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Senior SWE-Bench: open-source benchmark that assesses agents as senior engineers
- jonathanleane 4mo agoTop solve rate is currently 24% with Opus 4.8... What's a competent human supposed to score?
- lacunary 4mo agopresumably whatever the top model uses and then some, since the human can use the model. I wonder if a model could score higher if it had a human at its disposal?
- pishpash 4mo agoMaybe models should ask for human-in-the-loop input, as a matter of convention.
- sinuhe69 4mo agoA model that can ask questions or ask for help when in doubt is indeed a major feat. None of the current frontier models can do that.
- olmo23 4mo agoWith a human at its disposal, it could probably count the number of R's in strawberry! In all seriousness though, adding capabilities should not normally reduce the effectiveness of a model (within reason: don't pollute the context window with millions of useless tools).
- jascha_eng 4mo agoI mean these were all solved before I assume so 100% not the same human ofc but models are expected to be good at a variety of code bases while human can specialize in one and learn. I think it's fair to compare to an individual that is used to working on a product. I'm more interested in how fable would do
- danpalmer 4mo agoWhy didn't they just make it "Staff SWE-Bench", would be much better smh. /s But seriously, as an industry we're terrible at assessing engineering levels, I've worked with "senior engineers" who can't code and I've worked with "junior engineers" who could run rings around them. Benchmarks like this should be much more precise about what they're actually testing, and what axes they're hard on. We also need to rise above prompts like "you are a senior engineer", it's woo, and it's far better to ask for precise outcomes.
- amrrs 4mo agoAs someone who's trying to get better assessments, I'm struggling to come up with objective coding tasks that evaluates all aspects of real life like planning, design choices, problem solving and context usage. From your experience with humans, Do you have any recommendations on what could be effective in measuring it?
- allan_s 4mo agoI think the source of your issue is in your statement itself, why do you want a task that evaluate things as broad to be only a coding task ? Shouldn't it be a planning task, documentation task, knowledge retrieval task etc. And very certainly not with just an initial prompt but an existing codebase + existing doc + tickets ?
- glaslong 4mo agoPrincipal-SWE-Bench will take some time to run, because the LLM needs to wait for a crisis to present its solution, having correctly identified that the same solution would have been organizationally impossible to propose until that moment.
- facorreia 4mo agoIt's nice to see a new public benchmark from Snorkel. They're doing some pretty sophisticated stuff over there.
- purple-leafy 4mo agoBenchmarks are great, but I feel like there’s a better way this seems quite subjective. What you really need is an objective benchmark
- echelon 4mo ago> What you really need is an objective benchmark "When are all the software engineers unemployed?"
- purple-leafy 4mo agoNot sure I follow haha
- eli 4mo agoI actually really like subjective benchmarks, so long as it's a human (ideally me) grading the results. LLM as judge never made much sense.
- charcircuit 4mo agoThe issue is that you can't do unsupervised learning if you require humans.
- rhdunn 4mo agoLLMs grading the answers is relying on the LLM knowing the answer and not just hallucinating it. You also have issues if/when the model refuses to answer, or if it gets stuck in a loop (e.g. if running locally with a heavily quantized model). I'm investigating/experimenting with using traditional NLP (stanza, spaCy, etc.) to try and grade the responses according to different metrics (is the response in first/second/third person?, is it written as poetry, prose, or drama? etc.). I'm also thinking about using information extraction and synonym detection to handle data queries and the like.
- charcircuit 4mo ago
- LiamPowell 4mo ago> You are a senior SWE-Bench reviewer, make no mistakes. I don't know what a better approach would look like while still remaining feasible, however this approach of telling a LLM to make a subjective judgement seems fundamentally flawed.
- FeepingCreature 4mo agoMore importantly, I suspect this actually hinders the work. If the LLM does make a mistake, it's now incentivized to downplay it instead of acknowledging and correcting.
- antonvs 4mo agoThe “make no mistakes” admonition does seem pretty silly (it’s been skewered to death on yt), but… it’s easy to imagine how it might work. E.g. it could be interpreted as simply as “check your work”. Of course, no-one seems to be (publicly) doing the comparative measurements that might allow us to reach rational conclusions here.
- rhdunn 4mo agoI'm not sure if they've fixed this, but older models have a tendency to ignore negation as `no`, `not`, etc. all occur frequently in the training data so are weighted less strongly than the verbs and nouns. The advice I've heard is to emphasize the traits you want, not discourage the traits you don't. So rather than saying "make no mistakes" you can do something like you suggested with writing it as "check your work" or "ensure you answer correctly and concisely".
- fsddfsdfssdf 4mo agoConversations in its training data that explicitly mentioned "make no mistakes" don't strike me as particularly rich sources of high-quality reasoning signals. They strike me as conversations with Pointy-haired Bosses.
- rhdunn 4mo agoThis approach is effectively seeding the context with how you want the LLM to behave/operate ("senior reviewer", i.e. the style of the responses you want) and the context/domain in which the LLM is operating in ("SWE-Bench"). This is common in system prompts and frames the responses. For example, you'd get different responses saying: 1. you are a pirate writing sea shanties about programming; 2. you are a news reporter writing an article on physics; 3. you are a senior software engineer with complete knowledge of PostgreSQL. For 1 you could get responses along the lines of the Wellerman sea shanty -- "There once was a program that was set to C ...". The "make no mistakes" bit does look dubious. It would be interesting comparing the results with and without that bit and trying alternative ways of getting the same desired behavior.
- jocelyner 4mo ago[flagged]
- Madmallard 4mo ago[flagged]
- dozerly 4mo agoJust wait for the next 100 rounds. People love seeing the 65% -> 85% seemingly over and over again for every new model.
- dang 4mo agoWould you please stop posting like this? You've been doing it repeatedly, and it degrades the threads. In fact the majority of your recent comments have been this sort of shallow, dimissive, snarky stuff. That is not what this site is for, and destroys what it is for. If you want to express your substantive points thoughtfully, that of course would be fine. If you'd please review https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html and stick to the rules when posting here, we'd appreciate it.
- Madmallard 3mo agoUnfortunately I'm telling the truth. You guys have a serious quality control problem for both the articles and the incentivized comment sections. It's all political. Millions of dollars to be on the front page of HN. It all breaks your post rules way more than my comments do. But this is low hanging fruit so you go for this.
- guilhermecgs 4mo agofable 5?
- deleted 4mo ago[deleted]
- 0xbadcafebee 4mo agoThe "tasteful solves" is codified cargo culting. The software industry has a tendency to anthropomorphize software while playing to the ego of the programmer. The programmer imagines they are creating a "beautiful" artistic expression. Good code becomes "tasteful", as a software artist must have "good taste" to tell the good software from the bad software. Good quality lacks "bad smells", because a good artist has fine senses (and everybody must like the same smells). "Fine craftsmanship", in code as in woodworking, means your finely-crafted work is "technically superior", so you can charge more money for something that could've been made cheaper and faster and done the same thing. But it's a lie. Nobody's paying you to make paintings. They're paying you to build machines. The comparison between "making working software" with "taste" always devolves into bikeshedding and subjective opinionism, uses subjective human feelings to describe what should be objective and functional, isn't rooted in scientific rigor, and detracts from the real purpose of the thing. The work doesn't actually get better by trying to apply artistic principles to engineering. It just feels better for the people making it. Once you make the machine work, then you can go about gilding the lily. But this is unromantic, unsatisfying, boring. Since the inmates run this particular asylum, we end up with a benchmark that tries to accurately mimic the human ego as applied to software design. Thus the new Gods create their digital Adams and Eves in their image.
- Dban1 4mo agoAs time passes we will have fewer and fewer literati
- Eridrus 4mo agoMost engineers are wrong (I obviously am the true arbiter of taste), but that doesn't mean there isn't better and worse code. "Does it work" glosses over a bunch of things: is it fast, cheap, secure, reliable, easy to understand, easy to modify? And that's just for server software where you've nailed down all the functional requirements. Determining what the functional requirements is it's own question. And all these other non-happy path requirements are somewhat in tension with each other, so what is ideal in one environment is not necessarily ideal in another. And in particular, "easy to understand/modify" is truly subjective. Different people have different ideas of what easy to understand means. Even if we get to a world where AI is writing all our code, "easy to understand/modify for the AI" is still an important question. We've probably all seen prototypes that collapse under their own weight of slop by now.
- magnio 4mo agoI saw on Twitter that in an ML course at Tsinghua University, one of the tests asks students to write quizzes that fail the most LLM models as possible. What if we create a benchmark that works like this and assigns ELO scores? Models fight head-to-head by writing a question, a bug, or an incomplete implementation, which the opponent has to answer, fix, or finish.
- vincnetas 4mo agoWe could call this "generative adversarial network" (GAN) :) https://en.wikipedia.org/wiki/Generative_adversarial_network https://en.wikipedia.org/wiki/Generative_adversarial_network
- wwind123 4mo agoThis kind of approach would generally still need human guidance, otherwise these models might get stuck in weird niche corners of the problem space that would not be relevant to any real world project.
- ben_w 4mo agoWe could call this "reinforcement learning from human feedback" (RLHF) :) https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback https://en.wikipedia.org/wiki/Reinforcement_learning_from_hu...
- olmo23 4mo agoHow do you prevent degenerate strategies? I could trivially give a model a SHA256 hash and ask it to provide the source input. In class you'd probably want a rule saying at least one LLM should be able to figure out the answer, but in a head-to-head I'm not sure how to solve it.
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- wwind123 4mo ago
- _345 4mo agoThis makes so much sense as to why I've always felt that Opus 4.8 was leagues ahead of GPT 5.5. It's so good at taking underspecified requirements and filling in the gaps with sensible approaches for your project
- re-thc 4mo ago> It's so good at taking underspecified requirements and filling in the gaps with sensible approaches for your project. At a high level. It misses low level or other non-functional requirements differently so I wouldn't say Opus is just strictly better. It's also possible that it's just a harness problem more than model.
- e9 4mo agoI agree with you on the harness. I find that Claude can be good in any harness but GPT is only superior inside Codex.
- nsingh2 4mo agoWhy supply underspecified requirements in the first place? Both models are good at challenging assumptions/edge cases and asking questions to clarify, but seemingly only when explicitly asked (i.e. something like a "brainstorm" skill). I don't think either harnesses do enough to encourage the model to challenge all assumptions and ask questions, maybe because users might find it annoying. That step is basically a requirement IMO. I've found all of the GPT-5 models to be very nit-picky, useful for code review and mathematics (important for my work), but seemingly gets in the way of "aesthetic" code, e.g. overly defensive code to cover all edge cases, even if unlikely. There is seemingly also a tradeoff between flexibility vs instruction following. In my experience Opus will sometimes ignore instructions but can "fill in the blanks" more, vs GPT-5.5 follows instructions better but perhaps at the cost of rigidity.
- antonvs 4mo ago> Why supply underspecified requirements in the first place? Minimizes effort, is the obvious answer.
- monster_truck 4mo agoOnce again I am asking: who are these people and what makes them more qualified than any of you to asses anyone or anything "as a senior engineer" (with the subtext being that none of you are, either)
- re-thc 4mo ago> who are these people and what makes them more qualified than any of you Anyone can run something and make a web page. These people just do it instead of questioning. Main difference. If everyone asks "how could you" "are you qualified" then we have nothing but gatekeeping.
- funnywish 4mo ago[flagged]
- fiso64 4mo agoI think benchmarks like this are too subjective and narrow to be useful. For example, whether a patch "bloats" the codebase really depends on the situation: If it's building a feature that will grow in the future, or refactoring code that has a long history of bugs, then a larger patch might in fact be good. It's not clear from the blog just how much context the LLM judge receives about the long term project goals and history. Benchmarks should be focused on evaluating the final result only. Maybe ask the coder to build a full app, or implement many new large features for an existing app in sequence, with a larger set of requirements, or have another LLM roleplay as the human to make the instructions a little more underspecified. When done, ask a reviewer harness to test the product for 5 hours, not the code. Count the number of bugs and weigh them by severity. "Taste" would then become an automatic consequence of correctness. (Full disclosure, I'm not a software engineer.)
- iLoveOncall 4mo ago> Full disclosure, I'm not a software engineer Then maybe you should abstain, because your comment is a complete load of nonsense. Bad code is bad code regardless of the history or scope of the feature. Maintainability is important because you can never know if a feature will be built upon in the future or not. Bloat is bad regardless, because it increases the overall complexity of the whole software development lifecycle, for the whole team, forever (or until refactored out): It's harder to keep track of the code and how it works to write new requirements, it's harder to write, it's harder to read and review, it's harder to debug, etc. You can write extremely poor code that has no bugs, it doesn't make it tasteful. This is simply a ridiculous statement.
- fiso64 4mo ago>Maintainability is important because you can never know if a feature will be built upon in the future or not. Of course maintainability is important. It's almost like saying good code is important (duh). The issue is that what is or isn't maintainable depends on the problem at hand. Sometimes you need to build heavier abstractions or refactor existing code when implementing a feature because it will pay off later. Other times, that exact same approach is horrible over-engineering because a simple, direct fix was all that was needed, so in fact you introduced a maintenance burden. You cannot reliably decide whether a patch is "bloated" or "tasteful" when looking at a diff without knowing where the project is headed. >You can write extremely poor code that has no bugs, it doesn't make it tasteful. You can, but it becomes increasingly hard to do so as you try to add features and maintain it. Taste, whatever that is, should ultimately lead to a measurable increase in the quality of the final product; if it doesn't, then your definition of "taste" is irrelevant. What I'm proposing is to skip trying to measure this ill-defined concept and only assess the quality of the final product, after the agent spent a significant amount of time working on it, and a reviewer spent a significant amount of time testing it. Agents should be assessed on their ability to build entire projects (e.g., many large features or even an entire app), not just a single feature. If an agent has no taste, then its bad decisions will compound and result in it stalling, or its output having more bugs and performing worse, given a sufficiently large scope.
- 21asdffdsa12 4mo agoThe value of a senior situation is to apply known solutions and strategies, to novel problems. I can not see how any benchmark, without ever changing, can provide a novel challenge for long. Any decent benchmark would use the whole of TRIZ to generate a giant ball of a problem first and watch a AI deduce a optimal solution.
- kerlenton 4mo ago[flagged]
- dang 4mo agoCan you please not post AI-generated or AI-edited comments to HN? It's not allowed here - see https://news.ycombinator.com/newsguidelines.html#generated https://news.ycombinator.com/newsguidelines.html#generated and https://news.ycombinator.com/item?id=47340079 https://news.ycombinator.com/item?id=47340079. Of course, it's impossible to know for sure what was LLM processed or not, but some of your posts (like this one) are getting classified that way.
- piterrro 4mo ago> Senior engineers build features without over-specified requirements To me this already disqualifies the benchmark. That statement is missing the most critical piece about senior engineers: the senior engineers know how to obtain input for their work on their own whether that talking to customers or using metrics. Never ever they come up with stuff on their own - that’s junior behaviour. Until a coding agent will be able to *gather* the input on its own, its never going to be „senior”
- jghn 4mo agoI'd take this a step further, but that step also curls back to the other side a small bit. The real skill is being able to both pull the necessary information from these sources as well as being able to intuit gaps in that knowledge based on their understanding of the business and their domain expertise & wisdom. Sometimes you can't get a perfect picture, sometimes the people who should know aren't able to tell you what they really need. You still need to do the right thing. A benchmark like this can potentially do the second part. But I don't think any model would be good at it, for now.
- tangweigang 4mo ago[flagged]
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- adrianwitaszak 4mo ago[flagged]
- HarHarVeryFunny 4mo agoSounds more like vibe-bench. For any professional work you care about the details. Even for hobby work, if you are using LLMs then presumably it is to do the drudge work of coding, not making the decisions, and that goes doubly so if you are a senior developer. Sure the LLM can "fill in the details" and vibe code (or attempt to) you a compiler or whatever, but the whole reason you are doing a hobby project is presumably because you want to bring your experience to bear and build a GOOD compiler, not a generic one.
- impartshadow 4mo ago[flagged]
- jeffbee 4mo agoStaff SWE Bench: LLM doubts whether we should do any of this, calls the entire project into question, refuses to merge code, but is happy to delete it.
- organsnyder 4mo agoPrincipal version: similar, but also says the only acceptable approach is to do it like they did it at their last company.
- pgwhalen 4mo agoDistinguished version: write the outline of the slide deck for the talk you plan to give at conferences about it, without having shipped anything or even written code yet.
- fsddfsdfssdf 4mo agoYou jest but I indeed find rejection an integral part of the job. Not plainly saying "no, get away", but backing up, requesting big picture views and trying to see if the overall organization is in need of and capable of carrying said project long-term feels like the absolute minimum that needs doing before we even begin. I suspect LLMs can do this just fine and probably better than us, but they do need to be trained specifically for it and I have a hard time coming up with good sources of training data for it.
- bloody-crow 4mo agoIsn't being open source creating incentives for the AI companies to optimize their LLMs for the specific benchmark? I thought all those benchmarks are deliberately closed source primarily for this reason.
- jfim 4mo agoI wonder how they're planning for the benchmark to stay relevant over time. If the benchmark is to implement features that are part of an open source project, and LLMs have those changes as part of their training dataset, it seems that they could just give a verbatim or slightly modified version of the change in their training data. And if one updates the benchmark to only incorporate code changes that are past the models knowledge cutoff, then the benchmark is less comparable over time, since the changes in the benchmark at time T and T+1 aren't the same.
- HaloZero 4mo agoHow far back in time do you need to give though? I imagine it will be limited by the time to think of new problems. Still seems valuable.
- apitman 4mo agoIf something like this works wouldn't that imply technical interviews can be automated?
- guptadagger 4mo agocan you prove that senior engineers can pass this benchmark?
- noashavit 4mo agoInteresting how close sonnet 5 comes to opus 4.8
- ltononro 4mo agoUsing LLMs to judge this could be very hackable, shouldnt it be? Is that the best practice we expect? Would be very interested in see some ablations about failure modes where the LLMs tried to hack it somehow. Or failures from the llmaaj