Guest editorial: special issue on predictive models for software quality

Leandro L. Minku, Ayşe B. Bener, Burak Turhan
2018 Software quality journal  
Software systems are increasingly large and complex, making activities related to ensuring software quality increasingly difficult. In this context, techniques able to automatically retrieve knowledge from software data in order to improve software quality are highly desirable. 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
more » ... n 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 greatly benefit the software quality community, given that it will improve the external validity of studies and provide insights into how to improve algorithms further. Following an open call for papers, the special issue received a total of 11 submissions, one of which was withdrawn. The remaining 10 submissions were peer-reviewed by experts in the field. At the end, four papers were selected for inclusion in this special issue:
doi:10.1007/s11219-018-9409-7 fatcat:253ddzlnl5dttdnprkkw743yci