  How to generate instances with relevant properties and without bias remains
an open problem of critical importance for a fair comparison of heuristics. In
the context of scheduling with precedence constraints, the instance consists of
a task graph that determines a partial order on task executions. To avoid
selecting instances among a set populated mainly with trivial ones, we rely on
properties that quantify the characteristics specific to difficult instances.
Among numerous identified such properties, the mass measures how much a task
graph can be decomposed into smaller ones. This property, together with an
in-depth analysis of existing random task graph generation methods, establishes
the sub-exponential generic time complexity of the studied problem. Empirical
observations on the impact of existing generation methods on scheduling
heuristics concludes our study.
