However, these studies ignore that discrimination is often the result of a lack of well-specified fairness requirements and their verification. The fact that experts' knowledge about fairness is often implicit makes the task of s…

Dynamic ReAct: Scalable Tool Selection for Large-Scale MCP Environments
Nishant Gaurav, Adit Akarsh, Ankit Ranjan, Manoj Bajaj
https://arxiv.org/abs/2509.20386 https://

Dynamic ReAct: Scalable Tool Selection for Large-Scale MCP Environments
We present Dynamic ReAct, a novel approach for enabling ReAct agents to ef- ficiently operate with extensive Model Control Protocol (MCP) tool sets that exceed the contextual memory limitations of large language models. Our approach addresses the fundamental challenge of tool selection in environments containing hundreds or thousands of available tools, where loading all tools simultaneously is computationally infeasible. We propose and evaluate five distinct architectures that progressively re…

State-of-the-Art in Software Security Visualization: A Systematic Review
Ishara Devendra, Chaman Wijesiriwardana, Prasad Wimalaratne
https://arxiv.org/abs/2509.20385 https://

State-of-the-Art in Software Security Visualization: A Systematic Review
Software security visualization is an interdisciplinary field that combines the technical complexity of cybersecurity, including threat intelligence and compliance monitoring, with visual analytics, transforming complex security data into easily digestible visual formats. As software systems get more complex and the threat landscape evolves, traditional text-based and numerical methods for analyzing and interpreting security concerns become increasingly ineffective. The purpose of this paper is…

ACCeLLiuM: Supervised Fine-Tuning for Automated OpenACC Pragma Generation
Samyak Jhaveri, Vanessa Klotzmann, Crista Lopes
https://arxiv.org/abs/2509.20380 https://

ACCeLLiuM: Supervised Fine-Tuning for Automated OpenACC Pragma Generation
The increasing ubiquity of GPUs is accompanied by the increasing complexity of their hardware and parallel programming frameworks. Directive-based parallel programming standards like OpenACC simplify GPU programming to some extent by abstracting away low-level complexities, but a fair amount of expertise is still required in order to use those directives effectively. We introduce ACCeLLiuM, two open weights Large Language Models specifically fine-tuned for generating expert OpenACC directives…

[2025-09-26 Fri (UTC), 18 new articles found for cs.SE Software Engineering]
toXiv_bot_toot

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Towards Automated Identification of Violation Symptoms of Architecture Erosion
Ruiyin Li, Peng Liang, Paris Avgeriou

Crosslisted article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Data and Context Matter: Towards Generalizing AI-based Software Vulnerability Detection
Rijha Safdar, Danyail Mateen, Syed Taha Ali, M. Umer Ashfaq, Wajahat Hussain

A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code
Keke Lian, Bin Wang, Lei Zhang, Libo Chen, Junjie Wang, Ziming Zhao, Yujiu Yang, Haotong Duan, Haoran Zhao, Shuang Liao, Mingda Guo, Jiazheng Quan, Yilu Zhong, Chenhao He, Zichuan Chen, Jie Wu, Haoling Li, Zhaoxuan Li, Jiongchi Yu, Hui Li, Dong Zhang
https://a…

A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code
The increasing adoption of large language models (LLMs) in software engineering necessitates rigorous security evaluation of their generated code. However, existing benchmarks are inadequate, as they focus on isolated code snippets, employ unstable evaluation methods that lack reproducibility, and fail to connect the quality of input context with the security of the output. To address these gaps, we introduce A.S.E (AI Code Generation Security Evaluation), a benchmark for repository-level secur…

LLM-Guided Genetic Improvement: Envisioning Semantic Aware Automated Software Evolution
Karine Even-Mendoza, Alexander Brownlee, Alina Geiger, Carol Hanna, Justyna Petke, Federica Sarro, Dominik Sobania
https://arxiv.org/abs/2508.18089

LLM-Guided Genetic Improvement: Envisioning Semantic Aware Automated Software Evolution
Genetic Improvement (GI) of software automatically creates alternative software versions that are improved according to certain properties of interests (e.g., running-time). Search-based GI excels at navigating large program spaces, but operates primarily at the syntactic level. In contrast, Large Language Models (LLMs) offer semantic-aware edits, yet lack goal-directed feedback and control (which is instead a strength of GI). As such, we propose the investigation of a new research line on AI-p…

Debian in the Research Software Ecosystem: A Bibliometric Analysis
Joenio Marques da Costa, Christina von Flach
https://arxiv.org/abs/2508.18073 https://ar…

Debian in the Research Software Ecosystem: A Bibliometric Analysis
Context: The Debian system has historically participated in academic works and scientific projects, with well-known examples including NeuroDebian, Debian Med, Debsources, Debian Science, and Debian GIS, where the scientific relevance of Debian and its contribution to the Research Software ecosystem are evident. Objective: The objective of this study is to investigate the Debian system through academic publications, with the aim of classifying articles, mapping research, identifying trends, a…

A Large-Scale Study on Developer Engagement and Expertise in Configurable Software System Projects
Karolina M. Milano, Wesley K. G. Assun\c{c}\~ao, Bruno B. P. Cafeo
https://arxiv.org/abs/2508.18070

A Large-Scale Study on Developer Engagement and Expertise in Configurable Software System Projects
Modern systems operate in multiple contexts making variability a fundamental aspect of Configurable Software Systems (CSSs). Variability, implemented via pre-processor directives (e.g., #ifdef blocks) interleaved with other code and spread across files, complicates maintenance and increases error risk. Despite its importance, little is known about how variable code is distributed among developers or whether conventional expertise metrics adequately capture variable code proficiency. This study …

Previously on... Automating Code Review
Robert Heum\"uller, Frank Ortmeier
https://arxiv.org/abs/2508.18003 https://arxiv.org/pdf/2508.18003

Previously on... Automating Code Review
Modern Code Review (MCR) is a standard practice in software engineering, yet it demands substantial time and resource investments. Recent research has increasingly explored automating core review tasks using machine learning (ML) and deep learning (DL). As a result, there is substantial variability in task definitions, datasets, and evaluation procedures. This study provides the first comprehensive analysis of MCR automation research, aiming to characterize the field's evolution, formalize lear…

DesCartes Builder: A Tool to Develop Machine-Learning Based Digital Twins
Eduardo de Conto, Blaise Genest, Arvind Easwaran, Nicholas Ng, Shweta Menon
https://arxiv.org/abs/2508.17988

DesCartes Builder: A Tool to Develop Machine-Learning Based Digital Twins
Digital twins (DTs) are increasingly utilized to monitor, manage, and optimize complex systems across various domains, including civil engineering. A core requirement for an effective DT is to act as a fast, accurate, and maintainable surrogate of its physical counterpart, the physical twin (PT). To this end, machine learning (ML) is frequently employed to (i) construct real-time DT prototypes using efficient reduced-order models (ROMs) derived from high-fidelity simulations of the PT's nominal…

Evaluating Citizen Satisfaction with Saudi Arabia's E-Government Services: A Standards-Based, Theory-Informed Approach
Mohammed O. Alannsary
https://arxiv.org/abs/2508.17912

Evaluating Citizen Satisfaction with Saudi Arabia's E-Government Services: A Standards-Based, Theory-Informed Approach
As digital government platforms become central to public service delivery, understanding citizen assessment is crucial for enhancing usability, trust, and inclusivity. This study investigates citizen satisfaction with the e-government services in Saudi Arabia through a quality-in-use framework based on ISO/IEC 25010 and ISO/IEC 25022 standards, interpreted through the lens of the Unified Theory of Acceptance and Use of Technology (UTAUT). A structured questionnaire was administered to 500 citiz…

A Defect Classification Framework for AI-Based Software Systems (AI-ODC)
Mohammed O. Alannsary
https://arxiv.org/abs/2508.17900 https://arxiv.org/pdf/2508.…

A Defect Classification Framework for AI-Based Software Systems (AI-ODC)
Artificial Intelligence has gained a lot of attention recently, it has been utilized in several fields ranging from daily life activities, such as responding to emails and scheduling appointments, to manufacturing and automating work activities. Artificial Intelligence systems are mainly implemented as software solutions, and it is essential to discover and remove software defects to assure its quality using defect analysis which is one of the major activities that contribute to software qualit…

modelSolver: A Symbolic Model-Driven Solver for Power Network Simulation and Monitoring
Izudin Dzafic, Rabih A. Jabr
https://arxiv.org/abs/2508.17882 https://

modelSolver: A Symbolic Model-Driven Solver for Power Network Simulation and Monitoring
The development of advanced software tools for power system analysis requires extensive programming expertise. Even when using open-source tools, programming skills are essential to modify built-in models. This can be particularly challenging for domain experts who lack coding proficiency. This paper introduces modelSolver, a software solution with a new framework centered around symbolic mathematical modeling. The proposed paradigm facilitates defining models through intuitive mathematical exp…

Logging Requirement for Continuous Auditing of Responsible Machine Learning-based Applications
Patrick Loic Foalem, Leuson Da Silva, Foutse Khomh, Heng Li, Ettore Merlo
https://arxiv.org/abs/2508.17851

Logging Requirement for Continuous Auditing of Responsible Machine Learning-based Applications
Machine learning (ML) is increasingly applied across industries to automate decision-making, but concerns about ethical and legal compliance remain due to limited transparency, fairness, and accountability. Monitoring through logging a long-standing practice in traditional software offers a potential means for auditing ML applications, as logs provide traceable records of system behavior useful for debugging, performance analysis, and continuous auditing. systematically auditing models for comp…

RepoTransAgent: Multi-Agent LLM Framework for Repository-Aware Code Translation
Ziqi Guan, Xin Yin, Zhiyuan Peng, Chao Ni
https://arxiv.org/abs/2508.17720 https://

RepoTransAgent: Multi-Agent LLM Framework for Repository-Aware Code Translation
Repository-aware code translation is critical for modernizing legacy systems, enhancing maintainability, and enabling interoperability across diverse programming languages. While recent advances in large language models (LLMs) have improved code translation quality, existing approaches face significant challenges in practical scenarios: insufficient contextual understanding, inflexible prompt designs, and inadequate error correction mechanisms. These limitations severely hinder accurate and eff…

DocFetch - Towards Generating Software Documentation from Multiple Software Artifacts
Akhila Sri Manasa Venigalla, Sridhar Chimalakonda
https://arxiv.org/abs/2508.17719 https://…

DocFetch - Towards Generating Software Documentation from Multiple Software Artifacts
Software Documentation plays a major role in the usage and development of a project. Widespread adoption of open source software projects contributes to larger and faster development of the projects, making it difficult to maintain the associated documentation. Existing automated approaches to generate documentation largely focus on source code. However, information useful for documentation is observed to be scattered across various artifacts that co-evolve with the source code. Leveraging this…

Code Difference Guided Fuzzing for FPGA Logic Synthesis Compilers via Bayesian Optimization
Zhihao Xu, Shikai Guo, Guilin Zhao, Peiyu Zou, Siwen Wang, Qian Ma, Hui Li, Furui Zhan
https://arxiv.org/abs/2508.17713

Code Difference Guided Fuzzing for FPGA Logic Synthesis Compilers via Bayesian Optimization
Field Programmable Gate Arrays (FPGAs) play a crucial role in Electronic Design Automation (EDA) applications, which have been widely used in safety-critical environments, including aerospace, chip manufacturing, and medical devices. A critical step in FPGA development is logic synthesis, which enables developers to translate their software designs into hardware net lists, which facilitates the physical implementation of the chip, detailed timing and power analysis, gate-level simulation, test …

Who Wins the Race? (R Vs Python) - An Exploratory Study on Energy Consumption of Machine Learning Algorithms
Rajrupa Chattaraj, Sridhar Chimalakonda, Vibhu Saujanya Sharma, Vikrant Kaulgud
https://arxiv.org/abs/2508.17344

Who Wins the Race? (R Vs Python) - An Exploratory Study on Energy Consumption of Machine Learning Algorithms
The utilization of Machine Learning (ML) in contemporary software systems is extensive and continually expanding. However, its usage is energy-intensive, contributing to increased carbon emissions and demanding significant resources. While numerous studies examine the performance and accuracy of ML, only a limited few focus on its environmental aspects, particularly energy consumption. In addition, despite emerging efforts to compare energy consumption across various programming languages for s…

Agentic AI for Software: thoughts from Software Engineering community
Abhik Roychoudhury
https://arxiv.org/abs/2508.17343 https://arxiv.org/pdf/2508.17343

Agentic AI for Software: thoughts from Software Engineering community
AI agents have recently shown significant promise in software engineering. Much public attention has been transfixed on the topic of code generation from Large Language Models (LLMs) via a prompt. However, software engineering is much more than programming, and AI agents go far beyond instructions given by a prompt. At the code level, common software tasks include code generation, testing, and program repair. Design level software tasks may include architecture exploration, requirements under…

What Developers Ask to ChatGPT in GitHub Pull Requests? an Exploratory Study
Julyanara R. Silva, Carlos Eduardo C. Dantas, Marcelo A. Maia
https://arxiv.org/abs/2508.17161 https…

What Developers Ask to ChatGPT in GitHub Pull Requests? an Exploratory Study
The emergence of Large Language Models (LLMs), such as ChatGPT, has introduced a new set of tools to support software developers in solving pro- gramming tasks. However, our understanding of the interactions (i.e., prompts) between developers and ChatGPT that result in contributions to the codebase remains limited. To explore this limitation, we conducted a manual evaluation of 155 valid ChatGPT share links extracted from 139 merged Pull Requests (PRs), revealing the interactions between develo…

Mind the Gap: A Decade-Scale Empirical Study of Multi-Stakeholder Dynamics in VR Ecosystem
Yijun Lu, Hironori Washizaki, Naoyasu Ubayashi, Nobukazu Yoshioka, Chenhao Wu, Masanari Kondo, Yuyin Ma, Jiong Dong, Jianjin Zhao, Dongqi Han
https://arxiv.org/abs/2508.16903

Mind the Gap: A Decade-Scale Empirical Study of Multi-Stakeholder Dynamics in VR Ecosystem
In the development and evolution of VR ecosystem, platform stakeholders continuously adapt their products in response to user and technical feedback, often reflected in subtle shifts in discussion topics or system updates. A comprehensive understanding of these changes is essential for identifying gaps between user expectations and developer actions, which can guide more effective quality assurance and user-centered innovation. While previous studies have analyzed either user reviews or develop…

TriagerX: Dual Transformers for Bug Triaging Tasks with Content and Interaction Based Rankings
Md Afif Al Mamun, Gias Uddin, Lan Xia, Longyu Zhang
https://arxiv.org/abs/2508.16860

TriagerX: Dual Transformers for Bug Triaging Tasks with Content and Interaction Based Rankings
Pretrained Language Models or PLMs are transformer-based architectures that can be used in bug triaging tasks. PLMs can better capture token semantics than traditional Machine Learning (ML) models that rely on statistical features (e.g., TF-IDF, bag of words). However, PLMs may still attend to less relevant tokens in a bug report, which can impact their effectiveness. In addition, the model can be sub-optimal with its recommendations when the interaction history of developers around similar bug…

DevLicOps: A Framework for Mitigating Licensing Risks in AI-Generated Code
Pratyush Nidhi Sharma, Lauren Wright, Anne Herfurth, Munsif Sokiyna, Pratyaksh Nidhi Sharma, Sethu Das, Mikko Siponen
https://arxiv.org/abs/2508.16853

DevLicOps: A Framework for Mitigating Licensing Risks in AI-Generated Code
Generative AI coding assistants (ACAs) are widely adopted yet pose serious legal and compliance risks. ACAs can generate code governed by restrictive open-source licenses (e.g., GPL), potentially exposing companies to litigation or forced open-sourcing. Few developers are trained in these risks, and legal standards vary globally, especially with outsourcing. Our article introduces DevLicOps, a practical framework that helps IT leaders manage ACA-related licensing risks through governance, incid…

EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention
Yifan Zhang, Chen Huang, Yueke Zhang, Jiahao Zhang, Toby Jia-Jun Li, Collin McMillan, Kevin Leach, Yu Huang
https://arxiv.org/abs/2508.16771

EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention
Code language models (so-called CodeLLMs) are now commonplace in software development. As a general rule, CodeLLMs are trained by dividing training examples into input tokens and then learn importance of those tokens in a process called machine attention. Machine attention is based solely on input token salience to output token examples during training. Human software developers are different, as humans intuitively know that some tokens are more salient than others. While intuition itself is in…

CelloAI: Leveraging Large Language Models for HPC Software Development in High Energy Physics
Mohammad Atif, Kriti Chopra, Ozgur Kilic, Tianle Wang, Zhihua Dong, Charles Leggett, Meifeng Lin, Paolo Calafiura, Salman Habib
https://arxiv.org/abs/2508.16713

CelloAI: Leveraging Large Language Models for HPC Software Development in High Energy Physics
Next-generation High Energy Physics (HEP) experiments will generate unprecedented data volumes, necessitating High Performance Computing (HPC) integration alongside traditional high-throughput computing. However, HPC adoption in HEP is hindered by the challenge of porting legacy software to heterogeneous architectures and the sparse documentation of these complex scientific codebases. We present CelloAI, a locally hosted coding assistant that leverages Large Language Models (LLMs) with retrieva…

A Scalable Framework for the Management of STPA Requirements: a Case Study on eVTOL Operations
Shufeng Chen, Halima El Badaoui, Mariat James Elizebeth, Takuya Nakashima, Siddartha Khastgir, Paul Jennings
https://arxiv.org/abs/2508.16708

A Scalable Framework for the Management of STPA Requirements: a Case Study on eVTOL Operations
System-Theoretic Process Analysis (STPA) is a recommended method for analysing complex systems, capable of identifying thousands of safety requirements often missed by traditional techniques such as Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FTA). However, the absence of a structured framework for managing and prioritising these requirements presents challenges, particularly in fast-paced development environments. This paper introduces a scalable framework for prioritisin…

Cybernaut: Towards Reliable Web Automation
Ankur Tomar, Hengyue Liang, Indranil Bhattacharya, Natalia Larios, Francesco Carbone
https://arxiv.org/abs/2508.16688 https://

Cybernaut: Towards Reliable Web Automation
The emergence of AI-driven web automation through Large Language Models (LLMs) offers unprecedented opportunities for optimizing digital workflows. However, deploying such systems within industry's real-world environments presents four core challenges: (1) ensuring consistent execution, (2) accurately identifying critical HTML elements, (3) meeting human-like accuracy in order to automate operations at scale and (4) the lack of comprehensive benchmarking data on internal web applications. Exist…

Democratizing AI Development: Local LLM Deployment for India's Developer Ecosystem in the Era of Tokenized APIs
Vikranth Udandarao, Nipun Misra
https://arxiv.org/abs/2508.16684

Democratizing AI Development: Local LLM Deployment for India's Developer Ecosystem in the Era of Tokenized APIs
India's developer community faces significant barriers to sustained experimentation and learning with commercial Large Language Model (LLM) APIs, primarily due to economic and infrastructural constraints. This study empirically evaluates local LLM deployment using Ollama as an alternative to commercial cloud-based services for developer-focused applications. Through a mixed-methods analysis involving 180 Indian developers, students, and AI enthusiasts, we find that local deployment enables subs…

Cognitive Agents Powered by Large Language Models for Agile Software Project Management
Konrad Cinkusz, Jaros{\l}aw A. Chudziak, Ewa Niewiadomska-Szynkiewicz
https://arxiv.org/abs/2508.16678

Cognitive Agents Powered by Large Language Models for Agile Software Project Management
This paper investigates the integration of cognitive agents powered by Large Language Models (LLMs) within the Scaled Agile Framework (SAFe) to reinforce software project management. By deploying virtual agents in simulated software environments, this study explores their potential to fulfill fundamental roles in IT project development, thereby optimizing project outcomes through intelligent automation. Particular emphasis is placed on the adaptability of these agents to Agile methodologies and…

Reflective Paper-to-Code Reproduction Enabled by Fine-Grained Verification
Mingyang Zhou, Quanming Yao, Lun Du, Lanning Wei, Da Zheng
https://arxiv.org/abs/2508.16671 https://…

Reflective Paper-to-Code Reproduction Enabled by Fine-Grained Verification
Reproducing machine learning papers is essential for scientific progress but remains challenging for both humans and automated agents. Existing agent-based methods often struggle to fully and accurately reproduce implementation details such as mathematical formulas and algorithmic logic. Previous studies show that reflection with explicit feedback improves agent performance. However, current paper reproduction methods fail to effectively adopt this strategy. This gap mainly arises from the dive…

[2025-08-26 Tue (UTC), 26 new articles found for cs.SE Software Engineering]
toXiv_bot_toot

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Boosting Redundancy-based Automated Program Repair by Fine-grained Pattern Mining
Jiajun Jiang, Fengjie Li, Zijie Zhao, Zhirui Ye, Mengjiao Liu, Bo Wang, Hongyu Zhang, Junjie Chen

Crosslisted article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Murakkab: Resource-Efficient Agentic Workflow Orchestration in Cloud Platforms
Chaudhry, Choukse, Qiu, Goiri, Fonseca, Belay, Bianchini

A Slice-Based Change Impact Analysis for Regression Test Case Prioritization of Object-Oriented Programs
S. Panda, D. Munjal, D. P. Mohapatra
https://arxiv.org/abs/2508.19056 ht…

A Slice-Based Change Impact Analysis for Regression Test Case Prioritization of Object-Oriented Programs
Test case prioritization focuses on finding a suitable order of execution of the test cases in a test suite to meet some performance goals like detecting faults early. It is likely that some test cases execute the program parts that are more prone to errors and will detect more errors if executed early during the testing process. Finding an optimal order of execution for the selected regression test cases saves time and cost of retesting. This paper presents a static approach to prioritizing th…

GitTaskBench: A Benchmark for Code Agents Solving Real-World Tasks Through Code Repository Leveraging
Ziyi Ni, Huacan Wang, Shuo Zhang, Shuo Lu, Ziyang He, Wang You, Zhenheng Tang, Yuntao Du, Bill Sun, Hongzhang Liu, Sen Hu, Ronghao Chen, Bo Li, Xin Li, Chen Hu, Binxing Jiao, Daxin Jiang, Pin Lyu
https://arxiv.org/abs/2508.18993

GitTaskBench: A Benchmark for Code Agents Solving Real-World Tasks Through Code Repository Leveraging
Beyond scratch coding, exploiting large-scale code repositories (e.g., GitHub) for practical tasks is vital in real-world software development, yet current benchmarks rarely evaluate code agents in such authentic, workflow-driven scenarios. To bridge this gap, we introduce GitTaskBench, a benchmark designed to systematically assess this capability via 54 realistic tasks across 7 modalities and 7 domains. Each task pairs a relevant repository with an automated, human-curated evaluation harness s…

Interleaving Large Language Models for Compiler Testing
Yunbo Ni, Shaohua Li
https://arxiv.org/abs/2508.18955 https://arxiv.org/pdf/2508.18955

Interleaving Large Language Models for Compiler Testing
Testing compilers with AI models, especially large language models (LLMs), has shown great promise. However, current approaches struggle with two key problems: The generated programs for testing compilers are often too simple, and extensive testing with the LLMs is computationally expensive. In this paper, we propose a novel compiler testing framework that decouples the testing process into two distinct phases: an offline phase and an online phase. In the offline phase, we use LLMs to generate …

Dealing with SonarQube Cloud: Initial Results from a Mining Software Repository Study
Sabato Nocera, Davide Fucci, Giuseppe Scanniello
https://arxiv.org/abs/2508.18816 https://

Dealing with SonarQube Cloud: Initial Results from a Mining Software Repository Study
Background: Static Code Analysis (SCA) tools are widely adopted to enforce code quality standards. However, little is known about how open-source projects use and customize these tools. Aims: This paper investigates how GitHub projects use and customize a popular SCA tool, namely SonarQube Cloud. Method: We conducted a mining study of GitHub projects that are linked through GitHub Actions to SonarQube Cloud projects. Results: Among 321 GitHub projects using SonarQube Cloud, 81% of them are corr…

Does AI Code Review Lead to Code Changes? A Case Study of GitHub Actions
Kexin Sun, Hongyu Kuang, Sebastian Baltes, Xin Zhou, He Zhang, Xiaoxing Ma, Guoping Rong, Dong Shao, Christoph Treude
https://arxiv.org/abs/2508.18771

Does AI Code Review Lead to Code Changes? A Case Study of GitHub Actions
AI-based code review tools automatically review and comment on pull requests to improve code quality. Despite their growing presence, little is known about their actual impact. We present a large-scale empirical study of 16 popular AI-based code review actions for GitHub workflows, analyzing more than 22,000 review comments in 178 repositories. We investigate (1) how these tools are adopted and configured, (2) whether their comments lead to code changes, and (3) which factors influence their ef…

LLM as an Execution Estimator: Recovering Missing Dependency for Practical Time-travelling Debugging
Yunrui Pei, Hongshu Wang, Wenjie Zhang, Yun Lin, Weiyu Kong, Jin song Dong
https://arxiv.org/abs/2508.18721

LLM as an Execution Estimator: Recovering Missing Dependency for Practical Time-travelling Debugging
Dynamic data dependency, answering "why a variable has this value?", is critical for debugging. Given a program step `s` reading a variable `v`, finding the dynamic definition of `v` is challenging. Traditional methods require either (1) exhaustive instrumentation of all possible definitions of `v` in one run or (2) replicating the run to re-examine reads/writes - both costly. If `v` is defined in a library, instrumentation becomes expensive; for non-deterministic programs, replication is infea…

Requirements Development and Formalization for Reliable Code Generation: A Multi-Agent Vision
Xu Lu, Weisong Sun, Yiran Zhang, Ming Hu, Cong Tian, Zhi Jin, Yang Liu
https://arxiv.org/abs/2508.18675

Requirements Development and Formalization for Reliable Code Generation: A Multi-Agent Vision
Automated code generation has long been considered the holy grail of software engineering. The emergence of Large Language Models (LLMs) has catalyzed a revolutionary breakthrough in this area. However, existing methods that only rely on LLMs remain inadequate in the quality of generated code, offering no guarantees of satisfying practical requirements. They lack a systematic strategy for requirements development and modeling. Recently, LLM-based agents typically possess powerful abilities and …

LaQual: A Novel Framework for Automated Evaluation of LLM App Quality
Yan Wang, Xinyi Hou, Yanjie Zhao, Weiguo Lin, Haoyu Wang, Junjun Si
https://arxiv.org/abs/2508.18636 https:…

LaQual: A Novel Framework for Automated Evaluation of LLM App Quality
LLM app stores are quickly emerging as platforms that gather a wide range of intelligent applications based on LLMs, giving users many choices for content creation, coding support, education, and more. However, the current methods for ranking and recommending apps in these stores mostly rely on static metrics like user activity and favorites, which makes it hard for users to efficiently find high-quality apps. To address these challenges, we propose LaQual, an automated framework for evaluating…

How do Humans and LLMs Process Confusing Code?
Youssef Abdelsalam, Norman Peitek, Anna-Maria Maurer, Mariya Toneva, Sven Apel
https://arxiv.org/abs/2508.18547 https://

How do Humans and LLMs Process Confusing Code?
Already today, humans and programming assistants based on large language models (LLMs) collaborate in everyday programming tasks. Clearly, a misalignment between how LLMs and programmers comprehend code can lead to misunderstandings, inefficiencies, low code quality, and bugs. A key question in this space is whether humans and LLMs are confused by the same kind of code. This would not only guide our choices of integrating LLMs in software engineering workflows, but also inform about possible …

Engineering a Digital Twin for the Monitoring and Control of Beer Fermentation Sampling
Pierre-Emmanuel Goffi, Rapha\"el Tremblay, Bentley Oakes
https://arxiv.org/abs/2508.18452

Engineering a Digital Twin for the Monitoring and Control of Beer Fermentation Sampling
Successfully engineering interactive industrial DTs is a complex task, especially when implementing services beyond passive monitoring. We present here an experience report on engineering a safety-critical digital twin (DT) for beer fermentation monitoring, which provides continual sampling and reduces manual sampling time by 91%. We document our systematic methodology and practical solutions for implementing bidirectional DTs in industrial environments. This includes our three-phase engineerin…

DTInsight: A Tool for Explicit, Interactive, and Continuous Digital Twin Reporting
K\'erian Fiter, Louis Malassign\'e-Onfroy, Bentley Oakes
https://arxiv.org/abs/2508.18431

DTInsight: A Tool for Explicit, Interactive, and Continuous Digital Twin Reporting
With Digital Twin (DT) construction and evolution occurring over time, stakeholders require tools to understand the current characteristics and conceptual architecture of the system at any time. We introduce DTInsight, a systematic and automated tool and methodology for producing continuous reporting for DTs. DTInsight offers three key features: (a) an interactive conceptual architecture visualization of DTs; (b) generation of summaries of DT characteristics based on ontological data; and (c) i…

Training Language Model Agents to Find Vulnerabilities with CTF-Dojo
Terry Yue Zhuo, Dingmin Wang, Hantian Ding, Varun Kumar, Zijian Wang
https://arxiv.org/abs/2508.18370 https:…

Training Language Model Agents to Find Vulnerabilities with CTF-Dojo
Large language models (LLMs) have demonstrated exceptional capabilities when trained within executable runtime environments, notably excelling at software engineering tasks through verified feedback loops. Yet, scalable and generalizable execution-grounded environments remain scarce, limiting progress in training more capable ML agents. We introduce CTF-Dojo, the first large-scale executable runtime tailored for training LLMs with verifiable feedback, featuring 658 fully functional Capture-The-…

[2025-08-27 Wed (UTC), 12 new articles found for cs.SE Software Engineering]
toXiv_bot_toot

An object-centric core metamodel for IoT-enhanced event logs
Yannis Bertrand, Christian Imenkamp, Lukas Malburg, Matthias Ehrendorfer, Marco Franceschetti, Joscha Gr\"uger, Francesco Leotta, J\"urgen Mangler, Ronny Seiger, Agnes Koschmider, Stefanie Rinderle-Ma, Barbara Weber, Estefania Serral
https://arxiv.org/abs/250…

An object-centric core metamodel for IoT-enhanced event logs
Advances in Internet-of-Things (IoT) technologies have prompted the integration of IoT devices with business processes (BPs) in many organizations across various sectors, such as manufacturing, healthcare and smart spaces. The proliferation of IoT devices leads to the generation of large amounts of IoT data providing a window on the physical context of BPs, which facilitates the discovery of new insights about BPs using process mining (PM) techniques. However, to achieve these benefits, IoT dat…

KOALA: a Configurable Tool for Collecting IDE Data When Solving Programming Tasks
Daniil Karol, Elizaveta Artser, Ilya Vlasov, Yaroslav Golubev, Hieke Keuning, Anastasiia Birillo
https://arxiv.org/abs/2506.21266

KOALA: a Configurable Tool for Collecting IDE Data When Solving Programming Tasks
Collecting data of students solving programming tasks is incredibly valuable for researchers and educators. It allows verifying that the students correctly apply the features and concepts they are taught, or finding students' misconceptions. However, existing data collection tools have limitations, e.g., no control over the granularity of the collected code, not collecting the specific events of the programming environment used, and overall being hard to configure. To overcome these limitatio…

How Good Are Synthetic Requirements ? Evaluating LLM-Generated Datasets for AI4RE
Abdelkarim El-Hajjami, Camille Salinesi
https://arxiv.org/abs/2506.21138 …

How Good Are Synthetic Requirements ? Evaluating LLM-Generated Datasets for AI4RE
The shortage of publicly available, labeled requirements datasets remains a major barrier to advancing Artificial Intelligence for Requirements Engineering (AI4RE). While Large Language Models offer promising capabilities for synthetic data generation, systematic approaches to control and optimize the quality of generated requirements remain underexplored. This paper presents Synthline v1, an enhanced Product Line approach for generating synthetic requirements data that extends our earlier v0 v…

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- ToolScan: A Benchmark for Characterizing Errors in Tool-Use LLMs
Shirley Kokane, et al.

Exploring Micro Frontends: A Case Study Application in E-Commerce
Ricardo Hideki Hangai Kojo (University of S\~ao Paulo), Luiz Fernando Corte Real (University of S\~ao Paulo), Renato Cordeiro Ferreira (University of S\~ao Paulo, Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University), Thatiane de Oliveira Rosa (University of S\~ao Paulo, Federal Institute of Tocantins), Alfredo Goldman (University of S\~ao Paulo)

Exploring Micro Frontends: A Case Study Application in E-Commerce
In the micro frontends architectural style, the frontend is divided into smaller components, which can range from a simple button to an entire page. The goal is to improve scalability, resilience, and team independence, albeit at the cost of increased complexity and infrastructure demands. This paper seeks to understand when it is worth adopting micro frontends, particularly in the context of industry. To achieve this, we conducted an investigation into the state of the art of micro frontends, …

$T^3$: Multi-level Tree-based Automatic Program Repair with Large Language Models
Quanming Liu, Xupeng Bu, Zhichao Yan, Ru Li
https://arxiv.org/abs/2506.21211

$T^3$: Multi-level Tree-based Automatic Program Repair with Large Language Models
Automatic Program Repair (APR) is a core technology in software development and maintenance, with aims to enable automated defect repair with minimal human intervention. In recent years, the substantial advancements in Large Language Models (LLMs) and the Chain-of-Thought (CoT) techniques have significantly enhanced the reasoning capabilities of these models. However, due to the complex logic and multi-step reasoning ability needed, the application of CoT techniques in the APR domain remains in…

Boosting Vulnerability Detection with Inter-function Multilateral Association Insights
Shaojian Qiu, Mengyang Huang, Jiahao Cheng
https://arxiv.org/abs/2506.21014

Boosting Vulnerability Detection with Inter-function Multilateral Association Insights
Vulnerability detection is a crucial yet challenging technique for ensuring the security of software systems. Currently, most deep learning-based vulnerability detection methods focus on stand-alone functions, neglecting the complex inter-function interrelations, particularly the multilateral associations. This oversight can fail to detect vulnerabilities in these interrelations. To address this gap, we present an Inter-Function Multilateral Association analysis framework for Vulnerability Dete…

Complex Model Transformations by Reinforcement Learning with Uncertain Human Guidance
Kyanna Dagenais, Istvan David
https://arxiv.org/abs/2506.20883 https:…

Complex Model Transformations by Reinforcement Learning with Uncertain Human Guidance
Model-driven engineering problems often require complex model transformations (MTs), i.e., MTs that are chained in extensive sequences. Pertinent examples of such problems include model synchronization, automated model repair, and design space exploration. Manually developing complex MTs is an error-prone and often infeasible process. Reinforcement learning (RL) is an apt way to alleviate these issues. In RL, an autonomous agent explores the state space through trial and error to identify benef…

Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation
Md Toufique Hasan, Muhammad Waseem, Kai-Kristian Kemell, Ayman Asad Khan, Mika Saari, Pekka Abrahamsson
https://arxiv.org/abs/2506.20869

Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation
Retrieval-Augmented Generation (RAG) systems are emerging as a key approach for grounding Large Language Models (LLMs) in external knowledge, addressing limitations in factual accuracy and contextual relevance. However, there is a lack of empirical studies that report on the development of RAG-based implementations grounded in real-world use cases, evaluated through general user involvement, and accompanied by systematic documentation of lessons learned. This paper presents five domain-specific…

Generating Reliable Adverse event Profiles for Health through Automated Integrated Data (GRAPH-AID): A Semi-Automated Ontology Building Approach
Srikar Reddy Gadusu, Larry Callahan, Samir Lababidi, Arunasri Nishtala, Sophia Healey, Hande McGinty
https://arxiv.org/abs/2506.20851

Generating Reliable Adverse event Profiles for Health through Automated Integrated Data (GRAPH-AID): A Semi-Automated Ontology Building Approach
As data and knowledge expand rapidly, adopting systematic methodologies for ontology generation has become crucial. With the daily increases in data volumes and frequent content changes, the demand for databases to store and retrieve information for the creation of knowledge graphs has become increasingly urgent. The previously established Knowledge Acquisition and Representation Methodology (KNARM) outlines a systematic approach to address these challenges and create knowledge graphs. However,…

Agile Management for Machine Learning: A Systematic Mapping Study
Lucas Romao, Hugo Villamizar, Romeu Oliveira, Silvio Alonso, Marcos Kalinowski
https://arxiv.org/abs/2506.20759

Agile Management for Machine Learning: A Systematic Mapping Study
[Context] Machine learning (ML)-enabled systems are present in our society, driving significant digital transformations. The dynamic nature of ML development, characterized by experimental cycles and rapid changes in data, poses challenges to traditional project management. Agile methods, with their flexibility and incremental delivery, seem well-suited to address this dynamism. However, it is unclear how to effectively apply these methods in the context of ML-enabled systems, where challenges …

Domain Knowledge in Requirements Engineering: A Systematic Mapping Study
Marina Ara\'ujo, J\'ulia Ara\'ujo, Romeu Oliveira, Lucas Romao, Marcos Kalinowski
https://arxiv.org/abs/2506.20754

Domain Knowledge in Requirements Engineering: A Systematic Mapping Study
[Context] Domain knowledge is recognized as a key component for the success of Requirements Engineering (RE), as it provides the conceptual support needed to understand the system context, ensure alignment with stakeholder needs, and reduce ambiguity in requirements specification. Despite its relevance, the scientific literature still lacks a systematic consolidation of how domain knowledge can be effectively used and operationalized in RE. [Goal] This paper addresses this gap by offering a com…