Tootfinder

From ⁨⁨⁨⁨⁨#AnnafromUkraine⁩⁩⁩⁩⁩ @AnnafromUkraine@youtube.com
HUGE EXPLOSIONS: RADARS IN CRIMEA, CHEMICAL PLANT IN BALASHIKHA Vlog 1153: War in Ukraine
On August 30, #Ukraine destroyed russian #FSB

Unsere Berichte zu den Normenturnieren und zur DLM findet ihr nachfolgend:
DLM: https://www.deutsche-schachjugend.de/news/2025/deutsche-laendermeisterschaft-tag-2-nrw-setzt-sich-ab/
Normen:

Von #direkt #direktMagazin
Eigenmietwert-Vorlage: Bürgerliche Politiker warnen vor Steuererhöhungen.
Am 28. September stimmen wir über den #Systemwechsel bei der

Eigenmietwert-Vorlage: Bürgerlichen Politiker warnen vor Steuererhöhungen
Am 28. September stimmen wir über den Systemwechsel bei der Wohneigentumsbesteuerung und die Abschaffung des Eigenmietwerts ab.

Viele Ergebnisse in der DLM:
NRW 1 - Baden 0,5 zu 1,5
Bayern - RLP 2 zu 3
SHO - Hessen 1 zu 2
Schaakbond - S-A 1,5 zu 1,5
NDS - NRW 2 2 zu 1
WÜR - SAC 3 zu 0
THÜ - BER 2 zu 2
HH 2 - HH 1 2,5 zu 3,5
SAA - MVP 3 zu 2
Und jetzt das Wetter
Bei sonnigen 14 Grad Celsius steht die Luft in den Spielsälen meterhoch. Wir empfehlen das regelmäßige Aufsuchen der Frischluftbereiche.

You are now a worker! (SimAnt, happy Labor Day) Via https://archive.org/details/XboxNation11Jan2004/page/n27/mode/2up?view=theater

Amped Up Local
Great Australian Pods Podcast Directory: #GreatAusPods

Die Gemengelage an der Elbe wird undurchsichtiger. Mittlerweile steht es zwischen HH 2 und HH 1 2 zu 3. Ob hier eine Hamburger Mannschaft gewinnen mag? #dlm25

If they were after the causes of whistleblowing, they certainly were on the right track:
“The NSA has some of the brightest minds in the country,” I began. “They’re honestly there to make a difference. Yet there’s no outlet for people who no longer feel like they’re on the right side. You’re requesting blind loyalty from some of the most nuanced and analytical individuals—individuals, not soldiers—to process your intelligence and make predictions. Furthermore, in 2017, and likely until…

'I Am Not Your Enemy': The Reality Winner Story
Book excerpt: “The world’s biggest terrorist has a Pikachu bedspread.”

#HulkHogan and Hot Onlookers
Season 6 Episode 15 "Bash at the Beach"
#RandomBaywatch #lvdlpx

openflights: Openflights airport network
A network of regularly occurring flights among airports worldwide, extracted from the openflights.org dataset. Nodes represent airports, and direction of edge (i,j) indicates a regularly occurring commercial flight by a particular airline from airport i to airport j. Multiple edges may exist between a pair of airports if multiple airlines offer that flight, or if one airline offers multiple such flights each day.
This network has 3214 node…

Series C, Episode 10 - Ultraworld
AVON: [Moves next to Tarrant at Main pilot's console] Anything on the star charts?
TARRANT: Not within this quadrant. Nothing listed, not even a Federation beacon.
AVON: It couldn't be that?
TARRANT: Why not?
https://blake.torpidity.net/m/310/6

New route yesterday morning, felt fast and was! Good but shorter sleep; decided to throw on the headlamp and reflective gear before sunrise. I think this was the first or second time out in the dark since I was hit by a car. A little less traffic on this one, and it avoids the intersection of previous impact.
iPhone seems to brighten things up; it was not that light out yet, lol.
Normal lunch run today on the east side at work. Some pretty flowers in the park (New England Asters…

Heartbroken, like countless across the world, to have woken up to the news of the passing of Jane Goodall. Her fierce passion and audacious hope in connection and communication across boundaries - human, animal, and otherwise - as the lifeforce is a lesson for us all.
I had the privilege of hearing her speak. Filled with incredible wit and compassion, she began her address with the call of a chimpanzee and by thanking her mother. 🤍
In humility and hope.

Now that the first draft of my PyCon UK keynote is done and my nerves are settling down a bit, why don’t I check on the issues and PRs that accumulated on my FOSS projects…

Meme-Maschine Colbow hat wieder zugeschlagen. Was war zuerst da? Das Ei oder Collin Colbow?
#dlm25

need to find a way to put #XFCE Terminal tabs at the bottom. it's a terminal: I spend all my pointer focus at the bottom and the tabs on the ceiling are a long way for a thumb on a touchpad 😅

Merz will über russisches Vermögen im Oktober entscheiden
Bundeskanzler Friedrich Merz strebt eine Entscheidung über die Nutzung des in der EU eingefrorenen Vermögens für die Ukraine-Hilfe noch im Oktober an. "Wir werden das jetzt sorgfältig prüfen und es wird in drei Wochen auf dem nächsten Europäischen Rat, aller Voraussicht nach, dazu eine konkrete Entscheidung geben", sagte der CDU-Vorsitzende n…
🔗

Merz: Eingefrorene russische Milliarden für Ukraine-Kredit nutzen
"Neuen Impuls setzen": Kanzler Merz will, dass die EU der Ukraine einen 140-Milliarden-Euro-Kredit zur Verfügung stellt. Aber nicht aus den eigenen Etats - das Geld soll aus eingefrorenen russischen Vermögen kommen.

https://social.vivaldi.net/@mechanical0815@tyrol.social/115270921336695910
Jürgen :verigreen:🌗🪐🌌 🇦🇹
@…

The decline of civilization continues.
Sperm racing is all the rage among the tech bros.
https://www.theguardian.com/commentisfree/2025/sep/30/sperm-racing-is-all-the-rage-among-the-tech-bros-why-am-i-not-surpr…

Nach Siegen von Lennart Tabales, Marcel Jung und Meara Classen führen Saarland & Friends 3 zu 0über Mecklenburg-Vorpommern.
#dlm25

A snail works daily
Weighing the world on its back
Thus labours Atlas.
#dailyhaikuprompt - snail
#haiku
#poem

Go with Your Gut: Scaling Confidence for Autoregressive Image Generation
Harold Haodong Chen, Xianfeng Wu, Wen-Jie Shu, Rongjin Guo, Disen Lan, Harry Yang, Ying-Cong Chen
https://arxiv.org/abs/2509.26376

Go with Your Gut: Scaling Confidence for Autoregressive Image Generation
Test-time scaling (TTS) has demonstrated remarkable success in enhancing large language models, yet its application to next-token prediction (NTP) autoregressive (AR) image generation remains largely uncharted. Existing TTS approaches for visual AR (VAR), which rely on frequent partial decoding and external reward models, are ill-suited for NTP-based image generation due to the inherent incompleteness of intermediate decoding results. To bridge this gap, we introduce ScalingAR, the first TTS fr…

Der Erholungszeitraum zwischen den Runden im GM-Turnier schien nach dem 10-zügigen Remis der vorherigen Runde nicht groß genug. So folgte zwischen Toms Kantans und Nikita Meshkovs ein weiteres schnelles Remis.
#dlm25

From ⁨⁨⁨⁨⁨#AnnafromUkraine⁩⁩⁩⁩⁩ @AnnafromUkraine@youtube.com
#ROSNEFT COLLAPSE: UKRAINE BURNED BIG REFINERIES IN KRASNODAR & SAMARA Vlog 1152: War in #Ukraine
Ukrainian

@… muy buenas, espero que no te moleste comentarte esto en público, porque así quizšs podamos ayudar a mšs gente.
Estoy con la duda de "adaptar" (por decirlo de alguna manera) una BTT que tengo el pueblo para usarla para moverme por ciudad; bšsicamente la adpatación sería cmabiar neumšticos y ponerle luces y guardabarros.
El tema es que va a …

aktuelles #Wetter #Behrendorf #Wische 02-11-2025 23:50
Temperatur: (min: 6,7C) 6,9C (max: 13,4C)
Feuchte: 99%
Luftdruck: 1011,0 hPa (gleichbleibend)
Windrichtung: N/A Grad
W…

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Are the Majority of Public Computational Notebooks Pathologically Non-Executable?
Tien Nguyen, Waris Gill, Muhammad Ali Gulzar

Die Blicke in der DLM sind erichtet auf die Spitzenpaarungen:
NRW 1 vs. Baden
Bayern vs. Rheinland-Pfalz
Alle vier Landesverbände teilen sich bisher die weiße Weste.
Zur landesinternen Paarung kommt es für die Hamburger:innen. Da trift die Erste auf die Zweite. Allzu friedliche Ergebnisse würden wir nicht erwarten.
#dlm25

sp_hospital: Hospital ward dynamic contacts (2010)
This dataset contains the temporal network of contacts between patients, patients and health-care workers (HCWs) and among HCWs in a hospital ward in Lyon, France, from Monday, December 6, 2010 at 1:00 pm to Friday, December 10, 2010 at 2:00 pm. The study included 46 HCWs and 29 patients.
This network has 75 nodes and 32424 edges.
Tags: Social, Offline, Unweighted, Temporal, Metadata

Honorieren möchten wir heute morgen die hervorragende Öffentlichkeitsarbeit der Schachjugend Niedersachsen. Der Bericht zur gestrigen Runde aus Sicht von NDS:
https://www.nsj-online.de/wordpress/dlm-2025-starker-erster-tag-vom-teamnsj/

Will Smith's concert crowds are real, but AI is blurring the lines - Waxy.org
https://waxy.org/2025/08/will-smiths-concert-crowds-were-real-but-ai-is-blurring-the-lines/

Der Bericht zu den ersten beiden Runden der DLM ist online und über diesen Link abrufbar: https://www.deutsche-schachjugend.de/news/2025/deutsche-laendermeisterschaft-tag-1/
Der Bericht zur zweiten und dritten Runde der Normenturniere ist hier a…

Israeli naval forces board pro-Palestinian flotilla 75 miles from Gaza
About 20 Israeli naval ships approached the fleet of more than 40 boats carrying aid, activists on board say
https://www.theguardian.com/world/2025/oct/01/israeli-m…

From ⁨⁨⁨⁨⁨#AnnafromUkraine⁩⁩⁩⁩⁩ @AnnafromUkraine@youtube.com
RECESSION STARTS IN #RUSSIA: KREMLIN CAN NOT HIDE Vlog 1151: War in #Ukraine
The

"What’s clear is that when we share music — whether at backyard barbecues, holiday gatherings, or road trips with the radio blaring — we’re doing something deeply meaningful. Our brains are literally synchronizing, creating shared neural patterns across generations and differences. In those moments when everyone finds themselves humming the same chorus or tapping to the same beat, we’re experiencing one of life’s simplest yet most profound connections."
When Your Favorite Song Plays, Your Brain 'Physically Embodies' Music
https://studyfinds.org/brain-cells-synchronize-to-music/

Crosslisted article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Addressing Reproducibility Challenges in HPC with Continuous Integration
Val\'erie Hayot-Sasson, Nathaniel Hudson, Andr\'e Bauer, Maxime Gonthier, Ian Foster, Kyle Chard

They learned from the Grifter-in-Chief.
Already Pardoned by Trump, Jan. 6 Rioters Push for Compensation
https://www.nytimes.com/2025/08/31/us/politics/trump-jan-6-rioters-compensation.html

Already Pardoned by Trump, Jan. 6 Rioters Push for Compensation
One of the rioters’ lawyers wants to create a panel that would decide on financial damages for what the rioters believe were unfair prosecutions.

A lot of 70s era National Lampoon and SNL did not age well, but other bits became timeless classics that just won't grow old.
Sadly, this clip is one of them.
https://youtube.com/watch?v=j6W65oo533o&si=2syHm4uXKd-b13F-

ICE obtains access to Israeli-made spyware that can hack phones and encrypted apps
https://www.theguardian.com/us-news/2025/sep/02/trump-immigration-ice-israeli-spyware

The Integration of Agile Methodologies in DevOps Practices within the Information Technology Industry
Ashley Hourigan, Ridewaan Hanslo
https://arxiv.org/abs/2508.21811 https://

The Integration of Agile Methodologies in DevOps Practices within the Information Technology Industry
The demand for rapid software delivery in the Information Technology (IT) industry has significantly intensified, emphasising the need for faster software products and service releases with enhanced features to meet customer expectations. Agile methodologies are replacing traditional approaches such as Waterfall, where flexibility, iterative development and adaptation to change are favoured over rigid planning and execution. DevOps, a subsequent evolution from Agile, emphasises collaborative ef…

From ⁨⁨⁨⁨⁨#AnnafromUkraine⁩⁩⁩⁩⁩ @AnnafromUkraine@youtube.com
SOUTH RUSSIA WITHOUT FUEL, MASSIVE ATTACK ON #KYIV Vlog 1150: War in #Ukraine
The south of

Happy Petrov Day to those who celebrate

Why bother? He'll just want to rip them up so that he can add fake gold in their place.
https://www.theguardian.com/commentisfree/2025/sep/01/soldiers-landscaping-washington-dc-crime

Yes, The News is reaching new heights of bad, but I dont think keywords are really the cause of my suffering.
So I ask myself why they irk me and it generally comes back that I want control over others, which, as tyrants inevitably find out, isn't really possible. Hard enough controlling myself!
So, given what feeble control I have, I ask what needs doing, here, now, and find I need to know what's happening around me, here, now, to answer 😅
My strategy for news is roughly
- compare multiple sources eg AP, Reuters etc
- ignore what anyone says
- ignore claims what they will do or have done
- ignore predictions and interpretations
- be skeptical of causal inferences
- attend to what was actually done
The first and last when combined are often amusing in which actual observations are included or left out. Humans are awfully clever, and so cunning in their rhetoric 🤣

Human-Written vs. AI-Generated Code: A Large-Scale Study of Defects, Vulnerabilities, and Complexity
Domenico Cotroneo, Cristina Improta, Pietro Liguori
https://arxiv.org/abs/2508.21634

Human-Written vs. AI-Generated Code: A Large-Scale Study of Defects, Vulnerabilities, and Complexity
As AI code assistants become increasingly integrated into software development workflows, understanding how their code compares to human-written programs is critical for ensuring reliability, maintainability, and security. In this paper, we present a large-scale comparison of code authored by human developers and three state-of-the-art LLMs, i.e., ChatGPT, DeepSeek-Coder, and Qwen-Coder, on multiple dimensions of software quality: code defects, security vulnerabilities, and structural complexit…

TL;RL: He is.
#dictatorship

A clever statement on the sorry state of the #AI arts via meme-able mesmergorical AI art?
#infiniteloops #kitchomatic
https://www.tumblr.com/teledyn/790711569148411904/surrealist-fan-service?source=share

From ⁨⁨⁨⁨⁨#AnnafromUkraine⁩⁩⁩⁩⁩ @AnnafromUkraine@youtube.com
RYAZAN-MOSCOW OIL PIPELINE HUGE EXPLOSION & #VORONEZH #RADARS DESTROYED Vlog 1149: War in

Corporation for Public Broadcasting to close after US funding cut
Closure of public broadcasting group follows House Republicans’ decision to strip $1.1bn in funding over two years
https://www.theguardian.com/us-news/2025/aug/01/corporation-for…

Reusable Test Suites for Reinforcement Learning
J{\o}rn Eirik Betten, Quentin Mazouni, Dennis Gross, Pedro Lind, Helge Spieker
https://arxiv.org/abs/2508.21553 https://

Reusable Test Suites for Reinforcement Learning
Reinforcement learning (RL) agents show great promise in solving sequential decision-making tasks. However, validating the reliability and performance of the agent policies' behavior for deployment remains challenging. Most reinforcement learning policy testing methods produce test suites tailored to the agent policy being tested, and their relevance to other policies is unclear. This work presents Multi-Policy Test Case Selection (MPTCS), a novel automated test suite selection method for RL en…

Enhancing Semantic Understanding in Pointer Analysis using Large Language Models
Baijun Cheng, Kailong Wang, Ling Shi, Haoyu Wang, Yao Guo, Ding Li, Xiangqun Chen
https://arxiv.org/abs/2508.21454

Enhancing Semantic Understanding in Pointer Analysis using Large Language Models
Pointer analysis has been studied for over four decades. However, existing frameworks continue to suffer from the propagation of incorrect facts. A major limitation stems from their insufficient semantic understanding of code, resulting in overly conservative treatment of user-defined functions. Recent advances in large language models (LLMs) present new opportunities to bridge this gap. In this paper, we propose LMPA (LLM-enhanced Pointer Analysis), a vision that integrates LLMs into pointer a…

The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Management
Tobias Lindenbauer, Igor Slinko, Ludwig Felder, Egor Bogomolov, Yaroslav Zharov
https://arxiv.org/abs/2508.21433

The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Management
Large Language Model (LLM)-based agents solve complex tasks through iterative reasoning, exploration, and tool-use, a process that can result in long, expensive context histories. While state-of-the-art Software Engineering ( SE) agents like OpenHands or Cursor use LLM-based summarization to tackle this issue, it is unclear whether the increased complexity offers tangible performance benefits compared to simply omitting older observations. We present a systematic comparison of these strategies …

Automated Bug Triaging using Instruction-Tuned Large Language Models
Kiana Kiashemshaki, Arsham Khosravani, Alireza Hosseinpour, Arshia Akhavan
https://arxiv.org/abs/2508.21156 …

Automated Bug Triaging using Instruction-Tuned Large Language Models
Bug triaging, the task of assigning new issues to developers, is often slow and inconsistent in large projects. We present a lightweight framework that instruction-tuned large language model (LLM) with LoRA adapters and uses candidate-constrained decoding to ensure valid assignments. Tested on EclipseJDT and Mozilla datasets, the model achieves strong shortlist quality (Hit at 10 up to 0.753) despite modest exact Top-1 accuracy. On recent snapshots, accuracy rises sharply, showing the framework…

Learning to Generate Unit Test via Adversarial Reinforcement Learning
Dongjun Lee, Changho Hwang, Kimin Lee
https://arxiv.org/abs/2508.21107 https://arxiv.…

Learning to Generate Unit Test via Adversarial Reinforcement Learning
Unit testing is a core practice in programming, enabling systematic evaluation of programs produced by human developers or large language models (LLMs). Given the challenges in writing comprehensive unit tests, LLMs have been employed to automate test generation, yet methods for training LLMs to produce high-quality tests remain underexplored. In this work, we propose UTRL, a novel reinforcement learning framework that trains an LLM to generate high-quality unit tests given a programming instru…

Model-Driven Quantum Code Generation Using Large Language Models and Retrieval-Augmented Generation
Nazanin Siavash, Armin Moin
https://arxiv.org/abs/2508.21097 https://

Model-Driven Quantum Code Generation Using Large Language Models and Retrieval-Augmented Generation
This paper introduces a novel research direction for model-to-text/code transformations by leveraging Large Language Models (LLMs) that can be enhanced with Retrieval-Augmented Generation (RAG) pipelines. The focus is on quantum and hybrid quantum-classical software systems, where model-driven approaches can help reduce the costs and mitigate the risks associated with the heterogeneous platform landscape and lack of developers' skills. We validate one of the proposed ideas regarding generating …

[2025-09-01 Mon (UTC), 8 new articles found for cs.SE Software Engineering]
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[2025-09-02 Tue (UTC), no new articles found for cs.SE Software Engineering]
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CircInspect: Integrating Visual Circuit Analysis, Abstraction, and Real-Time Development in Quantum Debugging
Mushahid Khan, Prashant J. Nair, Olivia Di Matteo
https://arxiv.org/abs/2509.25199

CircInspect: Integrating Visual Circuit Analysis, Abstraction, and Real-Time Development in Quantum Debugging
Software bugs typically result from errors in specifications or code translation. While classical software engineering has evolved with various tools and methodologies to tackle such bugs, the emergence of quantum computing presents unique challenges. Quantum software development introduces complexities due to the probabilistic nature of quantum computing, distinct algorithmic primitives, and potential hardware noise. In this paper, we introduce CircInspect, an interactive tool tailored for deb…

Towards Repository-Level Program Verification with Large Language Models
Si Cheng Zhong, Xujie Si
https://arxiv.org/abs/2509.25197 https://arxiv.org/pdf/25…

Towards Repository-Level Program Verification with Large Language Models
Recent advancements in large language models (LLMs) suggest great promises in code and proof generations. However, scaling automated formal verification to real-world projects requires resolving cross-module dependencies and global contexts, which are crucial challenges overlooked by existing LLM-based methods with a special focus on targeting isolated, function-level verification tasks. To systematically explore and address the significant challenges of verifying entire software repositories, …

APRIL: API Synthesis with Automatic Prompt Optimization and Reinforcement Learning
Hua Zhong, Shan Jiang, Sarfraz Khurshid
https://arxiv.org/abs/2509.25196 https://

APRIL: API Synthesis with Automatic Prompt Optimization and Reinforcement Learning
APIs are central to modern software development, yet composing new APIs from large libraries is difficult due to the exponential search space; traditional component-based synthesis relies on costly exploration and hand-crafted specifications. While large language models (LLMs) can generate implementations from natural language, hallucinations and limited access to up-to-date contextual information often yield incorrect code. In this paper, we present APRIL, an approach that combines LLM-based s…

Understanding Practitioners Perspectives on Monitoring Machine Learning Systems
Hira Naveed, John Grundy, Chetan Arora, Hourieh Khalajzadeh, Omar Haggag
https://arxiv.org/abs/2509.25195

Understanding Practitioners Perspectives on Monitoring Machine Learning Systems
Given the inherent non-deterministic nature of machine learning (ML) systems, their behavior in production environments can lead to unforeseen and potentially dangerous outcomes. For a timely detection of unwanted behavior and to prevent organizations from financial and reputational damage, monitoring these systems is essential. This paper explores the strategies, challenges, and improvement opportunities for monitoring ML systems from the practitioners perspective. We conducted a global survey…

Automated Code Development for PDE Solvers Using Large Language Models
Haoyang Wu, Xinxin Zhang, Lailai Zhu
https://arxiv.org/abs/2509.25194 https://arxiv.…

Automated Code Development for PDE Solvers Using Large Language Models
Foundation models -- large language models (LLMs) in particular -- have become ubiquitous, shaping daily life and driving breakthroughs across science, engineering, and technology. Harnessing their broad cross-domain knowledge, text-processing, and reasoning abilities for software development, e.g., numerical libraries for solving partial differential equations (PDEs), is therefore attracting growing interest. Yet existing studies mainly automate case setup and execution for end users. We intro…

Devstral: Fine-tuning Language Models for Coding Agent Applications
Abhinav Rastogi, Adam Yang, Albert Q. Jiang, Alexander H. Liu, Alexandre Sablayrolles, Am\'elie H\'eliou, Am\'elie Martin, Anmol Agarwal, Andy Ehrenberg, Andy Lo, Antoine Roux, Arthur Darcet, Arthur Mensch, Baptiste Bout, Baptiste Rozi\`ere, Baudouin De Monicault, Chris Bamford, Christian Wallenwein, Christophe Renaudin, Cl\'emence Lanfranchi, Cl\'ement Denoix, Corentin Barreau, Darius Dabert Devon …

Devstral: Fine-tuning Language Models for Coding Agent Applications
We introduce Devstral-Small, a lightweight open source model for code agents with the best performance among models below 100B size. In this technical report, we give an overview of how we design and develop a model and craft specializations in agentic software development. The resulting model, Devstral-Small is a small 24B model, fast and easy to serve. Despite its size, Devstral-Small still attains competitive performance compared to models more than an order of magnitude larger.

WARP -- Web-Augmented Real-time Program Repairer: A Real-Time Compilation Error Resolution using LLMs and Web-Augmented Synthesis
Anderson de Lima Luiz
https://arxiv.org/abs/2509.25192

WARP -- Web-Augmented Real-time Program Repairer: A Real-Time Compilation Error Resolution using LLMs and Web-Augmented Synthesis
Compilation errors represent a significant bottleneck in software development productivity. This paper introduces WARP (Web-Augmented Real-time Program Repairer), a novel system that leverages Large Language Models (LLMs) and dynamic web-augmented synthesis for real-time resolution of these errors. WARP actively monitors developer terminals, intelligently detects compilation errors, and synergistically combines the understanding of a fine-tuned Code-LLM with relevant solutions, explanations, an…

[2025-10-01 Wed (UTC), 38 new articles found for cs.SE Software Engineering]
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Crosslisted article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Memory Management and Contextual Consistency for Long-Running Low-Code Agents
Jiexi Xu

Black-box Context-free Grammar Inference for Readable & Natural Grammars
Mohammad Rifat Arefin, Shanto Rahman, Christoph Csallner
https://arxiv.org/abs/2509.26616 https://…

Black-box Context-free Grammar Inference for Readable & Natural Grammars
Black-box context-free grammar inference is crucial for program analysis, reverse engineering, and security, yet existing tools such as Arvada, TreeVada, and Kedavra struggle with scalability, readability, and accuracy on large, complex languages. We present NatGI, a novel LLM-guided grammar inference framework that extends TreeVada's parse tree recovery with three key innovations: bracket-guided bubble exploration, LLM- driven bubble generation and non-terminal labeling, and hierarchical delta…

Towards Verified Code Reasoning by LLMs
Meghana Sistla, Gogul Balakrishnan, Pat Rondon, Jos\'e Cambronero, Michele Tufano, Satish Chandra
https://arxiv.org/abs/2509.26546 ht…

Towards Verified Code Reasoning by LLMs
While LLM-based agents are able to tackle a wide variety of code reasoning questions, the answers are not always correct. This prevents the agent from being useful in situations where high precision is desired: (1) helping a software engineer understand a new code base, (2) helping a software engineer during code review sessions, and (3) ensuring that the code generated by an automated code generation system meets certain requirements (e.g. fixes a bug, improves readability, implements a featur…

ErrorPrism: Reconstructing Error Propagation Paths in Cloud Service Systems
Junsong Pu, Yichen Li, Zhuangbin Chen, Jinyang Liu, Zhihan Jiang, Jianjun Chen, Rui Shi, Zibin Zheng, Tieying Zhang
https://arxiv.org/abs/2509.26463

ErrorPrism: Reconstructing Error Propagation Paths in Cloud Service Systems
Reliability management in cloud service systems is challenging due to the cascading effect of failures. Error wrapping, a practice prevalent in modern microservice development, enriches errors with context at each layer of the function call stack, constructing an error chain that describes a failure from its technical origin to its business impact. However, this also presents a significant traceability problem when recovering the complete error propagation path from the final log message back t…

EQ-Robin: Generating Multiple Minimal Unique-Cause MC/DC Test Suites
Robin Lee, Youngho Nam
https://arxiv.org/abs/2509.26458 https://arxiv.org/pdf/2509.264…

EQ-Robin: Generating Multiple Minimal Unique-Cause MC/DC Test Suites
Modified Condition/Decision Coverage (MC/DC), particularly its strict Unique-Cause form, is a cornerstone of safety-critical software verification. A recent algorithm, "Robin's Rule," introduced a deterministic method to construct the theoretical minimum of N+1 test cases for Singular Boolean Expressions (SBEs). However, this approach yields only a single test suite, introducing a critical risk: if a test case forming a required 'independence pair' is an illegal input forbidden by system constr…

Institutional Policy Pathways for Supporting Research Software: Global Trends and Local Practices
Michelle Barker, Jeremy Cohen, Pedro Hern\'andez Serrano, Daniel S. Katz, Kim Martin, Dan Rudmann, Hugh Shanahan
https://arxiv.org/abs/2509.26422

Institutional Policy Pathways for Supporting Research Software: Global Trends and Local Practices
As research software becomes increasingly central to modern science, research-performing organisations (RPOs) need to ensure that their investment in people, skills and infrastructure around research software produces sustainable and maintainable software that improves the research they perform, which in turn improves the overall institution and its reputation and funding, for example, by competing with peers who lack this approach. However, research institution management and recognition of re…

UniSage: A Unified and Post-Analysis-Aware Sampling for Microservices
Zhouruixing Zhu, Zhihan Jiang, Tianyi Yang, Pinjia He
https://arxiv.org/abs/2509.26336 https://

UniSage: A Unified and Post-Analysis-Aware Sampling for Microservices
Traces and logs are essential for observability and fault diagnosis in modern distributed systems. However, their ever-growing volume introduces substantial storage overhead and complicates troubleshooting. Existing approaches typically adopt a sample-before-analysis paradigm: even when guided by data heuristics, they inevitably discard failure-related information and hinder transparency in diagnosing system behavior. To address this, we introduce UniSage, the first unified framework to sample …

Hamster: A Large-Scale Study and Characterization of Developer-Written Tests
Rangeet Pan, Tyler Stennett, Raju Pavuluri, Nate Levin, Alessandro Orso, Saurabh Sinha
https://arxiv.org/abs/2509.26204

Hamster: A Large-Scale Study and Characterization of Developer-Written Tests
Automated test generation (ATG), which aims to reduce the cost of manual test suite development, has been investigated for decades and has produced countless techniques based on a variety of approaches: symbolic analysis, search-based, random and adaptive-random, learning-based, and, most recently, large-language-model-based approaches. However, despite this large body of research, there is still a gap in our understanding of the characteristics of developer-written tests and, consequently, in …

Understanding Collective Social Behavior in OSS Communities: A Co-editing Network Analysis of Activity Cascades
Lisi Qarkaxhija, Maximilian Carparo, Stefan Menzel, Bernhard Sendhoff, Ingo Scholtes
https://arxiv.org/abs/2509.26173

Understanding Collective Social Behavior in OSS Communities: A Co-editing Network Analysis of Activity Cascades
Understanding the collective social behavior of software developers is crucial to model and predict the long-term dynamics and sustainability of Open Source Software (OSS) communities. To this end, we analyze temporal activity patterns of developers, revealing an inherently ``bursty'' nature of commit contributions. To investigate the social mechanisms behind this phenomenon, we adopt a network-based modelling framework that captures developer interactions through co-editing networks. Our frame…

A Multi-Language Object-Oriented Programming Benchmark for Large Language Models
Shuai Wang, Liang Ding, Li Shen, Yong Luo, Han Hu, Lefei Zhang, Fu Lin
https://arxiv.org/abs/2509.26111

A Multi-Language Object-Oriented Programming Benchmark for Large Language Models
Establishing fair and robust benchmarks is essential for evaluating intelligent code generation by large language models (LLMs). Our survey of 35 existing benchmarks uncovers three major imbalances: 85.7% focus on a single programming language; 94.3% target only function-level or statement-level tasks; and over 80% include fewer than ten test cases on average. To address these gaps, we propose MultiOOP, a multi-language object-oriented programming benchmark covering six popular languages (Pytho…

Agent-based code generation for the Gammapy framework
Dmitriy Kostunin, Vladimir Sotnikov, Sergo Golovachev, Abhay Mehta, Tim Lukas Holch, Elisa Jones
https://arxiv.org/abs/2509.26110

Agent-based code generation for the Gammapy framework
Software code generation using Large Language Models (LLMs) is one of the most successful applications of modern artificial intelligence. Foundational models are very effective for popular frameworks that benefit from documentation, examples, and strong community support. In contrast, specialized scientific libraries often lack these resources and may expose unstable APIs under active development, making it difficult for models trained on limited or outdated data. We address these issues for th…

Evaluating the impact of code smell refactoring on the energy consumption of Android applications
Hina Anwar, Dietmar Pfahl, Satish N. Srirama
https://arxiv.org/abs/2509.26031 h…

Evaluating the impact of code smell refactoring on the energy consumption of Android applications
Energy consumption of mobile apps is a domain that is receiving a lot of attention from researchers. Recent studies indicate that the energy consumption of mobile devices could be improved by improving the quality of mobile apps. Frequent refactoring is one way of achieving this goal. In this paper, we explore the performance and energy impact of several common code refactorings in Android apps. Experimental results indicate that some code smell refactorings positively impact the energy consump…

Using GPT to build a Project Management assistant for Jira environments
Joel Garcia-Escribano, Arkaitz Carbajo, Mikel Ega\~na Aranguren, Unai Lopez-Novoa
https://arxiv.org/abs/2509.26014

Using GPT to build a Project Management assistant for Jira environments
In the domain of Project Management, the sheer volume of data is a challenge that project managers continually have to deal with. Effectively steering projects from inception to completion requires handling of diverse information streams, including timelines, budgetary considerations, and task dependencies. To navigate this data-driven landscape with precision and agility, project managers must rely on efficient and sophisticated tools. These tools have become essential, as they enable project …

R-Log: Incentivizing Log Analysis Capability in LLMs via Reasoning-based Reinforcement Learning
Yilun Liu, Ziang Chen, Song Xu, Minggui He, Shimin Tao, Weibin Meng, Yuming Xie, Tao Han, Chunguang Zhao, Jingzhou Du, Daimeng Wei, Shenglin Zhang, Yongqian Sun
https://arxiv.org/abs/2509.25987

R-Log: Incentivizing Log Analysis Capability in LLMs via Reasoning-based Reinforcement Learning
The growing complexity of log data in modern software systems has prompted the use of Large Language Models (LLMs) for automated log analysis. Current approaches typically rely on direct supervised fine-tuning (SFT) on log-label pairs. However, this exacerbates the domain discrepancy between general-purpose LLMs and specialized log data, causing overfitting. Furthermore, SFT's imbalanced loss computation often allows lengthy contexts to overwhelm critical, concise details in model answers, lead…

Red Teaming Program Repair Agents: When Correct Patches can Hide Vulnerabilities
Simin Chen, Yixin He, Suman Jana, Baishakhi Ray
https://arxiv.org/abs/2509.25894 https://…

Red Teaming Program Repair Agents: When Correct Patches can Hide Vulnerabilities
LLM-based agents are increasingly deployed for software maintenance tasks such as automated program repair (APR). APR agents automatically fetch GitHub issues and use backend LLMs to generate patches that fix the reported bugs. However, existing work primarily focuses on the functional correctness of APR-generated patches, whether they pass hidden or regression tests, while largely ignoring potential security risks. Given the openness of platforms like GitHub, where any user can raise issues an…

LogPilot: Intent-aware and Scalable Alert Diagnosis for Large-scale Online Service Systems
Zhihan Jiang, Jinyang Liu, Yichen Li, Haiyu Huang, Xiao He, Tieying Zhang, Jianjun Chen, Yi Li, Rui Shi, Michael R. Lyu
https://arxiv.org/abs/2509.25874

LogPilot: Intent-aware and Scalable Alert Diagnosis for Large-scale Online Service Systems
Effective alert diagnosis is essential for ensuring the reliability of large-scale online service systems. However, on-call engineers are often burdened with manually inspecting massive volumes of logs to identify root causes. While various automated tools have been proposed, they struggle in practice due to alert-agnostic log scoping and the inability to organize complex data effectively for reasoning. To overcome these limitations, we introduce LogPilot, an intent-aware and scalable framework…

Are Classical Clone Detectors Good Enough For the AI Era?
Ajmain Inqiad Alam, Palash Roy, Farouq Al-omari, Chanchal Roy, Banani Roy, Kevin Schneider
https://arxiv.org/abs/2509.25754

Are Classical Clone Detectors Good Enough For the AI Era?
The increasing adoption of AI-generated code has reshaped modern software development, introducing syntactic and semantic variations in cloned code. Unlike traditional human-written clones, AI-generated clones exhibit systematic syntactic patterns and semantic differences learned from large-scale training data. This shift presents new challenges for classical code clone detection (CCD) tools, which have historically been validated primarily on human-authored codebases and optimized to detect sy…

DeepCodeSeek: Real-Time API Retrieval for Context-Aware Code Generation
Esakkivel Esakkiraja, Denis Akhiyarov, Aditya Shanmugham, Chitra Ganapathy
https://arxiv.org/abs/2509.25716

DeepCodeSeek: Real-Time API Retrieval for Context-Aware Code Generation
Current search techniques are limited to standard RAG query-document applications. In this paper, we propose a novel technique to expand the code and index for predicting the required APIs, directly enabling high-quality, end-to-end code generation for auto-completion and agentic AI applications. We address the problem of API leaks in current code-to-code benchmark datasets by introducing a new dataset built from real-world ServiceNow Script Includes that capture the challenge of unclear API us…

Explainable Fault Localization for Programming Assignments via LLM-Guided Annotation
Fang Liu, Tianze Wang, Li Zhang, Zheyu Yang, Jing Jiang, Zian Sun
https://arxiv.org/abs/2509.25676

Explainable Fault Localization for Programming Assignments via LLM-Guided Annotation
Providing timely and personalized guidance for students' programming assignments, offers significant practical value for helping students complete assignments and enhance their learning. In recent years, various automated Fault Localization (FL) techniques have demonstrated promising results in identifying errors in programs. However, existing FL techniques face challenges when applied to educational contexts. Most approaches operate at the method level without explanatory feedback, resulting i…

M&SCheck: Towards a Checklist to Support Software Engineering Newcomers to the Modeling and Simulation Area
Luiza Martins de Freitas Cintra, Philipp Zech, Mohamad Kassab, Eliomar Ara\'ujo Lima, Sofia Larissa da Costa Paiva, Valdemar Vicente Graciano Neto
https://arxiv.org/abs/2509.25625

M&SCheck: Towards a Checklist to Support Software Engineering Newcomers to the Modeling and Simulation Area
The advent of increasingly complex and dynamic ecosystems, such as digital twins (DT), smart cities and Industry 4.0 and 5.0, has made evident the need to include modeling and simulation (M&S) in the software development life cycle. Such disruptive systems include simulation models in their own architecture (such as DT) or require the use of simulation models to represent the high degree of movement and the multiplicity of interactions that occur between the involved systems. However, when soft…

AGNOMIN - Architecture Agnostic Multi-Label Function Name Prediction
Yonatan Gizachew Achamyeleh, Tongtao Zhang, Joshua Hyunki Kim, Gabriel Garcia, Shih-Yuan Yu, Anton Kocheturov, Mohammad Abdullah Al Faruque
https://arxiv.org/abs/2509.25514

AGNOMIN - Architecture Agnostic Multi-Label Function Name Prediction
Function name prediction is crucial for understanding stripped binaries in software reverse engineering, a key step for \textbf{enabling subsequent vulnerability analysis and patching}. However, existing approaches often struggle with architecture-specific limitations, data scarcity, and diverse naming conventions. We present AGNOMIN, a novel architecture-agnostic approach for multi-label function name prediction in stripped binaries. AGNOMIN builds Feature-Enriched Hierarchical Graphs (FEHGs),…

BloomAPR: A Bloom's Taxonomy-based Framework for Assessing the Capabilities of LLM-Powered APR Solutions
Yinghang Ma (Jack), Jiho Shin (Jack), Leuson Da Silva (Jack), Zhen Ming (Jack), Jiang, Song Wang, Foutse Khomh, Shin Hwei Tan
https://arxiv.org/abs/2509.25465

BloomAPR: A Bloom's Taxonomy-based Framework for Assessing the Capabilities of LLM-Powered APR Solutions
Recent advances in large language models (LLMs) have accelerated the development of AI-driven automated program repair (APR) solutions. However, these solutions are typically evaluated using static benchmarks such as Defects4J and SWE-bench, which suffer from two key limitations: (1) the risk of data contamination, potentially inflating evaluation results due to overlap with LLM training data, and (2) limited ability to assess the APR capabilities in dynamic and diverse contexts. In this paper,…

PIPer: On-Device Environment Setup via Online Reinforcement Learning
Alexander Kovrigin, Aleksandra Eliseeva, Konstantin Grotov, Egor Bogomolov, Yaroslav Zharov
https://arxiv.org/abs/2509.25455

PIPer: On-Device Environment Setup via Online Reinforcement Learning
Environment setup-the process of configuring the system to work with a specific software project-represents a persistent challenge in Software Engineering (SE). Automated environment setup methods could assist developers by providing fully configured environments for arbitrary repositories without manual effort. This also helps SE researchers to scale execution-based benchmarks. However, recent studies reveal that even state-of-the-art Large Language Models (LLMs) achieve limited success in aut…

A Cartography of Open Collaboration in Open Source AI: Mapping Practices, Motivations, and Governance in 14 Open Large Language Model Projects
Johan Lin{\aa}ker, Cailean Osborne, Jennifer Ding, Ben Burtenshaw
https://arxiv.org/abs/2509.25397

A Cartography of Open Collaboration in Open Source AI: Mapping Practices, Motivations, and Governance in 14 Open Large Language Model Projects
The proliferation of open large language models (LLMs) is fostering a vibrant ecosystem of research and innovation in artificial intelligence (AI). However, the methods of collaboration used to develop open LLMs both before and after their public release have not yet been comprehensively studied, limiting our understanding of how open LLM projects are initiated, organized, and governed as well as what opportunities there are to foster this ecosystem even further. We address this gap through an …

Detecting and Fixing API Misuses of Data Science Libraries Using Large Language Models
Akalanka Galappaththi, Francisco Ribeiro, Sarah Nadi
https://arxiv.org/abs/2509.25378 http…

Automatically Generating Web Applications from Requirements Via Multi-Agent Test-Driven Development
Yuxuan Wan, Tingshuo Liang, Jiakai Xu, Jingyu Xiao, Yintong Huo, Michael R. Lyu
https://arxiv.org/abs/2509.25297

Automatically Generating Web Applications from Requirements Via Multi-Agent Test-Driven Development
Developing full-stack web applications is complex and time-intensive, demanding proficiency across diverse technologies and frameworks. Although recent advances in multimodal large language models (MLLMs) enable automated webpage generation from visual inputs, current solutions remain limited to front-end tasks and fail to deliver fully functional applications. In this work, we introduce TDDev, the first test-driven development (TDD)-enabled LLM-agent framework for end-to-end full-stack web app…

RANGER -- Repository-Level Agent for Graph-Enhanced Retrieval
Pratik Shah, Rajat Ghosh, Aryan Singhal, Debojyoti Dutta
https://arxiv.org/abs/2509.25257 https://

RANGER -- Repository-Level Agent for Graph-Enhanced Retrieval
General-purpose automated software engineering (ASE) includes tasks such as code completion, retrieval, repair, QA, and summarization. These tasks require a code retrieval system that can handle specific queries about code entities, or code entity queries (for example, locating a specific class or retrieving the dependencies of a function), as well as general queries without explicit code entities, or natural language queries (for example, describing a task and retrieving the corresponding code…

BuildBench: Benchmarking LLM Agents on Compiling Real-World Open-Source Software
Zehua Zhang, Ati Priya Bajaj, Divij Handa, Siyu Liu, Arvind S Raj, Hongkai Chen, Hulin Wang, Yibo Liu, Zion Leonahenahe Basque, Souradip Nath, Vishal Juneja, Nikhil Chapre, Yan Shoshitaishvili, Adam Doup\'e, Chitta Baral, Ruoyu Wang
https://arxiv.org/abs/2…

BuildBench: Benchmarking LLM Agents on Compiling Real-World Open-Source Software
Automatically compiling open-source software (OSS) projects is a vital, labor-intensive, and complex task, which makes it a good challenge for LLM Agents. Existing methods rely on manually curated rules and workflows, which cannot adapt to OSS that requires customized configuration or environment setup. Recent attempts using Large Language Models (LLMs) used selective evaluation on a subset of highly rated OSS, a practice that underestimates the realistic challenges of OSS compilation. In pract…

Protocode: Prototype-Driven Interpretability for Code Generation in LLMs
Krishna Vamshi Bodla, Haizhao Yang
https://arxiv.org/abs/2509.25247 https://arxiv.…

Protocode: Prototype-Driven Interpretability for Code Generation in LLMs
Since the introduction of Large Language Models (LLMs), they have been widely adopted for various tasks such as text summarization, question answering, speech-to-text translation, and more. In recent times, the use of LLMs for code generation has gained significant attention, with tools such as Cursor and Windsurf demonstrating the ability to analyze massive code repositories and recommend relevant changes. Big tech companies have also acknowledged the growing reliance on LLMs for code generati…

Reinforcement Learning-Guided Chain-of-Draft for Token-Efficient Code Generation
Xunzhu Tang, Iyiola Emmanuel Olatunji, Tiezhu Sun, Jacques Klein, Tegawende F. Bissyande
https://arxiv.org/abs/2509.25243

Reinforcement Learning-Guided Chain-of-Draft for Token-Efficient Code Generation
LLMs demonstrate surface-level fluency in code generation but struggle with structured reasoning tasks requiring correctness and semantic alignment. While Chain-of-Thought (CoT) prompting enhances reasoning through intermediate steps, it suffers from verbosity and inefficiency. Chain-of-Draft (CoD) prompting offers more concise reasoning, but the stochastic nature of LLMs produces varying solution quality, making optimal selection challenging. We propose \multicod, a reinforcement learning fram…

A Benchmark for Localizing Code and Non-Code Issues in Software Projects
Zejun Zhang, Jian Wang, Qingyun Yang, Yifan Pan, Yi Tang, Yi Li, Zhenchang Xing, Tian Zhang, Xuandong Li, Guoan Zhang
https://arxiv.org/abs/2509.25242

A Benchmark for Localizing Code and Non-Code Issues in Software Projects
Accurate project localization (e.g., files and functions) for issue resolution is a critical first step in software maintenance. However, existing benchmarks for issue localization, such as SWE-Bench and LocBench, are limited. They focus predominantly on pull-request issues and code locations, ignoring other evidence and non-code files such as commits, comments, configurations, and documentation. To address this gap, we introduce MULocBench, a comprehensive dataset of 1,100 issues from 46 popul…