Build systems are used in all but the smallest software projects to invoke the right build tools on the right files in the right order. A build system must satisfy two central properties. First, a build system must guarantee correct builds, which means that generated artifacts consistently reflect the latest source artifacts. Second, a build system must be efficient, which means that it incrementally rechecks and rebuilds as few build steps as possible.

Illustrating example

Consider the following build scenario for a Java preprocessor that uses ANTLR for parsing the non-preprocessed source code.

Checkout preprocessor source code from remote repository. Extract Java version targeted by the preprocessor from a configuration file. Build ANTLR parser for that Java version. Download ANTLR binary. Download ANTLR grammar for correct Java version. Run ANTLR binary to compile the ANTLR grammar into Java code. Compile Java code. Compile preprocessor source code against parser. Package preprocessor together with parser into JAR file.

There are many source artifacts involved in this build, such as the code in the remote repository, the code in the local working copy, the configuration file, the ANTLR binary, or the ANTLR grammar. Correctness requires that the build system rebuilds the generated artifacts whenever any one of these source artifacts changes. Efficiency requires that the build system reexecutes as few build steps as possible. For example, if the code in the remote repository changes but the Java version in the configuration file remains unchanged, the build system should only execute steps 1, 2, 4, and 5. Note that the configuration file may require non-trivial parsing to determine the targeted Java version.

Dynamic dependencies needed!

Almost all existing build systems fail on this example and either provide inefficient or incorrect building. The reason is that it is only possible to determine whether to execute step 3 after executing step 1 and 2, that is, after fetching the configuration file and extracting the version number. Thus, the build script has a dynamic dependency on step 3. This is in contrast to static dependencies that are hard-coded in the build script. Existing build systems only support static dependencies, which they use to construct schedule the execution of build steps. Since the build schedule cannot change during build execution, existing build systems either inefficiently execute step 3 even when the Java version does not change or incorrectly only execute step 3 on the first run, reusing the parser even when the Java version changes. Dynamic dependencies allow us to decide after step 2 finished for which version of Java step 3 should build a parser.

Dynamic dependencies are not only necessary for the example scenario above, it is also easier to derive precise dynamic dependencies than it is to approximate static dependencies. For example, when compiling the preprocessor source code in step 4 above, the code repository may contain source files that are not used by the preprocessor implementation. With static dependencies, we have to make the required source files explicit in the build script or apply a static dependency analyzer to generate this information before the build execution starts. With dynamic dependencies, we can simply start the Java compiler and inspect its verbose output to determine the set of required source files. In practice, dynamic dependencies make precise dependency analysis a lot easier and yield less overapproximation (less superfluous dependencies) and less underapproximation (less missing dependencies).

The pluto build system

We have developed a new build system called pluto that supports dynamic build dependencies. In pluto, each build step consists of a build function that can declare dependencies on files and other build steps during its execution through a simple API. When a build step finishes, pluto stores the dependency list of the build step to detect changes of required artifacts in subsequent runs. However, in contrast to other build systems, pluto combines change detection and the execution of build steps into a single traversal of the dependency graph. This traversal only initiates the reexecution of those build steps that (i) are still required and (ii) depend on a file that was changed since the previous build. We have formally verified the correctness and optimality of pluto’s incremental rebuilding algorithm.

We have developed pluto as a Java API where users can define build steps by implementing a simple Java interface. For illustration, the script of build step 3 of our example is available online for inspection. Using a full-fledged programming language like Java for build scripts has various positive design implications. First and maybe most importantly, Java makes it possible to debug a build script using any Java IDE. Second, build-script authors can use all features of Java and are not bound to a low-level language such as Bash. Third, our API makes it easy to parameterize build steps over run-time Java values. Parameterized build steps can often be reused across build scripts. Fourth, the type system of Java provides a statically typed interface for using build steps and ensures that build steps are invoked on well-typed arguments only.

In summary, pluto provides the following features:

Dynamic dependencies on files and other build steps.

A Java API for debuggable and reusable build steps.

Dependency injection into build steps to avoid hard-coded dependencies.

Semantic file stamps for avoiding rebuilds after irrelevant file changes.

Automatic metadependencies that ensure a rebuild after the build script changes.

Detection of build cycles and customizable cycle handling.

pluto is fast

We have used pluto to develop build scripts for a few projects so far. In particular, we have migrated the existing Ant build script of the Spoofax IDE to pluto. Using pluto, we achieved speedups of 4x-50x for rebuilding after a typical program change. The speedup depends on how many downstream build steps a file change invalidates. The fine-grained dependency tracking of pluto also revealed hidden dependencies in the original build script that led to incorrect rebuilds. Pluto automatically detects such hidden dependencies and guarantees correct rebuilds in all cases.

We have used pluto to develop numerous reusable build steps that perform fine-grained dependency tracking out of the box. The provided build steps range from Java compilation and JUnit testing to dependency resolution via git and Maven. Our experience shows that it is easy to develop new build steps and to make them reusable.

Resources on pluto