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The rising popularity of deep learning (DL) methods and techniques hasinvigorated interest in the topic of SE4DL, the application of softwareengineering (SE) practices on deep learning software. Despite the novelengineering challenges brought on by the data-driven and non-deterministicparadigm of DL software, little work has been invested into developingAI-targeted SE tools. On the other hand, tools tackling more generalengineering issues in DL are actively used and referred to under the umbrellaterm of ``MLOps tools''. Furthermore, the available literature supports theutility of conventional SE tooling in DL software development. Building uponprevious MSR research on tool usage in open-source software works, we identifyconventional and MLOps tools adopted in popular applied DL projects that usePython as the main programming language. About 70% of the GitHub repositoriesmined contained at least one conventional SE tool. Software configurationmanagement tools are the most adopted, while the opposite applies tomaintenance tools. Substantially fewer MLOps tools were in use, with only 9tools out of a sample of 80 used in at least one repository. The majority ofthem were open-source rather than proprietary. One of these tools, TensorBoard,was found to be adopted in about half of the repositories in our study.Consequently, the use of conventional SE tooling demonstrates its relevance toDL software. Further research is recommended on the adoption of MLOps toolingby open-source projects, focusing on the relevance of particular tool types,the development of required tools, as well as ways to promote the use ofalready available tools.

Software engineering for deep learning applications: usage of SWEng and MLops tools in GitHub repositories