Manage the evolution in Software Product Lines (SPL) can bring some benefits such as keep the trace ability between assets in core assets and products, avoid some irregular growth or decrease before it becomes a threat to the system, and also use the products feedback to improve the core asset quality. In order to understand the evolution in SPL, this paper presents an empirical study to investigate evidence between information from features non-conformities and data from corrective maintenance, based on an SPL industrial project in the medical domain. The investigation aims at tracking the features non-conformities and their likely root causes using results from two preliminary studies. The first one captured and classified the features non-conformities from features specification of nine sub-domains and the second one investigated the evolution of SPL assets along the sub-domains development. The study sample was analyzed using statistical techniques, such as Spearman correlation rank and Poisson regression models. The findings indicated that there is significant positive correlation between feature non-conformities and corrective maintenance. Sub-domains with a high number of feature non-conformities had a higher number of corrective maintenance. Moreover, sub-domains qualified as high risk have also positive correlation with corrective maintenance. This correlation allows the building of predictive models to estimate corrective maintenance based on the risk sub-domain attribute values.
展开▼
