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Modern software is incredibly complex. A typical application may comprisehundreds or thousands of reusable components. Automated package managers canhelp to maintain a consistent set of dependency versions, but ultimately thesolvers in these systems rely on constraints generated by humans. At scale,small errors add up, and it becomes increasingly difficult to findhigh-fidelity configurations. We cannot test all configurations, because thespace is combinatorial, so exhaustive exploration is infeasible.  In this paper, we present Reliabuild, an auto-tuning framework thatefficiently explores the build configuration space and learns which packageversions are likely to result in a successful configuration. We implement twomodels in Reliabuild to rank the different configurations and use adaptivesampling to select good configurations with fewer samples. We demonstrateReliabuild's effectiveness by evaluating 31,186 build configurations of 61packages from the Extreme-scale Scientific Software Stack(E4S). Reliabuildselects good configurations efficiently. For example, Reliabuild selects 3X thenumber of good configurations in comparison to random sampling for severalpackages including Abyss, Bolt, libnrm, OpenMPI. Our framework is also able toselect all the high-fidelity builds in half the number of samples required byrandom sampling for packages such as Chai, OpenMPI, py-petsc4py, and slepc. Wefurther use the model to learn statistics about the compatibility of differentpackages, which will enable package solvers to better select high-fidelitybuild configurations automatically.

Reliabuild: Searching for High-Fidelity Builds Using Active Learning