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Data Science: Technologies for Better Software

: Ebert, Christof; Heidrich, Jens; Martinez-Fernandez, Silverio; Trendowicz, Adam


IEEE Software 36 (2019), No.6, pp.66-72
ISSN: 0740-7459
ISSN: 1937-4194
Journal Article
Fraunhofer IESE ()
Software tools; Data science; Software measurement; Complexity theory; Analytical models

Data science is mandatory in today's business to capitalize on achievements and assets. This specifically holds for modern software development, where data science facilitates analyzing product, process, and usage and thus managing evolution and performance. With the convergence of embedded and IT domains, such as the Internet of Things (IoT) and automotive systems, software systems are becoming more complex. Complexity has two faces. On one hand it means more functionality and fluid delivery models, thus offering markets more value, such as the ability to deliver a single-customer focus. Complexity, however, also means the growth of technical debt, which slows productivity and lowers quality. As software engineering generates ever larger and more varied data sets, such as feature usage, code analysis, test coverage, error logs, and maintenance data, companies face the challenge of unlocking the value of that data.