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While some areas of software engineering knowledge present great advances with respect to the automation of processes, tools, and practices, areas such as software maintenance have scarcely been addressed by either industry or academia, thus delegating the solution of technical tasks or human capital to manual or semiautomatic forms. In this context, machine learning (ML) techniques play an important role when it comes to improving maintenance processes and automation practices that can accelerate delegated but highly critical stages when the software launches. The aim of this article is to gain a global understanding of the state of ML-based software maintenance by using the compilation, classification, and analysis of a set of studies related to the topic. The study was conducted by applying a systematic mapping study protocol, which was characterized by the use of a set of stages that strengthen its replicability. The review identified a total of 3776 research articles that were subjected to four filtering stages, ultimately selecting 81 articles that were analyzed thematically. The results reveal an abundance of proposals that use neural networks applied to preventive maintenance and case studies that incorporate ML in subjects of maintenance management and management of the people who carry out these tasks. In the same way, a significant number of studies lack the minimum characteristics of replicability. [ABSTRACT FROM AUTHOR]

The establishment of associations between bivalents from Mus domesticus 2n=40 spermatocytes is a common phenomenon that shows up during the first prophase of meiotic nuclei. In each nucleus, a seemingly random display of variable size clusters of bivalents in association is observed. These associations originate a particular nuclear architecture and determine the probability of encounters between chromosome domains. Hence, the type of randomness in associations between bivalents has nontrivial consequences. We explore different models for randomness and the associated bivalent probability distributions and find that a simple model based on randomly coloring a subset of vertices of a 6-regular graph provides best agreement with microspreads observations. The notion of randomness is thereby explained in conjunction with the underlying local geometry of the nuclear envelope. [ABSTRACT FROM AUTHOR]