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As technical computing software, such as MATLAB and SciPy, has gained popularity, ecosystems of interdependent software solutions and communities have formed around these technologies.The development and maintenance of these technical computing ecosystems requires expertise in both software engineering and the underlying technical domain. The inherently interdisciplinary nature of these ecosystems presents unique challenges and opportunities that shape software development practices.Proof assistants, a type of technical computing software, aid users in the creation of formal proofs. In order to examine the influence of the underlying technical domain --- mathematics --- on the development of proof assistant ecosystems, we mined participant activity data from the code repositories and social channels of three popular proof assistants: Lean, Coq, Isabelle. Despite having a shared technical domain, we found little cross-pollination between contributors to the proof assistants. Additionally, we found that most long-term developers focused solely on technical work and did not participate in official social channels. We also found that proof assistant developers specialized into technical subfields. However, the proportion of specialists varied between ecosystems. We did not find evidence that these specialties contributed to fractures within the ecosystems. We discuss the implications of these results on the long-term health and sustainability of proof assistant ecosystems.This artifact contains the scripts and dataset that support an MSR 2024 article. 

Dynamic taint tracking, a technique that traces relationships between values as a program executes, has been used to support a variety of software engineering tasks. Some taint tracking systems only consider data flows and ignore control flows. As a result, relationships between some values are not reflected by the analysis. Many applications of taint tracking either benefit from or rely on these relationships being traced, but past works have found that tracking control flows resulted in over-tainting, dramatically reducing the precision of the taint tracking system. In this article, we introduce Conflux , alternative semantics for propagating taint tags along control flows. Conflux aims to reduce over-tainting by decreasing the scope of control flows and providing a heuristic for reducing loop-related over-tainting. We created a Java implementation of Conflux and performed a case study exploring the effect of Conflux on a concrete application of taint tracking, automated debugging. In addition to this case study, we evaluated Conflux ’s accuracy using a novel benchmark consisting of popular, real-world programs. We compared Conflux against existing taint propagation policies, including a state-of-the-art approach for reducing control-flow-related over-tainting, finding that Conflux had the highest F1 score on 43 out of the 48 total tests.