Software Qual J (2018) 26:521–523 https://doi.org/10.1007/s11219-018-9409-7 Guest editorial: special issue on predictive models for software quality 1 2 3 Leandro L. Minku · Ays¸e B. Bener · Burak Turhan Published online: 16 April 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Software systems are increasingly large and complex, making activities related to ensur- ing software quality increasingly difficult. In this context, techniques able to automatically retrieve knowledge from software data in order to improve software quality are highly desir- able. Predictive modelling has been showing promising results in this area. For instance, it can be used to learn the relationship between features retrieved from software processes, software usage or software itself and certain properties of interest, e.g., the presence of bugs, the likelihood of changes leading to crashes and the presence of code smells. Such knowledge can be particularly useful to improve the quality of large and complex systems. With this in mind, this special issue aims at investigating predictive models for software quality. We solicited submissions that provide an in depth understanding of when, why and how algorithms to create predictive models work in the context of software quality. We believe that such understanding will
Software Quality Journal – Springer Journals
Published: Apr 16, 2018
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