Git is a widely used version control system that is powerful but complicated. Its complexity may not be an inevitable consequence of its power but rather evidence of flaws in its design. To explore this hypothesis, we analyzed the design of Git using a theory that identifies concepts, purposes, and misfits. Some well-known difficulties with Git are described, and explained as misfits in which underlying concepts fail to meet their intended purpose. Based on this analysis, we designed a reworking of Git (called Gitless) that attempts to remedy these flaws.

In this article we present a novel technique for automated parallel bug-finding based on the sequential analysis tool TACO. TACO is a tool based on SAT-solving for efficient bug-finding in Java code with rich class invariants. It prunes the SAT-solver's search space by introducing precise symmetry-breaking predicates and bounding the relational semantics of Java class fields. The bounds computed by TACO generally include a substantial amount of nondeterminism; its reduction allows us to split the original analysis into disjoint subproblems. We discuss the soundness and completeness of the decomposition. Furthermore, we present experimental results showing that MUCHO-TACO, our tool which implements this technique, yields significant speed-ups over TACO on commodity cluster hardware.