Tootfinder

Trying to use old USB hard drives I found in a drawer as additional local backup storage is not going well.
One disconnects after some time. Another stays connected but has a bad block.

Found it, but I can’t get to it! Nooooo!

#Journalism school: "Headings should be concise and easy to understand."
Yahoo! Sport:

On Saturday I met Freddie greyhound, Mandy greyhound's nephew¹. Mandy passed away in 2021.
Coincidentally both homed by WGW and Freddie now lives a few miles from where Mandy lived her pet life. His human knows Mandy's humans well.
Considering the rather small greyhound gene pool this is not unusual. Mandy's sire has had over 5,000 offspring so far.
#Greyhounds

🔊 #NowPlaying on #BBCRadio3:
#Radio3InConcert
- The Ulster Orchestra Season Finale- Shostakovich and Walton
The Ulster Orchestra perform music by William Walton - his overture Portsmouth Point and the Cello Concerto, featuring soloist Nicolas Altstaedt - and Shostakovich's Symphony No 5.
Relisten now 👇
https://www.bbc.co.uk/programmes/m002cymh

Signs we made for #NoKings protest in #Pasadena. Lots of people showed up and the signs turned out pretty good! Photos by @…

Signs we made for #NoKings protest in #Pasadena. Lots of people showed up and the signs turned out pretty good! Photos by @…

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Supervised Semantic Similarity-based Conflict Detection Algorithm: S3CDA
Garima Malik, Mucahit Cevik, Ayse Basar, Devang Parikh

On Saturday I met Freddie greyhound, Mandy greyhound's nephew¹. Mandy passed away in 2021.
Coincidentally both homed by WGW and Freddie now lives a few miles from where Mandy lived her pet life. His human knows Mandy's humans well.
Considering the rather small greyhound gene pool this is not unusual. Mandy's sire has had over 5,000 offspring so far.
#Greyhounds

🔊 #NowPlaying on #BBCRadio3:
#ClassicalMixtape
- Your daily classical soundtrack
null
Relisten now 👇
https://www.bbc.co.uk/programmes/m002cymf

Security Debt in Practice: Nuanced Insights from Practitioners
Chaima Boufaied, Taher Ghaleb, Zainab Masood
https://arxiv.org/abs/2507.11362 https://

Security Debt in Practice: Nuanced Insights from Practitioners
With the increasing reliance on software and automation nowadays, tight deadlines, limited resources, and prioritization of functionality over security can lead to insecure coding practices. When not handled properly, these constraints cause unaddressed security vulnerabilities to accumulate over time, forming Security Debts (SDs). Despite their critical importance, there is limited empirical evidence on how software practitioners perceive, manage, and communicate SDs in real-world settings. In…

🇺🇦 #NowPlaying on BBCRadio3's #InTune
Marc-André Hamelin & Marc-André Hamelin:
🎵 Study No 3 after Paganini-Liszt
#MarcAndréHamelin

RefModel: Detecting Refactorings using Foundation Models
Pedro Sim\~oes, Rohit Gheyi, Rian Melo, Jonhnanthan Oliveira, M\'arcio Ribeiro, Wesley K. G. Assun\c{c}\~ao
https://arxiv.org/abs/2507.11346

RefModel: Detecting Refactorings using Foundation Models
Refactoring is a common software engineering practice that improves code quality without altering program behavior. Although tools like ReExtractor+, RefactoringMiner, and RefDiff have been developed to detect refactorings automatically, they rely on complex rule definitions and static analysis, making them difficult to extend and generalize to other programming languages. In this paper, we investigate the viability of using foundation models for refactoring detection, implemented in a tool nam…

🔊 #NowPlaying on #BBCRadio3:
#ThroughTheNight
- American Landscapes
From Turin, the RAI National Symphony Orchestra and conductor David Greilsammer perform music by Grofé, Daugherty, Ives and Copland. Penny Gore presents.
Relisten now 👇
https://www.bbc.co.uk/programmes/m002cyms

An Empirical Study of Multi-Agent RAG for Real-World University Admissions Counseling
Anh Nguyen-Duc, Chien Vu Manh, Bao Anh Tran, Viet Phuong Ngo, Luan Le Chi, Anh Quang Nguyen
https://arxiv.org/abs/2507.11272

An Empirical Study of Multi-Agent RAG for Real-World University Admissions Counseling
This paper presents MARAUS (Multi-Agent and Retrieval-Augmented University Admission System), a real-world deployment of a conversational AI platform for higher education admissions counseling in Vietnam. While large language models (LLMs) offer potential for automating advisory tasks, most existing solutions remain limited to prototypes or synthetic benchmarks. MARAUS addresses this gap by combining hybrid retrieval, multi-agent orchestration, and LLM-based generation into a system tailored fo…

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
David Preston:
🎵 Casablanca, 1913 (feat. Kit Downes, Seb Rochford, Kevin Glasgow)
#DavidPreston
https://open.spotify.com/track/7JQ0dL7dtXbD3zFmk4DKAC

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
The Cannonball Adderley Quintet:
🎵 Exodus (Live)
#TheCannonballAdderleyQuintet
https://open.spotify.com/track/4R6IJsYHnYobG0zfAO3oxn

New Formulation of DNN Statistical Mutation Killing for Ensuring Monotonicity: A Technical Report
Jinhan Kim, Nargiz Humbatova, Gunel Jahangirova, Shin Yoo, Paolo Tonella
https://arxiv.org/abs/2507.11199

New Formulation of DNN Statistical Mutation Killing for Ensuring Monotonicity: A Technical Report
Mutation testing has emerged as a powerful technique for evaluating the effectiveness of test suites for Deep Neural Networks. Among existing approaches, the statistical mutant killing criterion of DeepCrime has leveraged statistical testing to determine whether a mutant significantly differs from the original model. However, it suffers from a critical limitation: it violates the monotonicity property, meaning that expanding a test set may result in previously killed mutants no longer being cla…

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
Cecil McBee:
🎵 Tulsa Black
#CecilMcBee
https://open.spotify.com/track/2Gy31LO3Uor4TFsnTo7nsV

Automata Models for Effective Bug Description
Tom Yaacov, Gera Weiss, Gal Amram, Avi Hayoun
https://arxiv.org/abs/2507.11146 https://…

Automata Models for Effective Bug Description
Debugging complex systems is a crucial yet time-consuming task. This paper presents the use of automata learning and testing techniques to obtain concise and informative bug descriptions. We introduce the concepts of Failure Explanations (FE), Eventual Failure Explanations (EFE), and Early Detection (ED) to provide meaningful summaries of failing behavior patterns. By factoring out irrelevant information and focusing on essential test patterns, our approach aims to enhance bug detection and und…

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
Camila Meza:
🎵 Utopia
#CamilaMeza
https://open.spotify.com/track/34ApnWJPjZPMAJqZw0MxPV

MT4DP: Data Poisoning Attack Detection for DL-based Code Search Models via Metamorphic Testing
Gong Chen, Wenjie Liu, Xiaoyuan Xie, Xunzhu Tang, Tegawend\'e F. Bissyand\'e, Songqiang Chen
https://arxiv.org/abs/2507.11092

MT4DP: Data Poisoning Attack Detection for DL-based Code Search Models via Metamorphic Testing
Recently, several studies have indicated that data poisoning attacks pose a severe security threat to deep learning-based (DL-based) code search models. Attackers inject carefully crafted malicious patterns into the training data, misleading the code search model to learn these patterns during training. During the usage of the poisoned code search model for inference, once the malicious pattern is triggered, the model tends to rank the vulnerability code higher. However, existing detection meth…

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
The Kanpai Quartet:
🎵 Pearly Gates
#TheKanpaiQuartet
https://open.spotify.com/track/1UpONVbRNgkkYq2PLU5bwo

SWE-MERA: A Dynamic Benchmark for Agenticly Evaluating Large Language Models on Software Engineering Tasks
Pavel Adamenko, Mikhail Ivanov, Aidar Valeev, Rodion Levichev, Pavel Zadorozhny, Ivan Lopatin, Dmitry Babayev, Alena Fenogenova, Valentin Malykh
https://arxiv.org/abs/2507.11059

SWE-MERA: A Dynamic Benchmark for Agenticly Evaluating Large Language Models on Software Engineering Tasks
The rapid advancement of Large Language Models (LLMs) in software engineering has revealed critical limitations in existing benchmarks, particularly the widely used SWE-bench dataset. Recent studies have uncovered severe data contamination issues, e.g. SWE-bench reports 32.67% of successful patches involve direct solution leakage and 31.08\% pass due to inadequate test cases. We introduce SWE-MERA, a dynamic, continuously updated benchmark designed to address these fundamental challenges throug…

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
New Regency Orchestra:
🎵 Para Los Papines (Momoko Gill Remix)
#NewRegencyOrchestra
https://open.spotify.com/track/6qk9njCOVQJUd9cNaX89ud

Evaluating Generated Commit Messages with Large Language Models
Qunhong Zeng, Yuxia Zhang, Zexiong Ma, Bo Jiang, Ningyuan Sun, Klaas-Jan Stol, Xingyu Mou, Hui Liu
https://arxiv.org/abs/2507.10906

Evaluating Generated Commit Messages with Large Language Models
Commit messages are essential in software development as they serve to document and explain code changes. Yet, their quality often falls short in practice, with studies showing significant proportions of empty or inadequate messages. While automated commit message generation has advanced significantly, particularly with Large Language Models (LLMs), the evaluation of generated messages remains challenging. Traditional reference-based automatic metrics like BLEU, ROUGE-L, and METEOR have notable…

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
Beiggja:
🎵 Encore
#Beiggja
https://open.spotify.com/track/7nTTzZ5y1NTHQTObEgvsUF

🔊 #NowPlaying on #BBCRadio3:
#RoundMidnight
- A remastered classic from Cecil McBee
Soweto Kinch spins a track from double bass maestro Cecil McBee’s recently remastered debut album.
Relisten now 👇
https://www.bbc.co.uk/programmes/m002cymq

Past, Present and Future: Exploring Adaptive AI in Software Development Bots
Omar Elsisi, Glaucia Melo
https://arxiv.org/abs/2507.10822 https://

Past, Present and Future: Exploring Adaptive AI in Software Development Bots
Conversational agents, such as chatbots and virtual assistants, have become essential in software development, boosting productivity, collaboration, and automating various tasks. This paper examines the role of adaptive AI-powered conversational agents in software development, highlighting their ability to offer dynamic, context-aware assistance to developers. Unlike traditional rule-based systems, adaptive AI agents use machine learning and natural language processing to learn from interaction…

🇺🇦 #NowPlaying on BBCRadio3's #NightTracks
Danú:
🎵 Ten Thousand Miles
#Danú
https://open.spotify.com/track/2rjntmwJoCp0RsWszuUV0H

How Robust are LLM-Generated Library Imports? An Empirical Study using Stack Overflow
Jasmine Latendresse, SayedHassan Khatoonabadi, Emad Shihab
https://arxiv.org/abs/2507.10818

How Robust are LLM-Generated Library Imports? An Empirical Study using Stack Overflow
Software libraries are central to the functionality, security, and maintainability of modern code. As developers increasingly turn to Large Language Models (LLMs) to assist with programming tasks, understanding how these models recommend libraries is essential. In this paper, we conduct an empirical study of six state-of-the-art LLMs, both proprietary and open-source, by prompting them to solve real-world Python problems sourced from Stack Overflow. We analyze the types of libraries they import…

🇺🇦 #NowPlaying on BBCRadio3's #NightTracks
Ordnance Survey:
🎵 Seefin Passage Tomb
#OrdnanceSurvey

Towards a Closer Collaboration Between Practice and Research in Agile Software Development Workshop: A Summary and Research Agenda
Michael Neumann, Eva-Maria Sch\"on, Mali Senapathi, Maria Rauschenberger, Tiago Silva da Silva
https://arxiv.org/abs/2507.10785

Towards a Closer Collaboration Between Practice and Research in Agile Software Development Workshop: A Summary and Research Agenda
Agile software development principles and values have been widely adopted across various industries, influencing products and services globally. Despite its increasing popularity, a significant gap remains between research and practical implementation. This paper presents the findings of the first international workshop designed to foster collaboration between research and practice in agile software development. We discuss the main themes and factors identified by the workshop participants that…

🇺🇦 #NowPlaying on BBCRadio3's #ThroughTheNight
Georg Philipp Telemann, Jörg-Andreas Bötticher, Lea Sobbe, Hojin Kwon & Schola Cantorum Basiliensis:
🎵 Concerto in A minor for Two Recorders, TWV.52:a2
#LeaSobbe
https://open.spotify.com/track/5cU2fWMWAKxKhf5gVpaSkR

GenAI-Enabled Backlog Grooming in Agile Software Projects: An Empirical Study
Kasper Lien Oftebro, Anh Nguyen-Duc, Kai-Kristian Kemell
https://arxiv.org/abs/2507.10753

GenAI-Enabled Backlog Grooming in Agile Software Projects: An Empirical Study
Effective backlog management is critical for ensuring that development teams remain aligned with evolving requirements and stakeholder expectations. However, as product backlogs consistently grow in scale and complexity, they tend to become cluttered with redundant, outdated, or poorly defined tasks, complicating prioritization and decision making processes. This study investigates whether a generative-AI (GenAI) assistant can automate backlog grooming in Agile software projects without sacrifi…

🇺🇦 #NowPlaying on BBCRadio3's #ThroughTheNight
Jan Pieterszoon Sweelinck & Amsterdam Loeki Stardust Quartet:
🎵 Tes beaux yeux causent mon amour - chanson for 4 voices
#JanPieterszoonSweelinck
https://open.spotify.com/track/05hMSnsvKcZO93i2FruUBM

🔊 #NowPlaying on #BBCRadio3:
#Breakfast
- Start the day on the right note with classical music
Tom McKinney presents Radio 3's Breakfast show. To get in touch, email 3breakfast@bbc.co.uk
Relisten now 👇
https://www.bbc.co.uk/programmes/m002cywv

Toward Realistic Evaluations of Just-In-Time Vulnerability Prediction
Duong Nguyen, Thanh Le-Cong, Triet Huynh Minh Le, M. Ali Babar, Quyet-Thang Huynh
https://arxiv.org/abs/2507.10729

Toward Realistic Evaluations of Just-In-Time Vulnerability Prediction
Modern software systems are increasingly complex, presenting significant challenges in quality assurance. Just-in-time vulnerability prediction (JIT-VP) is a proactive approach to identifying vulnerable commits and providing early warnings about potential security risks. However, we observe that current JIT-VP evaluations rely on an idealized setting, where the evaluation datasets are artificially balanced, consisting exclusively of vulnerability-introducing and vulnerability-fixing commits. …

🇺🇦 #NowPlaying on BBCRadio3's #InTune
Wolfgang Amadeus Mozart, Vilde Frang, Maxim Rysanov, Arcangelo & Jonathan Cohen:
🎵 Sinfonia Concertante in E flat major K.364 for violin, viola and orch; 2nd mvt
#VildeFrang
https://open.spotify.com/track/0XHOl74BrJ6ApzvZwyRgxT

CodeAssistBench (CAB): Dataset & Benchmarking for Multi-turn Chat-Based Code Assistance
Myeongsoo Kim, Shweta Garg, Baishakhi Ray, Varun Kumar, Anoop Deoras
https://arxiv.org/abs/2507.10646

CodeAssistBench (CAB): Dataset & Benchmarking for Multi-turn Chat-Based Code Assistance
Programming assistants powered by large language models have transformed software development, yet most benchmarks focus narrowly on code generation tasks. Recent efforts like InfiBench and StackEval attempt to address this gap using Stack Overflow data but remain limited to single-turn interactions in isolated contexts, require significant manual curation, and fail to represent complete project environments. We introduce CodeAssistBench (CAB), the first benchmark framework for evaluating multi…

🔊 #NowPlaying on #BBCRadio3:
#InTune
- Live classical music for your commute
Petroc Trelawny is joined by the pianist Marc-André Hamelin, who performs at London's Wigmore Hall this week.
Relisten now 👇
https://www.bbc.co.uk/programmes/m002cyx5

A Code Comprehension Benchmark for Large Language Models for Code
Jayant Havare, Saurav Chaudhary, Ganesh Ramakrishnan, Kaushik Maharajan, Srikanth Tamilselvam
https://arxiv.org/abs/2507.10641

A Code Comprehension Benchmark for Large Language Models for Code
Large Language Models have shown impressive capabilities in coding tasks like code generation and code completion, as they have been trained on a large amount of code data. Also, since one of the core pretraining objectives is Next Token Prediction, these models tends to learn surface-level syntactic patterns in code. However, this does not guarantee code comprehension ability i.e. the ability to capture the semantics of the code. In our opinion, this is the reason why these models often underp…

SENSOR: An ML-Enhanced Online Annotation Tool to Uncover Privacy Concerns from User Reviews in Social-Media Applications
Labiba Farah, Mohammad Ridwan Kabir, Shohel Ahmed, MD Mohaymen Ul Anam, Md. Sakibul Islam
https://arxiv.org/abs/2507.10640

SENSOR: An ML-Enhanced Online Annotation Tool to Uncover Privacy Concerns from User Reviews in Social-Media Applications
The widespread use of social media applications has raised significant privacy concerns, often highlighted in user reviews. These reviews also provide developers with valuable insights into improving apps by addressing issues and introducing better features. However, the sheer volume and nuanced nature of reviews make manual identification and prioritization of privacy-related concerns challenging for developers. Previous studies have developed software utilities to automatically classify user …

ToolRegistry: A Protocol-Agnostic Tool Management Library for Function-Calling LLMs
Peng Ding
https://arxiv.org/abs/2507.10593 https://

ToolRegistry: A Protocol-Agnostic Tool Management Library for Function-Calling LLMs
Large Language Model (LLM) applications are increasingly relying on external tools to extend their capabilities beyond text generation. However, current tool integration approaches suffer from fragmentation, protocol limitations, and implementation complexity, leading to substantial development overhead. This paper presents Toolregistry, a protocol-agnostic tool management library that simplifies tool registration, representation, execution, and lifecycle management via a unified interface. Our…

Repairing Language Model Pipelines by Meta Self-Refining Competing Constraints at Runtime
Mojtaba Eshghie
https://arxiv.org/abs/2507.10590 https://

Repairing Language Model Pipelines by Meta Self-Refining Competing Constraints at Runtime
Language Model (LM) pipelines can dynamically refine their outputs against programmatic constraints. However, their effectiveness collapses when faced with competing soft constraints, leading to inefficient backtracking loops where satisfying one constraint violates another. We introduce Meta Self-Refining, a framework that equips LM pipelines with a meta-corrective layer to repair these competitions at runtime/inference-time. Our approach monitors the pipeline's execution history to detect osc…

🔊 #NowPlaying on #BBCRadio3:
#Radio3InConcert
- Janšcek’s Glagolitic Mass from Liverpool
The Royal Liverpool Philharmonic, conducted by Dinis Sousa, perform Beethoven's Symphony No.6 'Pastoral' and Janšček's Glagolitic Mass
Relisten now 👇
https://www.bbc.co.uk/programmes/m002cyp6

ARPaCCino: An Agentic-RAG for Policy as Code Compliance
Francesco Romeo, Luigi Arena, Francesco Blefari, Francesco Aurelio Pironti, Matteo Lupinacci, Angelo Furfaro
https://arxiv.org/abs/2507.10584

ARPaCCino: An Agentic-RAG for Policy as Code Compliance
Policy as Code (PaC) is a paradigm that encodes security and compliance policies into machine-readable formats, enabling automated enforcement in Infrastructure as Code (IaC) environments. However, its adoption is hindered by the complexity of policy languages and the risk of misconfigurations. In this work, we present ARPaCCino, an agentic system that combines Large Language Models (LLMs), Retrieval-Augmented-Generation (RAG), and tool-based validation to automate the generation and verificati…

🇺🇦 #NowPlaying on BBCRadio3's #ClassicalMixtape
George Ives, Gerald Drucker, Lyn Fletcher, Robert Heard, Sir Charles Groves, Camille Saint-Saëns, Philharmonia Orchestra, Kenneth Smith, Leslie Pearson & Michael Reeves:
🎵 Aquarium (Carnival of the Animals)
#GeorgeIves

$\texttt{Droid}$: A Resource Suite for AI-Generated Code Detection
Daniil Orel, Indraneil Paul, Iryna Gurevych, Preslav Nakov
https://arxiv.org/abs/2507.10583

$\texttt{Droid}$: A Resource Suite for AI-Generated Code Detection
In this work, we compile $\textbf{$\texttt{DroidCollection}$}$, the most extensive open data suite for training and evaluating machine-generated code detectors, comprising over a million code samples, seven programming languages, outputs from 43 coding models, and over three real-world coding domains. Alongside fully AI-generated samples, our collection includes human-AI co-authored code, as well as adversarial samples explicitly crafted to evade detection. Subsequently, we develop $\textbf{$\t…

🔊 #NowPlaying on #BBCRadio3:
#ClassicalMixtape
- Classical music for focus or relaxation
Take time out with a 30-minute soundscape of classical music, including Elgar, Shostakovich, JS Bach, Glazunov, Joanna Marsh, Handel and Grieg.
Relisten now 👇
https://www.bbc.co.uk/programmes/m001q798

[2025-07-16 Wed (UTC), 20 new articles found for cs.SE Software Engineering]
toXiv_bot_toot

🇺🇦 #NowPlaying on BBCRadio3's #InTune
Giuseppe Verdi, Francesco Maria Piave, Alexandre Dumas, Angela Gheorghiu, Frank Lopardo, Orchestra of the Royal Opera House, Covent Garden & Georg Solti:
🎵 La traviata, Act I: Follie! Follie! ...Sempre libera
#Spotify

🔊 #NowPlaying on #BBCRadio3:
#ThroughTheNight
- Ravel's Piano Concerto in G, from Stockholm
The Swedish Radio Symphony Orchestra performs with Lucas Debargue in Ravel's Piano Concerto in G. Opening the concert is Eleven Gates, a piece by Swedish composer Anders Hillborg.
Relisten now 👇
https://www.bbc.co.uk/programmes/m002cypg

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMs
htt…

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
Tanhai Collective:
🎵 Blue Note
#TanhaiCollective
https://open.spotify.com/track/3ukcMPzV23eKxUKLmQQtEZ

LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming?
Zihan Zheng, Zerui Cheng, Zeyu Shen, Shang Zhou, Kaiyuan Liu, Hansen He, Dongruixuan Li, Stanley Wei, Hangyi Hao, Jianzhu Yao, Peiyao Sheng, Zixuan Wang, Wenhao Chai, Aleksandra Korolova, Peter Henderson, Sanjeev Arora, Pramod Viswanath, Jingbo Shang, Saining Xie
http…

LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming?
Recent reports claim that large language models (LLMs) now outperform elite humans in competitive programming. Drawing on knowledge from a group of medalists in international algorithmic contests, we revisit this claim, examining how LLMs differ from human experts and where limitations still remain. We introduce LiveCodeBench Pro, a benchmark composed of problems from Codeforces, ICPC, and IOI that are continuously updated to reduce the likelihood of data contamination. A team of Olympiad medal…

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
Empirical:
🎵 Gazzelloni
#Empirical
https://open.spotify.com/track/7t2lQEcUxvyqsuyRzqKzC8

A Short Survey on Formalising Software Requirements using Large Language Models
Arshad Beg, Diarmuid O'Donoghue, Rosemary Monahan
https://arxiv.org/abs/2506.11874

A Short Survey on Formalising Software Requirements using Large Language Models
This paper presents a focused literature survey on the use of large language models (LLM) to assist in writing formal specifications for software. A summary of thirty-five key papers is presented, including examples for specifying programs written in Dafny, C and Java. This paper arose from the project VERIFAI - Traceability and verification of natural language requirements that addresses the challenges in writing formal specifications from requirements that are expressed in natural language. O…

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
Mamiko Kitaura:
🎵 Escape From The Moon
#MamikoKitaura
https://open.spotify.com/track/4B3q9z7YInw8Uu1YbpZCCD

Classification of Quality Characteristics in Online User Feedback using Linguistic Analysis, Crowdsourcing and LLMs
Eduard C. Groen, Fabiano Dalpiaz, Martijn van Vliet, Boris Winter, Joerg Doerr, Sjaak Brinkkemper
https://arxiv.org/abs/2506.11722

Classification of Quality Characteristics in Online User Feedback using Linguistic Analysis, Crowdsourcing and LLMs
Software qualities such as usability or reliability are among the strongest determinants of mobile app user satisfaction and constitute a significant portion of online user feedback on software products, making it a valuable source of quality-related feedback to guide the development process. The abundance of online user feedback warrants the automated identification of quality characteristics, but the online user feedback's heterogeneity and the lack of appropriate training corpora limit the a…

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
Donovan Haffner:
🎵 The Sublime
#DonovanHaffner
https://open.spotify.com/track/3lwVb9R5KihmIw1U00q6l2

SoK: Automated Vulnerability Repair: Methods, Tools, and Assessments
Yiwei Hu, Zhen Li, Kedie Shu, Shenghua Guan, Deqing Zou, Shouhuai Xu, Bin Yuan, Hai Jin
https://arxiv.org/abs/2506.11697

SoK: Automated Vulnerability Repair: Methods, Tools, and Assessments
The increasing complexity of software has led to the steady growth of vulnerabilities. Vulnerability repair investigates how to fix software vulnerabilities. Manual vulnerability repair is labor-intensive and time-consuming because it relies on human experts, highlighting the importance of Automated Vulnerability Repair (AVR). In this SoK, we present the systematization of AVR methods through the three steps of AVR workflow: vulnerability analysis, patch generation, and patch validation. We ass…

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
Orchestra Mambo International:
🎵 Mi Mambo
#OrchestraMamboInternational

An Empirical study on LLM-based Log Retrieval for Software Engineering Metadata Management
Simin Sun, Yuchuan Jin, Miroslaw Staron
https://arxiv.org/abs/2506.11659

An Empirical study on LLM-based Log Retrieval for Software Engineering Metadata Management
Developing autonomous driving systems (ADSs) involves generating and storing extensive log data from test drives, which is essential for verification, research, and simulation. However, these high-frequency logs, recorded over varying durations, pose challenges for developers attempting to locate specific driving scenarios. This difficulty arises due to the wide range of signals representing various vehicle components and driving conditions, as well as unfamiliarity of some developers' with the…

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
Àbšse:
🎵 Bloom (Live at Brewery)
#Àbáse

Accelerating Delta Debugging through Probabilistic Monotonicity Assessment
Yonggang Tao, Jingling Xue
https://arxiv.org/abs/2506.11614 https://

Accelerating Delta Debugging through Probabilistic Monotonicity Assessment
Delta debugging assumes search space monotonicity: if a program causes a failure, any supersets of that program will also induce the same failure, permitting the exclusion of subsets of non-failure-inducing programs. However, this assumption does not always hold in practice. This paper introduces Probabilistic Monotonicity Assessment (PMA), enhancing the efficiency of DDMIN-style algorithms without sacrificing effectiveness. PMA dynamically models and assesses the search space's monotonicity ba…

🔊 #NowPlaying on #BBCRadio3:
#RoundMidnight
- Donovan Haffner’s 4/4
This week rising UK alto saxophonist Donovan Haffner shares four albums that have resonated with him.
Relisten now 👇
https://www.bbc.co.uk/programmes/m002cypd

Understanding API Usage and Testing: An Empirical Study of C Libraries
Ahmed Zaki, Cristian Cadar
https://arxiv.org/abs/2506.11598 https://

Understanding API Usage and Testing: An Empirical Study of C Libraries
For library developers, understanding how their Application Programming Interfaces (APIs) are used in the field can be invaluable. Knowing how clients are using their APIs allows for data-driven decisions on prioritising bug reports, feature requests, and testing activities. For example, the priority of a bug report concerning an API can be partly determined by how widely that API is used. In this paper, we present an empirical study in which we analyse API usage across 21 popular open-source…

🇺🇦 #NowPlaying on BBCRadio3's #NightTracks
Lucrecia Dalt:
🎵 divina
#LucreciaDalt
https://open.spotify.com/track/2bj8PJCfKpALfWvBp78QV3

Further Evidence on a Controversial Topic about Human-Based Experiments: Professionals vs. Students
Simone Romano, Francesco Paolo Sferratore, Giuseppe Scanniello
https://arxiv.org/abs/2506.11597

Further Evidence on a Controversial Topic about Human-Based Experiments: Professionals vs. Students
Most Software Engineering (SE) human-based controlled experiments rely on students as participants, raising concerns about their external validity. Specifically, the realism of results obtained from students and their applicability to the software industry remains in question. In this short paper, we bring further evidence on this controversial point. To do so, we compare 62 students and 42 software professionals on a bug-fixing task on the same Java program. The students were enrolled in a Bac…

🇺🇦 #NowPlaying on BBCRadio3's #NightTracks
Qasim Naqvi:
🎵 Plastic Glacier
#QasimNaqvi
https://open.spotify.com/track/7gvKRnMCQUpZ0fLPWKUutw

Retrieval-Augmented Code Review Comment Generation
Hyunsun Hong, Jongmoon Baik
https://arxiv.org/abs/2506.11591 https://arxiv.org/pdf…

Retrieval-Augmented Code Review Comment Generation
Automated code review comment generation (RCG) aims to assist developers by automatically producing natural language feedback for code changes. Existing approaches are primarily either generation-based, using pretrained language models, or information retrieval-based (IR), reusing comments from similar past examples. While generation-based methods leverage code-specific pretraining on large code-natural language corpora to learn semantic relationships between code and natural language, they oft…

🔊 #NowPlaying on #BBCRadio3:
#Breakfast
- The best classical music wake-up call
Tom McKinney presents Radio 3's Breakfast show. To get in touch, email 3breakfast@bbc.co.uk
Relisten now 👇
https://www.bbc.co.uk/programmes/m002cym2

MBSR at Work: Perspectives from an Instructor and Software Developers
Simone Romano, Alberto Conforti, Gloria Guidetti, Sara Viotti, Rachele Ceschin, Giuseppe Scanniello
https://arxiv.org/abs/2506.11588

MBSR at Work: Perspectives from an Instructor and Software Developers
In this paper, we present the preliminary findings from a qualitative study (i.e., semi-structured interviews) on how a Mindfulness-Based Stress Reduction (MBSR) program, carried out in the Software Development (SD) working context, is perceived by the software developers of a multinational company who participated in the MBSR program and by the instructor who led it. MBSR is a deeply personal and experiential practice in helping individuals manage stress, particularly in high-pressure environm…

🇺🇦 #NowPlaying on BBCRadio3's #EssentialClassics
Johannes Brahms, Renaud Capuçon, Gautier Capuçon, Gustav Mahler Youth Orchestra & Myung-Whun Chung:
🎵 Concerto for violin & cello in A minor, Op 102 (2nd mvt, Andante)
https://open.spotify.com/track/3XigeCyh5UWugh1ttRUggf

Identifying Helpful Context for LLM-based Vulnerability Repair: A Preliminary Study
G\'abor Antal, Bence Bogenf\"urst, Rudolf Ferenc, P\'eter Heged\H{u}s
https://arxiv.org/abs/2506.11561

Identifying Helpful Context for LLM-based Vulnerability Repair: A Preliminary Study
Recent advancements in large language models (LLMs) have shown promise for automated vulnerability detection and repair in software systems. This paper investigates the performance of GPT-4o in repairing Java vulnerabilities from a widely used dataset (Vul4J), exploring how different contextual information affects automated vulnerability repair (AVR) capabilities. We compare the latest GPT-4o's performance against previous results with GPT-4 using identical prompts. We evaluated nine additional…

🔊 #NowPlaying on #BBCRadio3:
#ClassicalLive
- Brahms: a sonata in Belfast and a symphony in Cardiff
Elizabeth Alker showcases the best performances from the UK and beyond, including the BBC National Orchestra of Wales performing Brahms’s Symphony No. 4 in Cardiff.
Relisten now 👇
https://www.bbc.co.uk/programmes/m002cym6

Leveraging GPT-4 for Vulnerability-Witnessing Unit Test Generation
G\'abor Antal, D\'enes B\'an, Martin Isztin, Rudolf Ferenc, P\'eter Heged\H{u}s
https://arxiv.org/abs/2506.11559

Leveraging GPT-4 for Vulnerability-Witnessing Unit Test Generation
In the life-cycle of software development, testing plays a crucial role in quality assurance. Proper testing not only increases code coverage and prevents regressions but it can also ensure that any potential vulnerabilities in the software are identified and effectively fixed. However, creating such tests is a complex, resource-consuming manual process. To help developers and security experts, this paper explores the automatic unit test generation capability of one of the most widely used larg…

Augmenting the Generality and Performance of Large Language Models for Software Engineering
Fabian C. Pe\~na
https://arxiv.org/abs/2506.11548 https://

Augmenting the Generality and Performance of Large Language Models for Software Engineering
Large Language Models (LLMs) are revolutionizing software engineering (SE), with special emphasis on code generation and analysis. However, their applications to broader SE practices including conceptualization, design, and other non-code tasks, remain partially underexplored. This research aims to augment the generality and performance of LLMs for SE by (1) advancing the understanding of how LLMs with different characteristics perform on various non-code tasks, (2) evaluating them as sources o…

A Procedural Framework for Assessing the Desirability of Process Deviations
Michael Grohs, Nadine Cordes, Jana-Rebecca Rehse
https://arxiv.org/abs/2506.11525

A Procedural Framework for Assessing the Desirability of Process Deviations
Conformance checking techniques help process analysts to identify where and how process executions deviate from a process model. However, they cannot determine the desirability of these deviations, i.e., whether they are problematic, acceptable or even beneficial for the process. Such desirability assessments are crucial to derive actions, but process analysts typically conduct them in a manual, ad-hoc way, which can be time-consuming, subjective, and irreplicable. To address this problem, this…

VulStamp: Vulnerability Assessment using Large Language Model
Haoshen, Ming Hu, Xiaofei Xie, Jiaye Li, Mingsong Chen
https://arxiv.org/abs/2506.11484 https…

VulStamp: Vulnerability Assessment using Large Language Model
Although modern vulnerability detection tools enable developers to efficiently identify numerous security flaws, indiscriminate remediation efforts often lead to superfluous development expenses. This is particularly true given that a substantial portion of detected vulnerabilities either possess low exploitability or would incur negligible impact in practical operational environments. Consequently, vulnerability severity assessment has emerged as a critical component in optimizing software dev…

Understanding the Issue Types in Open Source Blockchain-based Software Projects with the Transformer-based BERTopic
Md Nahidul Islam Opu, Md Shahidul Islam, Sara Rouhani, Shaiful Chowdhury
https://arxiv.org/abs/2506.11451

Understanding the Issue Types in Open Source Blockchain-based Software Projects with the Transformer-based BERTopic
Blockchain-based software systems are increasingly deployed across diverse domains, yet a systematic understanding of their development challenges remains limited. This paper presents a large-scale empirical study of 497,742 issues mined from 1,209 open-source blockchain projects hosted on GitHub. Employing BERTopic, a transformer-based topic modeling technique, we identify 49 distinct issue topics and organize them hierarchically into 11 major subcategories. Our analysis reveals that both gene…

ReVeal: Self-Evolving Code Agents via Iterative Generation-Verification
Yiyang Jin, Kunzhao Xu, Hang Li, Xueting Han, Yanmin Zhou, Cheng Li, Jing Bai
https://arxiv.org/abs/2506.11442

ReVeal: Self-Evolving Code Agents via Iterative Generation-Verification
Recent advances in reinforcement learning (RL) with verifiable outcome rewards have significantly improved the reasoning capabilities of large language models (LLMs), especially when combined with multi-turn tool interactions. However, existing methods lack both meaningful verification signals from realistic environments and explicit optimization for verification, leading to unreliable self-verification. To address these limitations, we propose ReVeal, a multi-turn reinforcement learning framew…

A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline
Yupeng Jiang, Yao Deng, Sebastian Schroder, Linfeng Liang, Suhaas Gambhir, Alice James, Avishkar Seth, James Pirrie, Yihao Zhang, Xi Zheng
https://arxiv.org/abs/2506.11400

A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline
Autonomous drones are rapidly reshaping industries ranging from aerial delivery and infrastructure inspection to environmental monitoring and disaster response. Ensuring the safety, reliability, and efficiency of these systems is paramount as they transition from research prototypes to mission-critical platforms. This paper presents a step-by-step guide to establishing a robust autonomous drone testing pipeline, covering each critical stage: Software-in-the-Loop (SIL) Simulation Testing, Hardwa…

A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems
Renato Cordeiro Ferreira (University of S\~ao Paulo, Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)
https://arxiv.org/abs/2506.11295

A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems
How can the complexity of ML-enabled systems be managed effectively? The goal of this research is to investigate how complexity affects ML-Enabled Systems (MLES). To address this question, this research aims to introduce a metrics-based architectural model to characterize the complexity of MLES. The goal is to support architectural decisions, providing a guideline for the inception and growth of these systems. This paper brings, side-by-side, the architecture representation of two systems that …

Invocable APIs derived from NL2SQL datasets for LLM Tool-Calling Evaluation
Benjamin Elder, Anupama Murthi, Jungkoo Kang, Ankita Rajaram Naik, Kiran Kate, Kinjal Basu, Danish Contractor
https://arxiv.org/abs/2506.11266

Invocable APIs derived from NL2SQL datasets for LLM Tool-Calling Evaluation
Large language models (LLMs) are routinely deployed as agentic systems, with access to tools that interact with live environments to accomplish tasks. In enterprise deployments these systems need to interact with API collections that can be extremely large and complex, often backed by databases. In order to create datasets with such characteristics, we explore how existing NL2SQL (Natural Language to SQL query) datasets can be used to automatically create NL2API datasets. Specifically, this wor…

LLM-as-a-Judge for Reference-less Automatic Code Validation and Refinement for Natural Language to Bash in IT Automation
Ngoc Phuoc An Vo, Brent Paulovicks, Vadim Sheinin
https://arxiv.org/abs/2506.11237

LLM-as-a-Judge for Reference-less Automatic Code Validation and Refinement for Natural Language to Bash in IT Automation
In an effort to automatically evaluate and select the best model and improve code quality for automatic incident remediation in IT Automation, it is crucial to verify if the generated code for remediation action is syntactically and semantically correct and whether it can be executed correctly as intended. There are three approaches: 1) conventional methods use surface form similarity metrics (token match, exact match, etc.) which have numerous limitations, 2) execution-based evaluation focuses…

Beyond Formal Semantics for Capabilities and Skills: Model Context Protocol in Manufacturing
Luis Miguel Vieira da Silva, Aljosha K\"ocher, Felix Gehlhoff
https://arxiv.org/abs/2506.11180

Beyond Formal Semantics for Capabilities and Skills: Model Context Protocol in Manufacturing
Explicit modeling of capabilities and skills -- whether based on ontologies, Asset Administration Shells, or other technologies -- requires considerable manual effort and often results in representations that are not easily accessible to Large Language Models (LLMs). In this work-in-progress paper, we present an alternative approach based on the recently introduced Model Context Protocol (MCP). MCP allows systems to expose functionality through a standardized interface that is directly consumab…

Model Discovery and Graph Simulation: A Lightweight Alternative to Chaos Engineering
Anatoly A. Krasnovsky, Alexander Zorkin
https://arxiv.org/abs/2506.11176

Model Discovery and Graph Simulation: A Lightweight Alternative to Chaos Engineering
Microservice applications are prone to cascading failures because of dense inter-service dependencies. Ensuring resilience usually demands fault-injection experiments in production-like setups. We propose \textit{model discovery} -- an automated CI/CD step that extracts a live dependency graph from trace data -- and show that this lightweight representation is sufficient for accurate resilience prediction. Using the DeathStarBench Social Network, we build the graph, simulate failures via Monte-…

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation
Changxin Ke, Rui Zhang, Shuo Wang, Li Ding, Guangli Li, Yuanbo Wen, Shuoming Zhang, Ruiyuan Xu, Jin Qin, Jiaming Guo, Chenxi Wang, Ling Li, Qi Guo, Yunji Chen
https://arxiv.org/abs/2506.11153

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation
The rise of GPU-based high-performance computing (HPC) has driven the widespread adoption of parallel programming models such as CUDA. Yet, the inherent complexity of parallel programming creates a demand for the automated sequential-to-parallel approaches. However, data scarcity poses a significant challenge for machine learning-based sequential-to-parallel code translation. Although recent back-translation methods show promise, they still fail to ensure functional equivalence in the translate…

From over-reliance to smart integration: using Large-Language Models as translators between specialized modeling and simulation tools
Philippe J. Giabbanelli, John Beverley, Istvan David, Andreas Tolk
https://arxiv.org/abs/2506.11141

From over-reliance to smart integration: using Large-Language Models as translators between specialized modeling and simulation tools
Large Language Models (LLMs) offer transformative potential for Modeling & Simulation (M&S) through natural language interfaces that simplify workflows. However, over-reliance risks compromising quality due to ambiguities, logical shortcuts, and hallucinations. This paper advocates integrating LLMs as middleware or translators between specialized tools to mitigate complexity in M&S tasks. Acting as translators, LLMs can enhance interoperability across multi-formalism, multi-semantics, and multi…

Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor
Weibo Gao, Qi Liu, Rui Li, Yuze Zhao, Hao Wang, Linan Yre, Fangzhou Yao, Zheng Zhang
https://arxiv.org/abs/2506.11107

Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor
Programming Knowledge Tracking (PKT) aims to dynamically diagnose learners' mastery levels of programming knowledge based on their coding activities, facilitating more effective and personalized programming education. However, current PKT studies primarily focus on the implicit relationship between code content and knowledge assessment, often overlooking two types of noise signals in long-term programming activities: unwanted signals from unrelated submissions and weak signals from minor modifi…

LeanExplore: A search engine for Lean 4 declarations
Justin Asher (Independent Researcher)
https://arxiv.org/abs/2506.11085 https://a…

LeanExplore: A search engine for Lean 4 declarations
The expanding Lean 4 ecosystem poses challenges for navigating its vast libraries. This paper introduces LeanExplore, a search engine for Lean 4 declarations. LeanExplore enables users to semantically search for statements, both formally and informally, across select Lean 4 packages (including Batteries, Init, Lean, Mathlib, PhysLean, and Std). This search capability is powered by a hybrid ranking strategy, integrating scores from a multi-source semantic embedding model (capturing conceptual me…

Research and Analysis of Employers' Opinion on the Necessary Skills that Students in the Field of Web Programming Should Possess
Yordan Kalmukov
https://arxiv.org/abs/2506.11084

Research and Analysis of Employers' Opinion on the Necessary Skills that Students in the Field of Web Programming Should Possess
In the era of artificial intelligence (AI) and chatbots, based on large language models that can generate programming code in any language, write texts and summarize information, it is obvious that the requirements of employers for graduating students have already changed. The modern IT world offers significant automation of programming through software frameworks and a huge set of third-party libraries and application programming interfaces (APIs). All these tools provide most of the necessary…

DCE-LLM: Dead Code Elimination with Large Language Models
Minyu Chen, Guoqiang Li, Ling-I Wu, Ruibang Liu
https://arxiv.org/abs/2506.11076 https://

DCE-LLM: Dead Code Elimination with Large Language Models
Dead code introduces several challenges in software development, such as increased binary size and maintenance difficulties. It can also obscure logical errors and be exploited for obfuscation in malware. For LLM-based code-related tasks, dead code introduces vulnerabilities that can mislead these models, raising security concerns. Although modern compilers and IDEs offer dead code elimination, sophisticated patterns can bypass these tools. A universal approach that includes classification, loc…

CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval
Jiahui Geng, Fengyu Cai, Shaobo Cui, Qing Li, Liangwei Chen, Chenyang Lyu, Haonan Li, Derui Zhu, Walter Pretschner, Heinz Koeppl, Fakhri Karray
https://arxiv.org/abs/2506.11066

CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval
Code retrieval is essential in modern software development, as it boosts code reuse and accelerates debugging. However, current benchmarks primarily emphasize functional relevance while neglecting critical dimensions of software quality. Motivated by this gap, we introduce CoQuIR, the first large-scale, multilingual benchmark specifically designed to evaluate quality-aware code retrieval across four key dimensions: correctness, efficiency, security, and maintainability. CoQuIR provides fine-gra…

Code Researcher: Deep Research Agent for Large Systems Code and Commit History
Ramneet Singh, Sathvik Joel, Abhav Mehrotra, Nalin Wadhwa, Ramakrishna B Bairi, Aditya Kanade, Nagarajan Natarajan
https://arxiv.org/abs/2506.11060

Code Researcher: Deep Research Agent for Large Systems Code and Commit History
Large Language Model (LLM)-based coding agents have shown promising results on coding benchmarks, but their effectiveness on systems code remains underexplored. Due to the size and complexities of systems code, making changes to a systems codebase is a daunting task, even for humans. It requires researching about many pieces of context, derived from the large codebase and its massive commit history, before making changes. Inspired by the recent progress on deep research agents, we design the fi…

CodeMirage: A Multi-Lingual Benchmark for Detecting AI-Generated and Paraphrased Source Code from Production-Level LLMs
Hanxi Guo, Siyuan Cheng, Kaiyuan Zhang, Guangyu Shen, Xiangyu Zhang
https://arxiv.org/abs/2506.11059

CodeMirage: A Multi-Lingual Benchmark for Detecting AI-Generated and Paraphrased Source Code from Production-Level LLMs
Large language models (LLMs) have become integral to modern software development, producing vast amounts of AI-generated source code. While these models boost programming productivity, their misuse introduces critical risks, including code plagiarism, license violations, and the propagation of insecure programs. As a result, robust detection of AI-generated code is essential. To support the development of such detectors, a comprehensive benchmark that reflects real-world conditions is crucial. …

Refactoring Codebases through Library Design
Ziga Kovacic, Celine Lee, Justin Chiu, Wenting Zhao, Kevin Ellis
https://arxiv.org/abs/2506.11058 https://

Refactoring Codebases through Library Design
Maintainable and general software allows developers to build robust applications efficiently, yet achieving these qualities often requires refactoring specialized solutions into reusable components. This challenge becomes particularly relevant as code agents become increasingly accurate at solving isolated programming problems. We investigate code agents' capacity to refactor code in ways supporting growth and reusability. We present both a method and a benchmark for refactoring: Librarian, a s…

Software Security Mapping Framework: Operationalization of Security Requirements
Sung Une Lee, Liming Dong, Zhenchang Xing, Muhammad Ejaz Ahmed, Stefan Avgoustakis
https://arxiv.org/abs/2506.11051

Software Security Mapping Framework: Operationalization of Security Requirements
The escalating complexity of modern software development environments has heightened concerns around supply chain security. However, existing frameworks often fall short in translating abstract security principles into concrete, actionable practices. This paper introduces the Software Security Mapping Framework, a structured solution designed to operationalize security requirements across hierarchical levels -- from high-level regulatory standards (e.g., ISM, Australia cybersecurity standard pu…