Incoming Assistant Professor @HKUSTGuangzhou, PhD @sgSMU

Joined November 2016
Zhensu Sun retweeted
ok it's official... AI-written unit tests and integration tests are empirically proven to be unhelpful. you should tell your agents to stop writing tests by themselves on the deepswe eval set, banning sonnet 5.5 high from writing any tests actually resulted in slightly higher success rate (non stat-sig), with less time and token spent (stat-sig). this is pretty hard empirical evidence across the tests that were written by the "tests allowed" baseline arm, 65% of them were unit tests, 35% were integration tests, neither bucket resulted in any improvement compared to not writing any tests at all i also picked a random sample of 44 tasks subset where i completely disabled executing even existing tests - it also did not affect success rate at all i spot checked many tests written in the baseline arm, and my intuition is that most tests are simply a repetition of the implementation agent-written tests do not add any value because both the implementation and the tests were simply the agent's interpretation of our intent. the tests aren't any more accurate than the implementation itself i suspect we can still extract some value from unit tests and integration tests if we describe them ourselves when we believe we can articulate our intent better through test cases than through requirements. but i have not proven this yet also worth noting, during deepswe eval the agent wrote almost no e2e tests (only 17, compared to 3000+ unit/integration tests written). so this analysis does not prove nor disprove the value of e2e tests - i will do another eval specifically for that
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Zhensu Sun retweeted
Yeah
Software was eaten by AI.
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Zhensu Sun retweeted
java is the most token-efficient language, let that sink in
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Stripping code formatting cuts LLM token cost without hurting accuracy. Average input tokens drop by 24.5%, with output quality basically unchanged. The core issue is simple, indentation, spaces, and newlines help humans read but they inflate tokens that models pay to process. They remove only cosmetic formatting while keeping program meaning identical, checked by matching the abstract syntax tree of the code. They test Fill in the Middle code completion, where a model fills a missing block, across Java, C++, C#, and Python. Performance stays stable on unformatted input, big models barely move, smaller ones wobble a bit, Python sees less savings because its layout is part of the language. One surprise, models still print nicely formatted code even when given smashed input, so output token savings are small. To fix that, 2 cheap tactics work, explicit prompts that say output without formatting, and light fine tuning on unformatted samples. With clear instructions or tiny training, output length shrinks by 25% to 36% while pass rate on the first try holds. They also ship a tool that strips formatting before inference then restores it after, so humans read clean code while the model pays less. ---- Paper – arxiv. org/abs/2508.13666 Paper Title: "The Hidden Cost of Readability: How Code Formatting Silently Consumes Your LLM Budget"
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Zhensu Sun retweeted
🚨 Big Announcement! 🚨 We’re thrilled to welcome two distinguished keynote speakers to #FORGE2025! ✨ Prem Devanbu @devanbu (@UCDavis Professor) 🔗 cs.ucdavis.edu/~devanbu/ ✨ Graham Neubig @gneubig (@CarnegieMellon Associate Professor ) 🔗 phontron.com/
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Zhensu Sun retweeted
OpenAI whistleblower Suchir Balaji, who accused the company of breaking copyright law, found dead in apparent suicide
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Zhensu Sun retweeted
🎉 Exciting News! 🎉 We are thrilled to announce that ACM SIGSOFT has officially upgraded FORGE from an ICSE Special Event to an ICSE Co-Located Conference! 🚀 We can’t wait to see your submissions for FORGE 2025! See more below👇 #FORGE #FORGE2025 @ICSEconf
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AI is not making any progress"? Look closer. 🙄 GPT-4 level models got 240x cheaper in just 2 years! AI progress isn't linear and is just about bigger models. BERT -> DistilBERT Llama 2 70B -> Llama 3 8B GPT-4 -> GPT-4o-mini Llama 3 405B → Llama 4 70B?? 🤔 Models get bigger, then smaller but equally powerful. It's a cycle of innovation. Today's quality per $ is the most expensive we'll see. Making it cheaper will lead to more people using, learning, and building with AI, which might unlock more potential and “goodput” for everyone than yet another Foundation Model! AI's real progress: Getting into more hands.🤗 [Image credits: @davidtsong]
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Wow. @Jandodev just showed me a prompt humans can’t read but LLMs understand this language better. The San Francisco AI people are designing a new language. In stealth. You are first to see it.
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Today we are releasing two small models: Mathstral 7B and Codestral Mamba 7B. On the MATH benchmark, Mathstral 7B obtains 56.6% pass@1, outperforming Minerva 540B by more than 20%. Mathstral scores 68.4% on MATH with majority voting@64, and 74.6% using a reward model. Codestral Mamba is one of the first open source models with a Mamba 2 architecture. It is the best 7B code model available, and is trained with a context length of 256k tokens. Both models are released under the Apache 2 license. mistral.ai/news/mathstral/ mistral.ai/news/codestral-ma…
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Zhensu Sun retweeted
"Code is there to explain the comments to the computer." — Andy Harris
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This is fast. Chrome running Gemini locally on my laptop. 2 lines of code.
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Want to turn your own GitHub Repos into a playground for 🤖 coding agents? 📢📢 Introducing R2E: Repository to Environment 📈 Scalable, dynamic, real-world repo-level benchmarks 💡 Generate Equivalence Tests Harnesses 🔗 r2e.dev | Accepted @ ICML '24 🧵
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#impact: @lminozem's MIP talk at @ICSEconf in Lisbon has inspired @davidlo2015 and Corina Pasareanu to include a slide about constructive reviews in the slide deck for the PC members of #ICSE2025.
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Welp
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We have just released 🍷 FineWeb: 15 trillion tokens of high quality web data. We filtered and deduplicated all CommonCrawl between 2013 and 2024. Models trained on FineWeb outperform RefinedWeb, C4, DolmaV1.6, The Pile and SlimPajama!
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🏆 Extremely happy to share that I received the Google Research Scholar award (research.google/programs-and…) this year with my proposal "Towards Faster and Greener LLM-assisted Programming"! I expect to share more results in this direction soon! 😊
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What a bigggg achievement! What’s the next award @lilicoding 😎? – at Lisbon, Portugal
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Zhensu Sun retweeted
Members and alumni of the Centre of Research on Intelligent Software (RISE), SCIS, @sgSMU at @ICSEconf - having a great time presenting and getting valuable inputs for our research work, as well as reconnecting with and learning from 2200+ colleagues from across the globe :)
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Zhensu Sun retweeted
My point here is not that I dislike "delve," though I do, but that it's a sign that text was written by ChatGPT.
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