AutoBuilder: Automated Build Systems
- AutoBuilder is a class of systems that automatically transform high-level specifications and metadata into validated, executable artifacts across diverse domains such as software, engineering, and robotics.
- These systems integrate domain-specific constraints and closed-loop validation to ensure that outputs meet not only syntax but also functional and semantic requirements.
- Applications of AutoBuilder span automated code builds, structural model generation, incremental compilation, and neural architecture design, enabling efficient and adaptive construction pipelines.
Searching arXiv for papers on “AutoBuilder” and closely related build-automation systems. Searching arXiv for “automatic software build methods” and repository-level build/compile agents. AutoBuilder denotes a class of automated construction systems that appear across recent research in software engineering, computational engineering, machine learning, and robotics. The term is used for systems that transform high-level specifications, repository state, partial observations, or ecosystem metadata into executable artifacts such as software builds, simulation-ready structural models, verifiable repository environments, reusable feature hierarchies, trained models, or physically assembled objects. Across these uses, a recurring requirement is that outputs are not merely syntactically plausible: they are validated by execution, testing, simulation, or physical task completion under domain constraints (Jiang et al., 6 Feb 2026, Huang et al., 6 Jul 2026, Zhang et al., 15 Apr 2026).
1. Terminological scope and recurring architecture
In software engineering, the classical substrate for an AutoBuilder is a build description that automates a sequence of repetitive tasks operating on source code, libraries, database files, and related artifacts; the same description can be invoked manually in a levered build or automatically in continual build settings such as scheduled or triggered continuous integration, including polled and hooked variants (Kawalerowicz, 2013). In more recent agentic systems, the term expands from orchestration of build scripts to autonomous construction of environments, code, models, and verification loops.
| Domain | System name | Constructed artifact |
|---|---|---|
| Structural engineering | AutoBM | OpenSeesPy structural model code |
| Repository-level SWE | AutoBuilder | Sandboxsed executable repository environments |
| Maven supply chain | Macaron extension with CommitFinder | Reproducible Central build specifications |
| Reusable artifact management | FTBUILDER | Multi-level feature tree |
| AI model development | AIBuildAI | Trained models and runnable inference script |
| Neural architecture design | AutoBuild | High-scoring architecture modules and reduced search spaces |
The literature suggests that AutoBuilder systems share a common decomposition. First, they ingest a partially structured specification such as natural-language task descriptions, repository metadata, BIM geometry, or a small set of evaluated architectures. Second, they construct an internal representation amenable to algorithmic reasoning, for example a build script, a graph, a reward-annotated sandbox environment, or a hierarchical feature tree. Third, they apply closed-loop validation: Pass@k-style execution metrics for structural models, fail-to-pass and pass-to-pass tests for repositories, rebuild success for supply-chain artifacts, or downstream task metrics such as FID, PQ, or medal rate (Jiang et al., 6 Feb 2026, Huang et al., 6 Jul 2026, Hassanshahi et al., 10 Sep 2025, Mills et al., 2024).
2. Physics-consistent and simulation-executable structural builders
In structural engineering, AutoBuilder is instantiated by Automatic Building Modeling (AutoBM), where the input specification is formalized as and the output structural model as , with denoting textual task description, physical and engineering constraints, and modeling APIs and environment. The central requirement is not only executability of generated OpenSeesPy code but also physical consistency, API compliance, and semantic match to the specification (Jiang et al., 6 Feb 2026). The paper explicitly argues that LLM-generated modeling code must be evaluated under simulation and physics constraints rather than syntax alone.
The proposed framework combines domain knowledge construction, constraint-oriented model alignment, and verification-driven evaluation. CivilInstruct provides four types of supervision: fine-grained supervised data of about 3,881 samples, parameterized long code for about 3,100 structural instances, execution error-oriented Bug-CoT of about 3,500 samples, and 512 physics-informed expert samples with ground-truth fundamental period (Jiang et al., 6 Feb 2026). Constraint-oriented alignment uses supervised fine-tuning followed by RLA-SPC, a physics-constrained RL stage whose Multi-Granularity Hybrid Reward emphasizes execution and physical validity with weights , , and . Physical reward is driven by the normalized period error
Evaluation is benchmarked by BMBench/BMEval on 128 building cases with ground-truth periods and compliance status. The benchmark defines Pass@k, Pass@k, Pass@k0, and Pass@k1, where the strict metric requires execution success, period error within threshold, and an explicit compliance conclusion (Jiang et al., 6 Feb 2026). Quantitatively, generic code models exhibit a large gap between executability and engineering-grade correctness: Claude-Sonnet-4.5 reaches Pass@5 up to about 79.34% but Pass@52, while GPT-5 reaches Pass@53. After two-stage alignment, open models improve sharply: CodeLlama-7B-Instruct rises from Pass@54 to 67.73%, Qwen2.5-Coder-7B from 0.78% to 71.82%, and Seed-Coder-8B-R from 0.78% to 77.14% (Jiang et al., 6 Feb 2026). The framework is nevertheless scoped primarily to low- to mid-rise 2D reinforced-concrete frame structures and to OpenSeesPy-centered workflows.
3. Software build theory, incremental execution, and configuration search
In the software-build literature, AutoBuilder begins with the classification of build automation itself. Build methods are organized into levered build, also called commanded integration, and continual build, i.e. continuous integration. Continual build further divides into scheduled builds, described as transitional CI, and triggered builds, described as strict CI, with triggered systems implemented as either polled or hooked integration (Kawalerowicz, 2013). This taxonomy matters because AutoBuilder is not only a build script generator; it is also a trigger, orchestration, and feedback system that determines when and under what repository conditions build logic should run.
A second strand removes the need to hand-maintain dependency specifications for incremental builds. LaForge, whose system description in the provided data is given as Riker, traces execution of a simple full-build script and compiles it into TraceIR, a replayable IR over file accesses, metadata checks, content checks, process launches, and updates. On later runs, it re-evaluates the transcript against an in-memory filesystem model and re-executes only commands whose dependencies changed, while claiming always-correct incremental behavior (Curtsinger et al., 2021). Across 14 software packages, full builds incur a median overhead of 16.1% relative to default full builds, no-op incremental builds have a median time of 218 ms, and most incremental builds take less than 3.08s longer than manually specified incremental builds (Curtsinger et al., 2021). The significance is architectural: a full build script becomes the specification, and dependency discovery is transferred from the user to the tracing engine.
A third strand treats build selection itself as a learning problem over combinatorial configuration spaces. Reliabuild, also described as SHiFU in the provided material, models a build configuration as a vector of dependency versions and uses active learning with either a wisdom-of-the-crowd model or a Bayesian factorized model over the dependency graph to rank configurations by likelihood of success (Menon et al., 2022). The evaluation covers 31,186 build configurations of 61 packages from the E4S stack. The framework selects 3X the number of good configurations in comparison to random sampling for packages including Abyss, Bolt, libnrm, and OpenMPI, and selects all the high-fidelity builds in half the number of samples required by random sampling for Chai, OpenMPI, py-petsc4py, and slepc (Menon et al., 2022). This positions AutoBuilder not merely as execution infrastructure but as an adaptive search process over feasible build states.
4. Repository compilation agents and environment reconstruction
Recent agentic AutoBuilder systems operate directly on real repositories. In KAT-Coder-V2.5, AutoBuilder is the environment-scaling component that reconstructs multilingual repositories into sandboxed, executable environments and verifiable tasks. It operates as a sandboxed build–verification loop with a build agent that generates configuration scripts and a verification agent that executes them in isolation and validates the result through structured test-framework outputs rather than exit codes alone (Huang et al., 6 Jul 2026). Environments are accepted only if more than 90% of the expected tests are collected and pass/fail outcomes are reproducible across runs. The reported environment-construction success rate rises from 16.5% to 57.2%, yielding over 100,000 verifiable environments across 12 languages, while the reliability-hardened sandbox reduces feedback error rate from roughly 16% to below 2% (Huang et al., 6 Jul 2026).
CXXCrafter addresses a narrower but operationally difficult setting: automated C/C++ project building on generic Linux. Its empirical study on 100 highly starred C/C++ projects finds that default build commands succeed on only 21/100 projects, while four experienced developers spent about 153 person-hours and encountered 384 distinct build errors across 79 projects, averaging about 4.9 errors per project (Yu et al., 27 May 2025). Library issues account for 284 of these errors, build toolchain issues for 64, and configuration issues for 30. CXXCrafter then combines a parser module, a Dockerfile generator module, and an executor module with LLM-based discrimination for non-obvious failures, achieving a 78% project-building success rate overall and, on the Top100 dataset, 72 projects built successfully by both CXXCrafter and manual efforts, 3 by CXXCrafter only, and 14 manually only (Yu et al., 27 May 2025).
BuildBench tightens evaluation further by shifting from curated high-star repositories to a statistically grounded sample of the long tail. From an initial sample of 385 repositories, manual curation yields 148 compilable repositories as the final test set; among 130 of these, ground-truth URLs containing build instructions are annotated (Zhang et al., 27 Sep 2025). Success is evaluated against human-labeled expected binaries under strict and flexible criteria. Rule-based baselines remain weak, with GHCC at 10.1% strict success and 13.4% flexible success, while the best OSS-Build-Agent configuration—LLM-Assisted Retrieval plus Claude 3.7-Sonnet—reaches 66.4% strict success and 71.8% flexible success (Zhang et al., 27 Sep 2025). Retrieval quality is itself a major determinant: OSS-Build-Agent’s LLM retrieval reaches 73.8% accuracy on the labeled instruction URLs, compared with 46.2% for CompileAgent’s retrieval module (Zhang et al., 27 Sep 2025). A plausible implication is that repository AutoBuilders are increasingly bottlenecked by documentation discovery and environment synthesis rather than by local command generation alone.
5. Rebuilding source ecosystems and constructing reusable artifact hierarchies
In supply-chain security, AutoBuilder takes the form of automated rebuild from source. The Macaron extension described in "Unlocking Reproducibility: Automating re-Build Process for Open-Source Software" maps a Maven artifact identifier to source repository, commit, workflow-derived build command, and a Reproducible Central build specification (Hassanshahi et al., 10 Sep 2025). The motivation is that approximately 84% of the top 1200 commonly used artifacts are not built using a transparent CI/CD pipeline, and only 2 of 1198 analyzed artifacts expose SLSA provenance metadata (Hassanshahi et al., 10 Sep 2025). CommitFinder improves source detection over AROMA from 234,674 to 273,811 matched artifacts, and the system successfully rebuilds 73 Maven packages aligned with AROMA while additionally rebuilding 19 Gradle packages in the Reproducible Central dataset (Hassanshahi et al., 10 Sep 2025). The framework thereby turns artifact identifiers into rebuildable, inspectable build specifications rather than opaque binaries.
A different form of ecosystem construction appears in FTBUILDER, which automatically builds a multi-level feature tree for reusable artifact management. It crawls Linux distribution repositories, consolidates metadata into a structured library of 237 group artifacts, clusters artifact descriptions, and uses GPT-4 to summarize common features recursively into a hierarchy (Jin et al., 4 Jun 2025). Among 24 alternative solutions, the best configuration—text-embedding-ada-002 with GMM and BIC—produces a three-level feature tree with higher quality than the official feature tree, improving the silhouette coefficient by 9% and GValue by 11% (Jin et al., 4 Jun 2025). It also reduces developer artifact-selection time by 26% and improves GPT-4 artifact recommendation accuracy by 235% (Jin et al., 4 Jun 2025). Although this is not a software build system in the classical sense, it matches the AutoBuilder pattern of turning unstructured repository ecosystems into structured, reusable, semantically navigable artifacts.
6. AI model, neural architecture, and instruction builders
AutoBuilder also appears as an end-to-end model-development agent. AIBuildAI accepts a natural-language task description and training data, then uses a hierarchical agent architecture in which a manager coordinates designer, coder, and tuner sub-agents, alongside setup and aggregation stages, to produce trained models and a runnable inference script (Zhang et al., 15 Apr 2026). Evaluated on MLE-Bench, which contains 75 Kaggle-style tasks across vision, NLP, temporal, and tabular settings, AIBuildAI attains a medal rate of 63.1%, ranking first overall, with 77.27% on low-complexity tasks, 61.40% on medium-complexity tasks, and 46.67% on high-complexity tasks (Zhang et al., 15 Apr 2026). The system positions AutoBuilder beyond hyperparameter optimization, extending it to task framing, data handling, code generation, tuning, and final ensembling.
AutoBuild, in neural architecture design, removes much of NAS’s search burden by learning a Magnitude-Ranked Embedding Space in which graph-embedding norms correlate with architecture performance (Mills et al., 2024). Architectures are represented as graphs, node and subgraph embeddings are produced by a GNN, and the loss combines MSE with a differentiable SRCC term: 5 This makes 6 interpretable as an importance score for the 7-hop rooted subgraph around node 8 (Mills et al., 2024). In ImageNet macro search spaces, AutoBuild uses only 3,000 evaluated architectures from spaces of about 9 and 0 candidates; in Stable Diffusion v1.4 inpainting it operates with only 68–90 labeled architectures from spaces up to about 1 (Mills et al., 2024). It then directly constructs high-quality architectures or prunes the search space. In the inpainting setting, the top 4 AutoBuild architectures achieve average FID 10.13 and best FID 9.96, outperforming the exhaustive-search predictor baseline at 10.82 average and 10.29 best (Mills et al., 2024).
The same externalization principle appears in visual assembly learning. In "Learning to Build by Building Your Own Instructions," the agent later called InstructioNet constructs a stack of self-generated instruction images during the Break phase and then rebuilds the LEGO assembly by following the stack in reverse (Walsman et al., 2024). On RC-Vehicles, whose procedurally generated LEGO vehicles contain an average of 31 bricks and require over one hundred steps to disassemble and reassemble, the method attains 2, 3, 4, and AED 5; on smaller RC-2, RC-4, and RC-8 settings it substantially outperforms LSTM and Studnet baselines (Walsman et al., 2024). This suggests that some AutoBuilder systems achieve scalability by converting long-horizon latent memory into explicit intermediate artifacts such as instruction books or ranked module libraries.
7. Embodied builders in robotics and construction
Embodied AutoBuilder systems close the loop not with tests or simulators alone, but with physical execution. In modular construction robotics, a holistic framework links BIM-based task specification, robot morphology optimization, and mission execution for in situ drilling (Külz et al., 2024). Goals are formalized as poses with tolerances, robot morphologies are searched with a mixed lexicographic–Pareto genetic algorithm over modular components, and the chosen design is executed with navigation, visual calibration, and online trajectory adjustment. In 100 optimization runs limited to 50 generations, average computation time is about 78 minutes, and real-world experiments with 5-DoF and 6-DoF morphologies show base-positioning errors between 6 cm and 16 cm and 0.7° to 12.6°, while the relative precision between drilled holes remains within 1 cm (Külz et al., 2024). Initial assembly of the 6-DoF setup takes about 10 minutes, and reconfiguration to the 5-DoF setup about 5 minutes.
A more fine-grained embodied AutoBuilder assembles a gearbox from randomly placed parts using a single arm with vision, GelSight tactile sensing, and a wrist F/T sensor. The system segments parts with Mask R-CNN, performs part singulation by sliding actions chosen by random shooting in MuJoCo, localizes pegs in-hand through tactile regression, inserts gears using an LSTM over F/T trajectories, and meshes gears by compliant rotation (Ota et al., 2024). Quantitatively, singulation on the real robot succeeds in 100/100 trials with an average of 3.26 interactions, tactile correction raises peg insertion from 3/20 without correction to 20/20 with 100 or 1000 training samples, gear insertion reaches 95% success over 100 trials, and the full gearbox assembly succeeds in 223/225 random initial conditions, i.e. 99.11% (Ota et al., 2024).
These embodied systems differ from software or model builders in substrate, but not in architecture. High-level goals are first converted into structured intermediate representations such as BIM goals, stage-wise plans, or singulation trajectories; then morphology, code, or motion is synthesized; finally, execution feedback—vision, tactile, force, calibration, or compliance outcome—drives correction. The literature therefore suggests that AutoBuilder is less a single tool than a recurring systems pattern: automated construction under domain-specific constraints, with verification promoted from an afterthought to the central organizing principle.