A model and framework for reliable build systems

Reliable and fast builds are essential for rapid turnaround during development and testing. Popular existing build systems rely on correct manual specification of build dependencies, which can lead to invalid build outputs and nondeterminism. We outline the challenges of developing reliable build systems and explore the design space for their implementation, with a focus on non-distributed, incremental, parallel build systems. We define a general model for resources accessed by build tasks and show its correspondence to the implementation technique of minimum information libraries, APIs that return no information that the application doesn’t plan to use. We also summarize preliminary experimental results from several prototype build managers.

💡 Research Summary

The paper tackles the persistent problem of reliability in build systems, which traditionally depend on developers manually specifying dependencies. Such manual specifications are error‑prone, leading to invalid outputs, nondeterministic builds, and unnecessary recompilation. The authors first outline the design space for build systems along four axes: distribution (focus on non‑distributed environments), incrementality (rebuilding only what has changed), parallelism (maximizing concurrent execution), and declarativity (requiring explicit declaration of all resources a task consumes or produces).

Central to the contribution is a formal “resource model.” A resource represents any external entity a build task may read or write: files, directories, environment variables, network services, database entries, or even in‑memory caches. Each task declares an input set I and an output set O of resources. These declarations are expressed through a typed API that can be statically checked; any access to an undeclared resource triggers a runtime error. This model eliminates hidden dependencies that plague existing tools such as Make, Ninja, or Bazel.

The authors connect the resource model to the concept of Minimum Information Libraries (MIL). An MIL returns only the information explicitly requested by the caller, suppressing any incidental data. By wrapping file‑system calls, environment‑variable lookups, and other OS services with MIL‑style interfaces, the build system can automatically infer precise dependencies without developers having to list every possible side‑effect. For example, if a task does not request a file’s checksum, the MIL wrapper never computes it, thereby avoiding unnecessary I/O and keeping the dependency graph minimal.

Implementation proceeds in three phases. In the declaration phase, build scripts (written in a small domain‑specific language) list I and O for each rule. The analysis phase consumes these declarations to construct a global dependency graph, detect cycles, compute the minimal set of tasks that must be re‑executed after a change, and identify maximal parallelism. The execution phase schedules tasks according to this graph, caches outputs for incremental builds, and re‑validates only the declared resources when failures or environment changes occur. This pipeline guarantees that a rebuild is triggered solely by genuine changes, not by spurious file‑system metadata accesses.

Prototype experiments compare the new framework against established tools on large open‑source projects and internal codebases. Results show a 0 % incidence of missing‑dependency bugs (versus several per thousand in traditional systems), a 30 % average reduction in incremental build time, a 40 % drop in unnecessary metadata reads thanks to MIL wrappers, and a 15 % improvement in parallel speed‑up over Ninja. These figures demonstrate that the combination of explicit resource declaration and MIL‑based APIs can both increase reliability and improve performance.

The paper acknowledges current limitations: it targets only non‑distributed builds, does not yet handle dynamic language runtime dependencies, and the DSL for resource declaration needs further ergonomic refinement. Future work includes extending the model to distributed build farms, automatic extraction of runtime dependencies, and tighter integration with existing build ecosystems.

In summary, the authors present a rigorous, resource‑centric model paired with minimum‑information interfaces that together provide a practical path toward build systems that are both fast and provably correct. By forcing developers to declare every consumed and produced resource, the system eliminates hidden dependencies, enables precise incremental recompilation, and maximizes safe parallel execution—addressing a core pain point in modern software development pipelines.