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In the last few years, the Machine Learning (ML) and Artificial Intelligencecommunity has developed an increasing interest in Software Engineering (SE) forML Systems leading to a proliferation of best practices, rules, and guidelinesaiming at improving the quality of the software of ML Systems. However,understanding their impact on the overall quality has received less attention.Practices are usually presented in a prescriptive manner, without an explicitconnection to their overall contribution to software quality. Based on theobservation that different practices influence different aspects ofsoftware-quality and that one single quality aspect might be addressed byseveral practices we propose a framework to analyse sets of best practices withfocus on quality impact and prioritization of their implementation. We firstintroduce a hierarchical Software Quality Model (SQM) specifically tailored forML Systems. Relying on expert knowledge, the connection between individualpractices and software quality aspects is explicitly elicited for a large setof well-established practices. Applying set-function optimization techniques wecan answer questions such as what is the set of practices that maximizes SQMcoverage, what are the most important ones, which practices should beimplemented in order to improve specific quality aspects, among others. Weillustrate the usage of our framework by analyzing well-known sets ofpractices.

Best Practices for Machine Learning Systems: An Industrial Framework for Analysis and Optimization