@arXiv_csHC_bot@mastoxiv.page2025-06-26 07:37:40
Refining Participatory Design for AAC Users
Blade Frisch, Keith Vertanen
https://arxiv.org/abs/2506.19995 https://arxiv.org/pdf/2506.…
@arXiv_csHC_bot@mastoxiv.page2025-06-26 07:37:40
@tante@tldr.nettime.org2025-06-23 10:00:05
"I do not need the one magic machine that claims to solve all my issues and then makes me jump through conversational hoops to get a mediocre result. That is actually the opposite of what I need."
On chatbots as a bad design paradigm
(Original title: “ChatBot” is bad design)
https://t…
@cyrevolt@mastodon.social2025-07-25 12:20:12
I'm skimming this year's #OSDI's proceedings because operating systems are 🆒.
https://www.usenix.org/conference/osdi25/technical-sessions
@Techmeme@techhub.social2025-07-24 17:46:05
Apple releases the first public betas of iOS 26, iPadOS 26, macOS Tahoe 26, watchOS 26, and tvOS 26, with its new Liquid Glass design language (Jay Peters/The Verge)
https://www.theverge.com/news/695142/apple-public-betas-liquid-glass-ios-macos-26…
@arXiv_csCY_bot@mastoxiv.page2025-08-26 10:29:17
Do Students Learn Better Together? Teaching Design Patterns and the OSI Model with the Aronson Method
Daniel San Martin, Carlos Manzano, Valter Vieira de Camargo
https://arxiv.org/abs/2508.16770
@arXiv_eessSY_bot@mastoxiv.page2025-07-25 09:52:22
Design and optimization of a novel leaf-shape antenna for RF energy transfer
Junbin Zhong, Mingtong Chen, Zhengbao Yang
https://arxiv.org/abs/2507.18630 https://
@EarthOrgUK@mastodon.energy2025-08-24 03:23:03
@arXiv_csSE_bot@mastoxiv.page2025-08-25 09:27:20
The (C)omprehensive (A)rchitecture (P)attern (I)ntegration method: Navigating the sea of technology
Sebastian Copei, Oliver Hohlfeld, Jens Kosiol
https://arxiv.org/abs/2508.16341
@thomasfuchs@hachyderm.io2025-07-24 03:29:37
The latest iOS beta hides tabs in the browser on the iPad when you scroll? Whyyyy, they’re meant to quickly switch between shit.
Seriously whoever leads software design at Apple these days should really consider a career change.
@arXiv_csCR_bot@mastoxiv.page2025-06-24 12:06:20
Design high-confidence computers using trusted instructional set architecture and emulators
Shuangbao Paul Wang
https://arxiv.org/abs/2506.18780 https://…
@tezoatlipoca@mas.to2025-06-23 16:20:44
The tech lead next to me has been on the phone the past 3 (work) days fielding calls from the service team installing a prototype system.
Every single problem they've encountered so far is one I - a year ago - suggested a quality-of-life/design-for-serviceability/ux improvement to mitigate, minimize or deal with should it occur.
Our product managers know our customer's industry inside and out but not one has commercial software experience. I have 30 yrs of it.
T…
@tante@tldr.nettime.org2025-06-19 09:27:11
I think that "chatbot" is a bad design paradigm for most problems. It's actually the refusal to to design work.
https://tante.cc/2025/06/19/chatbot-is-bad-design/
@arXiv_csSE_bot@mastoxiv.page2025-06-25 09:40:10
Simulating the Waterfall Model: A Systematic Review
Antonios Saravanos (New York University)
https://arxiv.org/abs/2506.19653 https://
@arXiv_csAR_bot@mastoxiv.page2025-07-22 08:30:10
When Pipelined In-Memory Accelerators Meet Spiking Direct Feedback Alignment: A Co-Design for Neuromorphic Edge Computing
Haoxiong Ren, Yangu He, Kwunhang Wong, Rui Bao, Ning Lin, Zhongrui Wang, Dashan Shang
https://arxiv.org/abs/2507.15603
@gadgetboy@gadgetboy.social2025-08-19 11:13:15
This is an ambitious, beautifully designed "digital book."
https://www.makingsoftware.com/
@arXiv_eessSP_bot@mastoxiv.page2025-07-21 09:19:10
Toward Practical Fluid Antenna Systems: Co-Optimizing Hardware and Software for Port Selection and Beamforming
Sai Xu, Kai-Kit Wong, Yanan Du, Hanjiang Hong, Chan-Byoung Chae, Baiyang Liu, Kin-Fai Tong
https://arxiv.org/abs/2507.14035
@ErikJonker@mastodon.social2025-08-13 13:59:39
How AI Vibe Coding Is Erasing Developers’ Skills
Developers believe AI is boosting their productivity, but it is actually weakening core coding skills. Vibe coding is creating a generation of devs who cannot debug, design, or solve problems without AI.
https://www.finalroundai.com/blog…
@arXiv_quantph_bot@mastoxiv.page2025-08-19 11:03:30
The Evolution of IBM's Quantum Information Software Kit (Qiskit): A Review of its Applications
Param Pathak, K Tarakeshwar, Syed Sufiyan Ali, Shalini Devendrababu, Adarsh Ganesan
https://arxiv.org/abs/2508.12245
@arXiv_csSE_bot@mastoxiv.page2025-07-22 10:15:30
Emerging Trends in Software Architecture from the Practitioners Perspective: A Five Year Review
Ruoyu Su, Noman ahmad, Matteo Esposito, Andrea Janes, Davide Taibi, Valentina Lenarduzzi
https://arxiv.org/abs/2507.14554
@arXiv_csPL_bot@mastoxiv.page2025-08-22 09:13:41
Exploring the Theory and Practice of Concurrency in the Entity-Component-System Pattern
Patrick Redmond, Jonathan Castello, Jos\'e Manuel Calder\'on Trilla, Lindsey Kuper
https://arxiv.org/abs/2508.15264
@Techmeme@techhub.social2025-07-22 07:55:46
While AI hasn't yet led to new physics discoveries, the tech is proving powerful in the field, aiding in experiment design and spotting patterns in complex data (Anil Ananthaswamy/Quanta Magazine)
https://www.quantamagazine.org/ai-comes-up
@frankel@mastodon.top2025-07-10 16:22:00
Most #RESTful #APIs aren't really RESTful
https://florian-krae…
@arXiv_csCL_bot@mastoxiv.page2025-07-14 10:01:12
Multilingual Multimodal Software Developer for Code Generation
Linzheng Chai, Jian Yang, Shukai Liu, Wei Zhang, Liran Wang, Ke Jin, Tao Sun, Congnan Liu, Chenchen Zhang, Hualei Zhu, Jiaheng Liu, Xianjie Wu, Ge Zhang, Tianyu Liu, Zhoujun Li
https://arxiv.org/abs/2507.08719
@arXiv_csRO_bot@mastoxiv.page2025-08-19 11:15:20
Mechanical Automation with Vision: A Design for Rubik's Cube Solver
Abhinav Chalise, Nimesh Gopal Pradhan, Nishan Khanal, Prashant Raj Bista, Dinesh Baniya Kshatri
https://arxiv.org/abs/2508.12469 …
@arXiv_csLG_bot@mastoxiv.page2025-08-15 10:17:42
Electromagnetic Simulations of Antennas on GPUs for Machine Learning Applications
Murat Temiz, Vemund Bakken
https://arxiv.org/abs/2508.10713 https://arxiv…
@abstractsun@social.linux.pizza2025-06-12 02:26:00
Oh wow, MacOS's new liquid glass themes look genuinely cool. I hope the ricing community picks it up.
https://www.apple.com/newsroom/2025/06/apple-introduces-a-delightful-and-elegant-new-software-design/
@arXiv_csNE_bot@mastoxiv.page2025-06-17 09:53:13
Energy-Efficient Digital Design: A Comparative Study of Event-Driven and Clock-Driven Spiking Neurons
Filippo Marostica, Alessio Carpegna, Alessandro Savino, Stefano Di Carlo
https://arxiv.org/abs/2506.13268
@Techmeme@techhub.social2025-06-09 17:20:49
Apple unveils a Liquid Glass design language across its platforms, adding transparency and glass shine effects to Apple's in-app interfaces (Tom Warren/The Verge)
https://www.theverge.com/news/682636/apple-liquid-design-glass-theme-wwdc-2025
@EarthOrgUK@mastodon.energy2025-08-17 09:51:03
@fanf@mendeddrum.org2025-06-13 14:42:04
from my link log —
CHERIoT: the last ten years.
https://cheriot.org/cheri/history/2025/05/16/last-ten-years.html
saved 2025-05-17
@arXiv_csSE_bot@mastoxiv.page2025-07-22 09:53:10
Developing Shared Vocabulary System For Collaborative Software Engineering
Carey Lai Zheng Hui, Johnson Britto Jessia Esther Leena, Kumuthini Subramanian, Zhao Chenyu, Shubham Rajeshkumar Jariwala
https://arxiv.org/abs/2507.14396
@arXiv_csAR_bot@mastoxiv.page2025-08-20 09:01:30
Sustainable AI Training via Hardware-Software Co-Design on NVIDIA, AMD, and Emerging GPU Architectures
Yashasvi Makin, Rahul Maliakkal
https://arxiv.org/abs/2508.13163 https://
@arXiv_csDC_bot@mastoxiv.page2025-08-20 10:41:41
Crosslisted article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Sustainable AI Training via Hardware-Software Co-Design on NVIDIA, AMD, and Emerging GPU Architec...
Yashasvi Makin, Rahul Maliakkal
@arXiv_econGN_bot@mastoxiv.page2025-06-18 08:43:48
Life cycle assessment tools for road design: analysing linearity assumptions
Nikolaos Kalyviotis
https://arxiv.org/abs/2506.13896 https://
@arXiv_physicsinsdet_bot@mastoxiv.page2025-07-16 09:09:41
Practical Software Approach to Digital Pulse Processing
Jing Liu
https://arxiv.org/abs/2507.11360 https://arxiv.org/pdf/2507.11360
@inthehands@hachyderm.io2025-06-10 02:47:02
People keep making the same mistake, again and again and again and again forever, of thinking that it is syntax that makes software development hard.
Oh honey.
Re this from @mathaetaes:
https://infosec.exchange/@mathaetaes/114656764053846137
(P.S. Visual coding is actually really cool, and IMO an underexplored PL design space — but is very much coding, and very much tricky for the same reasons as any other kind of coding.)
@arXiv_csCR_bot@mastoxiv.page2025-08-15 08:51:02
Certifiably robust malware detectors by design
Pierre-Francois Gimenez, Sarath Sivaprasad, Mario Fritz
https://arxiv.org/abs/2508.10038 https://arxiv.org/p…
@arXiv_csHC_bot@mastoxiv.page2025-06-18 08:27:21
Controlling Context: Generative AI at Work in Integrated Circuit Design and Other High-Precision Domains
Emanuel Moss, Elizabeth Watkins, Christopher Persaud, Passant Karunaratne, Dawn Nafus
https://arxiv.org/abs/2506.14567
@arXiv_mathOC_bot@mastoxiv.page2025-06-19 09:07:22
An efficient co-simulation and control approach to tackle complex multi-domain energetic systems: concepts and applications of the PEGASE platform
Mathieu Vallee (DTCH), Roland Baviere (DTCH), Val\'erie Seguin (DTCH), Val\'ery Vuillerme (DTCH), Nicolas Lamaison (DTCH), Michael Nikhil Descamps (DTCH), Antoine Aurousseau (DTCH)
https://
@deprogrammaticaipsum@mas.to2025-05-29 10:23:40
"Mike gave a legendary talk called “F*ck You, Pay Me,” with a title inspired by a quote from the late Ray Liotta in the 1990 movie “Goodfellas.”
What is interesting about this talk is that, despite being primarily directed to designers, its contents are immediately suitable for freelancing software engineers, a profession whose work is, at least since the return of Steve Jobs to Apple in 1997, inextricably linked to that of designers."
@arXiv_csNI_bot@mastoxiv.page2025-08-15 08:43:12
Design of a Timer Queue Supporting Dynamic Update Operations
Zekun Wang, Binghao Yue, Weitao Pan, Jiangyi Shi, Yue Hao
https://arxiv.org/abs/2508.10283 https://
@arXiv_csSE_bot@mastoxiv.page2025-08-19 09:02:00
WIP: Leveraging LLMs for Enforcing Design Principles in Student Code: Analysis of Prompting Strategies and RAG
Dhruv Kolhatkar, Soubhagya Akkena, Edward F. Gehringer
https://arxiv.org/abs/2508.11717
@arXiv_csRO_bot@mastoxiv.page2025-07-17 09:57:20
Design and Development of an Automated Contact Angle Tester (ACAT) for Surface Wettability Measurement
Connor Burgess, Kyle Douin, Amir Kordijazi
https://arxiv.org/abs/2507.12431 …
@tiotasram@kolektiva.social2025-08-04 15:49:00
Should we teach vibe coding? Here's why not.
Should AI coding be taught in undergrad CS education?
1/2
I teach undergraduate computer science labs, including for intro and more-advanced core courses. I don't publish (non-negligible) scholarly work in the area, but I've got years of craft expertise in course design, and I do follow the academic literature to some degree. In other words, In not the world's leading expert, but I have spent a lot of time thinking about course design, and consider myself competent at it, with plenty of direct experience in what knowledge & skills I can expect from students as they move through the curriculum.
I'm also strongly against most uses of what's called "AI" these days (specifically, generative deep neutral networks as supplied by our current cadre of techbro). There are a surprising number of completely orthogonal reasons to oppose the use of these systems, and a very limited number of reasonable exceptions (overcoming accessibility barriers is an example). On the grounds of environmental and digital-commons-pollution costs alone, using specifically the largest/newest models is unethical in most cases.
But as any good teacher should, I constantly question these evaluations, because I worry about the impact on my students should I eschew teaching relevant tech for bad reasons (and even for his reasons). I also want to make my reasoning clear to students, who should absolutely question me on this. That inspired me to ask a simple question: ignoring for one moment the ethical objections (which we shouldn't, of course; they're very stark), at what level in the CS major could I expect to teach a course about programming with AI assistance, and expect students to succeed at a more technically demanding final project than a course at the same level where students were banned from using AI? In other words, at what level would I expect students to actually benefit from AI coding "assistance?"
To be clear, I'm assuming that students aren't using AI in other aspects of coursework: the topic of using AI to "help you study" is a separate one (TL;DR it's gross value is not negative, but it's mostly not worth the harm to your metacognitive abilities, which AI-induced changes to the digital commons are making more important than ever).
So what's my answer to this question?
If I'm being incredibly optimistic, senior year. Slightly less optimistic, second year of a masters program. Realistic? Maybe never.
The interesting bit for you-the-reader is: why is this my answer? (Especially given that students would probably self-report significant gains at lower levels.) To start with, [this paper where experienced developers thought that AI assistance sped up their work on real tasks when in fact it slowed it down] (https://arxiv.org/abs/2507.09089) is informative. There are a lot of differences in task between experienced devs solving real bugs and students working on a class project, but it's important to understand that we shouldn't have a baseline expectation that AI coding "assistants" will speed things up in the best of circumstances, and we shouldn't trust self-reports of productivity (or the AI hype machine in general).
Now we might imagine that coding assistants will be better at helping with a student project than at helping with fixing bugs in open-source software, since it's a much easier task. For many programming assignments that have a fixed answer, we know that many AI assistants can just spit out a solution based on prompting them with the problem description (there's another elephant in the room here to do with learning outcomes regardless of project success, but we'll ignore this over too, my focus here is on project complexity reach, not learning outcomes). My question is about more open-ended projects, not assignments with an expected answer. Here's a second study (by one of my colleagues) about novices using AI assistance for programming tasks. It showcases how difficult it is to use AI tools well, and some of these stumbling blocks that novices in particular face.
But what about intermediate students? Might there be some level where the AI is helpful because the task is still relatively simple and the students are good enough to handle it? The problem with this is that as task complexity increases, so does the likelihood of the AI generating (or copying) code that uses more complex constructs which a student doesn't understand. Let's say I have second year students writing interactive websites with JavaScript. Without a lot of care that those students don't know how to deploy, the AI is likely to suggest code that depends on several different frameworks, from React to JQuery, without actually setting up or including those frameworks, and of course three students would be way out of their depth trying to do that. This is a general problem: each programming class carefully limits the specific code frameworks and constructs it expects students to know based on the material it covers. There is no feasible way to limit an AI assistant to a fixed set of constructs or frameworks, using current designs. There are alternate designs where this would be possible (like AI search through adaptation from a controlled library of snippets) but those would be entirely different tools.
So what happens on a sizeable class project where the AI has dropped in buggy code, especially if it uses code constructs the students don't understand? Best case, they understand that they don't understand and re-prompt, or ask for help from an instructor or TA quickly who helps them get rid of the stuff they don't understand and re-prompt or manually add stuff they do. Average case: they waste several hours and/or sweep the bugs partly under the rug, resulting in a project with significant defects. Students in their second and even third years of a CS major still have a lot to learn about debugging, and usually have significant gaps in their knowledge of even their most comfortable programming language. I do think regardless of AI we as teachers need to get better at teaching debugging skills, but the knowledge gaps are inevitable because there's just too much to know. In Python, for example, the LLM is going to spit out yields, async functions, try/finally, maybe even something like a while/else, or with recent training data, the walrus operator. I can't expect even a fraction of 3rd year students who have worked with Python since their first year to know about all these things, and based on how students approach projects where they have studied all the relevant constructs but have forgotten some, I'm not optimistic seeing these things will magically become learning opportunities. Student projects are better off working with a limited subset of full programming languages that the students have actually learned, and using AI coding assistants as currently designed makes this impossible. Beyond that, even when the "assistant" just introduces bugs using syntax the students understand, even through their 4th year many students struggle to understand the operation of moderately complex code they've written themselves, let alone written by someone else. Having access to an AI that will confidently offer incorrect explanations for bugs will make this worse.
To be sure a small minority of students will be able to overcome these problems, but that minority is the group that has a good grasp of the fundamentals and has broadened their knowledge through self-study, which earlier AI-reliant classes would make less likely to happen. In any case, I care about the average student, since we already have plenty of stuff about our institutions that makes life easier for a favored few while being worse for the average student (note that our construction of that favored few as the "good" students is a large part of this problem).
To summarize: because AI assistants introduce excess code complexity and difficult-to-debug bugs, they'll slow down rather than speed up project progress for the average student on moderately complex projects. On a fixed deadline, they'll result in worse projects, or necessitate less ambitious project scoping to ensure adequate completion, and I expect this remains broadly true through 4-6 years of study in most programs (don't take this as an endorsement of AI "assistants" for masters students; we've ignored a lot of other problems along the way).
There's a related problem: solving open-ended project assignments well ultimately depends on deeply understanding the problem, and AI "assistants" allow students to put a lot of code in their file without spending much time thinking about the problem or building an understanding of it. This is awful for learning outcomes, but also bad for project success. Getting students to see the value of thinking deeply about a problem is a thorny pedagogical puzzle at the best of times, and allowing the use of AI "assistants" makes the problem much much worse. This is another area I hope to see (or even drive) pedagogical improvement in, for what it's worth.
1/2
@arXiv_csMS_bot@mastoxiv.page2025-08-15 08:19:52
TensorKit.jl: A Julia package for large-scale tensor computations, with a hint of category theory
Lukas Devos, Jutho Haegeman
https://arxiv.org/abs/2508.10076 https://
@EarthOrgUK@mastodon.energy2025-08-14 09:51:03
@arXiv_csSE_bot@mastoxiv.page2025-06-24 09:48:20
Re-Evaluating Code LLM Benchmarks Under Semantic Mutation
Zhiyuan Pan, Xing Hu, Xin Xia, Xiaohu Yang
https://arxiv.org/abs/2506.17369 https://
@Techmeme@techhub.social2025-08-12 00:20:59
Q&A with Notion CEO Ivan Zhao on Notion's evolution into an "AI workspace", being profitable, B2B vs. B2C, usage-based pricing for AI, and more (Casey Newton/The Verge)
https://www.theverge.com/decoder-podcast-w
@arXiv_quantph_bot@mastoxiv.page2025-08-20 10:10:40
Roadblocks and Opportunities in Quantum Algorithms -- Insights from the National Quantum Initiative Joint Algorithms Workshop, May 20--22, 2024
Eliot Kapit, Peter Love, Jeffrey Larson, Andrew Sornborger, Eleanor Crane, Alexander Schuckert, Teague Tomesh, Frederic Chong, Sabre Kais
https://arxiv.org/abs/2508.13973
@thomastraynor@social.linux.pizza2025-07-11 15:14:38
Yah, that is going to go over so well. About half my career is now supporting old stuff where the 'experts' claimed that it will replace programmers. Most of what I work on to be charitable is unmaintainable and inefficient code unless we use the developer package from the vendor. It has the potential to be a good tool that generates parts of the boring code, but needs someone who knows what the hell they are doing to make it secure and efficient! It also needs great (or a least …
@arXiv_csHC_bot@mastoxiv.page2025-07-16 09:31:11
Towards Creating Infrastructures for Values and Ethics Work in the Production of Software Technologies
Richmond Y. Wong
https://arxiv.org/abs/2507.11490 ht…
@arXiv_csDC_bot@mastoxiv.page2025-08-15 08:00:52
Dalek: An Unconventional and Energy-Aware Heterogeneous Cluster
Adrien Cassagne (ALSOC), No\'e Amiot (ALSOC), Manuel Bouyer (ALSOC)
https://arxiv.org/abs/2508.10481 https://…
@Techmeme@techhub.social2025-07-14 08:50:37
China approves Synopsys' acquisition of simulation software company Ansys, after the US lifted restrictions on chip design software sales to China (Reuters)
https://www.reuters.com/world/china/china-grants-conditional-approval-syno…
@arXiv_csSE_bot@mastoxiv.page2025-08-22 08:39:41
Foundational Design Principles and Patterns for Building Robust and Adaptive GenAI-Native Systems
Frederik Vandeputte
https://arxiv.org/abs/2508.15411 https://
@arXiv_csAR_bot@mastoxiv.page2025-06-06 07:15:29
QiMeng: Fully Automated Hardware and Software Design for Processor Chip
Rui Zhang, Yuanbo Wen, Shuyao Cheng, Di Huang, Shaohui Peng, Jiaming Guo, Pengwei Jin, Jiacheng Zhao, Tianrui Ma, Yaoyu Zhu, Yifan Hao, Yongwei Zhao, Shengwen Liang, Ying Wang, Xing Hu, Zidong Du, Huimin Cui, Ling Li, Qi Guo, Yunji Chen
https://arxiv.org/abs…
@arXiv_eessSP_bot@mastoxiv.page2025-08-11 09:09:40
Fast End-to-End Simulation and Exploration of Many-RISCV-Core Baseband Transceivers for Software-Defined Radio-Access Networks
Marco Bertuletti, Yichao Zhang, Mahdi Abdollahpour, Samuel Riedel, Alessandro Vanelli-Coralli
https://arxiv.org/abs/2508.06141
@arXiv_csSE_bot@mastoxiv.page2025-07-23 09:37:12
Search-based Generation of Waypoints for Triggering Self-Adaptations in Maritime Autonomous Vessels
Karoline Nyl{\ae}nder, Aitor Arrieta, Shaukat Ali, Paolo Arcaini
https://arxiv.org/abs/2507.16327
@arXiv_csHC_bot@mastoxiv.page2025-07-30 09:32:41
Identification of Design Recommendations for Augmented Reality Authors in Corporate Training
Stefan Graser, Martin Schrepp, Stephan B\"ohm
https://arxiv.org/abs/2507.21722 …
@frankel@mastodon.top2025-06-03 08:22:02
Residuality Theory: A Rebellious Take on Building Systems That Actually Survive
#resiliency
@arXiv_mathOC_bot@mastoxiv.page2025-08-05 09:02:30
Service Time Window Design in Last-Mile Delivery
Davod Hosseini, Borzou Rostami, Mojtaba Araghi
https://arxiv.org/abs/2508.01032 https://arxiv.org/pdf/2508…
@arXiv_csDC_bot@mastoxiv.page2025-06-12 07:26:01
Multi-GPU Acceleration of PALABOS Fluid Solver using C Standard Parallelism
Jonas Latt, Christophe Coreixas
https://arxiv.org/abs/2506.09242 https://
@Techmeme@techhub.social2025-06-12 13:25:41
Nominal, which makes industrial design software for space, energy, and defense tech, raised $75M led by Sequoia, after raising $27M in April 2024 (Paayal Zaveri/Bloomberg)
https://www.bloomberg.com/news/articles/20
@arXiv_quantph_bot@mastoxiv.page2025-07-31 10:01:31
DEQSE Quantum IDE Extension: Integrated Tool for Quantum Software Engineering
Majid Haghparast, Ronja Heikkinen, Samuel Ovaskainen, Julian Fuchs, Jussi P P Jokinen, Tommi Mikkonen
https://arxiv.org/abs/2507.22843
@arXiv_csCR_bot@mastoxiv.page2025-07-01 10:53:53
Not quite a piece of CHERI-cake: Are new digital security by design architectures usable?
Maysara Alhindi, Joseph Hallett
https://arxiv.org/abs/2506.23682 …
@Techmeme@techhub.social2025-08-07 19:05:48
OpenAI highlights GPT-5 scores on math, coding, and health benchmarks: 94.6% on AIME 2025 without tools, 74.9% on SWE-bench Verified, 46.2% on HealthBench Hard (Carl Franzen/VentureBeat)
https://venturebeat.com/ai/openai-launches-gpt-5-n…
@arXiv_csSE_bot@mastoxiv.page2025-07-17 08:04:40
REST in Pieces: RESTful Design Rule Violations in Student-Built Web Apps
Sergio Di Meglio, Valeria Pontillo, Luigi Libero Lucio Starace
https://arxiv.org/abs/2507.11689
@arXiv_csDC_bot@mastoxiv.page2025-06-12 07:26:21
A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO
Jonas Svedas, Hannah Watson, Nathan Laubeuf, Diksha Moolchandani, Abubakr Nada, Arjun Singh, Dwaipayan Biswas, James Myers, Debjyoti Bhattacharjee
https://arxiv.org/abs/2506.09275
@Techmeme@techhub.social2025-06-13 04:50:44
Sources: China delays approval of Synopsys' Ansys deal as the US moved in late May to ban chip design software sales by US firms, including Synopsys, to China (Financial Times)
https://www.ft.com/content/762b1818-795d-4270-b6cc-5d902d8bc0a8
@arXiv_csAR_bot@mastoxiv.page2025-07-02 07:39:40
ChatHLS: Towards Systematic Design Automation and Optimization for High-Level Synthesis
Runkai Li, Jia Xiong, Xiuyuan He, Jieru Zhao, Qiang Xu, Xi Wang
https://arxiv.org/abs/2507.00642
@arXiv_csSE_bot@mastoxiv.page2025-06-16 10:19:59
Augmenting the Generality and Performance of Large Language Models for Software Engineering
Fabian C. Pe\~na
https://arxiv.org/abs/2506.11548 https://
@Techmeme@techhub.social2025-07-06 03:25:51
An interview with Autodesk CEO Andrew Anagnost on settling the proxy fight with Starboard Value, serving the "design and make" workforce, US reshoring, and more (Andrew Edgecliffe-Johnson/Semafor)
https://www.semafor.com/article/07/02/2025
@arXiv_csSE_bot@mastoxiv.page2025-06-09 07:53:42
Survey of LLM Agent Communication with MCP: A Software Design Pattern Centric Review
Anjana Sarkar, Soumyendu Sarkar
https://arxiv.org/abs/2506.05364 https…
@arXiv_quantph_bot@mastoxiv.page2025-07-11 09:46:01
Quantum Executor: A Unified Interface for Quantum Computing
Giuseppe Bisicchia, Alessandro Bocci, Antonio Brogi
https://arxiv.org/abs/2507.07597 https://…
@arXiv_csAR_bot@mastoxiv.page2025-06-17 09:17:27
Reconfigurable Digital RRAM Logic Enables In-Situ Pruning and Learning for Edge AI
Songqi Wang, Yue Zhang, Jia Chen, Xinyuan Zhang, Yi Li, Ning Lin, Yangu He, Jichang Yang, Yingjie Yu, Yi Li, Zhongrui Wang, Xiaojuan Qi, Han Wang
https://arxiv.org/abs/2506.13151
@arXiv_csSE_bot@mastoxiv.page2025-07-18 09:05:12
LLM-Powered Quantum Code Transpilation
Nazanin Siavash, Armin Moin
https://arxiv.org/abs/2507.12480 https://arxiv.org/pdf/2507.12480
@Techmeme@techhub.social2025-07-03 01:50:40
Siemens says it has restored full access to its chip design software for its Chinese clients after the Trump admin lifted export curbs, initially imposed in May (Mackenzie Hawkins/Bloomberg)
https://www.bloomberg.com/news/articles/20
@arXiv_csSE_bot@mastoxiv.page2025-06-18 08:57:13
Designing a Custom Chaos Engineering Framework for Enhanced System Resilience at Softtech
Ethem Utku Aktas, Burak Tuzlutas, Burak Yesiltas
https://arxiv.org/abs/2506.14281
@arXiv_csHC_bot@mastoxiv.page2025-08-12 11:15:13
Are UX evaluation methods truly accessible
Andr\'es Eduardo Fuentes-Cort\'azar, Alejandra Rivera-Hern\'andez, Jos\'e Rafael Rojano-C\'aceres
https://arxiv.org/abs/2508.07620
@Techmeme@techhub.social2025-06-03 11:05:42
Sources: Xiaomi is among the Chinese tech companies most impacted by new US chip design export rules, affecting its new 3nm mobile chip made by TSMC in Taiwan (Financial Times)
https://www.ft.com/content/2b0a0000-1bf6-475a-ac96-c17212afecc2
@arXiv_csAR_bot@mastoxiv.page2025-08-05 07:45:30
GSIM: Accelerating RTL Simulation for Large-Scale Designs
Lu Chen, Dingyi Zhao, Zihao Yu, Ninghui Sun, Yungang Bao
https://arxiv.org/abs/2508.02236 https://
@Techmeme@techhub.social2025-06-09 22:25:49
Apple unveils several changes to CarPlay in iOS 26, including a Liquid Glass re-design, a dashboard for Live Activities, and broad support for widgets (Joe Rossignol/MacRumors)
https://www.macrumors.com/2025/06/09/ios-26-upgrades-carplay-in-five-ways/
@arXiv_csSE_bot@mastoxiv.page2025-06-16 10:07:19
Refactoring Codebases through Library Design
Ziga Kovacic, Celine Lee, Justin Chiu, Wenting Zhao, Kevin Ellis
https://arxiv.org/abs/2506.11058 https://
@arXiv_csSE_bot@mastoxiv.page2025-06-30 08:07:30
Using Generative AI in Software Design Education: An Experience Report
Victoria Jackson, Susannah Liu, Andre van der Hoek
https://arxiv.org/abs/2506.21703 …
@Techmeme@techhub.social2025-06-08 14:55:39
Thoughts on the major design overhaul of Apple's OSes with "Liquid Glass" UI elements, which will launch at WWDC and set the stage for fresh hardware products (Mark Gurman/Bloomberg)
https://www.bloombe…
@arXiv_csSE_bot@mastoxiv.page2025-07-30 08:28:12
MAAD: Automate Software Architecture Design through Knowledge-Driven Multi-Agent Collaboration
Ruiyin Li, Yiran Zhang, Xiyu Zhou, Peng Liang, Weisong Sun, Jifeng Xuan, Zhi Jin, Yang Liu
https://arxiv.org/abs/2507.21382
@arXiv_csSE_bot@mastoxiv.page2025-06-13 08:32:50
Formalising Software Requirements using Large Language Models
Arshad Beg, Diarmuid O'Donoghue, Rosemary Monahan
https://arxiv.org/abs/2506.10704 https:…
@arXiv_csSE_bot@mastoxiv.page2025-07-08 08:33:20
The Impact of LLM-Assistants on Software Developer Productivity: A Systematic Literature Review
Amr Mohamed, Maram Assi, Mariam Guizani
https://arxiv.org/abs/2507.03156
@arXiv_csSE_bot@mastoxiv.page2025-07-04 09:24:41
VeFIA: An Efficient Inference Auditing Framework for Vertical Federated Collaborative Software
Chung-ju Huang, Ziqi Zhang, Yinggui Wang, Binghui Wang, Tao Wei, Leye Wang
https://arxiv.org/abs/2507.02376
@Techmeme@techhub.social2025-07-01 19:50:52
Figma files for an IPO, plans to trade on the NYSE under FIG, and reports Q1 revenue increased 46% YoY to $228.2M and net income grew from $13.5M to $44.9M YoY (Jordan Novet/CNBC)
https://www.cnbc.com/2025/07/01/figma-fig-files-for-ipo-as-tec…
@arXiv_csSE_bot@mastoxiv.page2025-07-02 10:01:50
Out of the Day Job: Perspectives of Industry Practitioners in Co-Design and Delivery of Software Engineering Courses
Gillian Daniel, Chris Hall, Per Hammer, Alec-Angus Macdonald, Hollie Marwick-Best, Emma McKenzie, George Popa, Derek Somerville, Tim Storer
https://arxiv.org/abs/2507.00803
@Techmeme@techhub.social2025-06-27 13:30:53
Sources: mass production of Microsoft's next AI chip is delayed to 2026 and is expected to underperform Nvidia's Blackwell chip, released in late 2024 (The Information)
https://www.theinformation.com/articles/microsofts-ai-chip-effort-falls-b…
@arXiv_csSE_bot@mastoxiv.page2025-07-02 09:22:29
The Influence of HEXACO Personality Traits on the Teamwork Quality in Software Teams -- A Preliminary Research Approach
Philipp M. Z\"ahl, Sabine Theis, Martin R. Wolf
https://arxiv.org/abs/2507.00481
@arXiv_csSE_bot@mastoxiv.page2025-06-12 08:06:51
Towards Bridging Formal Methods and Human Interpretability
Abhijit Paul, Proma Chowdhury, Kazi Sakib
https://arxiv.org/abs/2506.09759 https://
@arXiv_csSE_bot@mastoxiv.page2025-06-05 07:23:17
Multi-Language Detection of Design Pattern Instances
Hugo Andrade, Jo\~ao Bispo, Filipe F. Correia
https://arxiv.org/abs/2506.03903 https://
@arXiv_csSE_bot@mastoxiv.page2025-07-01 10:18:03
What Challenges Do Developers Face When Using Verification-Aware Programming Languages?
Francisco Oliveira, Alexandra Mendes, Carolina Carreira
https://arxiv.org/abs/2506.23696
@arXiv_csSE_bot@mastoxiv.page2025-07-14 07:46:12
The State of Computational Science in Fission and Fusion Energy
Andrea Morales Coto, Aditi Verma
https://arxiv.org/abs/2507.08061 https://
@arXiv_csSE_bot@mastoxiv.page2025-06-06 09:40:38
This https://arxiv.org/abs/2506.03903 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…
@arXiv_csSE_bot@mastoxiv.page2025-08-06 09:31:50
A System Model Generation Benchmark from Natural Language Requirements
Dongming Jin, Zhi Jin, Linyu Li, Zheng Fang, Jia Li, Xiaohong Chen
https://arxiv.org/abs/2508.03215 https:…
@arXiv_csSE_bot@mastoxiv.page2025-06-30 12:34:16
Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Domain-Driven Design in Software Development: A Systematic Literature Review on Implementation, C...
Ozan \"Ozkan, \"Onder Babur, Mark van den Brand
@arXiv_csSE_bot@mastoxiv.page2025-08-04 08:57:11
Functional vs. Object-Oriented: Comparing How Programming Paradigms Affect the Architectural Characteristics of Systems
Briza Mel Dias de Sousa (University of S\~ao Paulo), Renato Cordeiro Ferreira (University of S\~ao Paulo, Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University), Alfredo Goldman (University of S\~ao Paulo)
h…
