Software product-line engineering aims at developing families of related products, which share common assets, to provide customers with tailor-made products. Customers are often interested not only in particular functionalities (i.e., features), but also in non-functional quality attributes such as performance, reliability, and footprint. Measuring quality attributes of all products usually does not scale. In this research-in-progress report, we propose a systematic approach aiming at efficient and scalable prediction of quality attributes of products. To this end, we establish predictors for certain categories of quality attributes (e.g., a predictor for high memory consumption) based on software and network measures, and receiver operating characteristic analysis. Afterwards, we use these predictors to guide a sampling process that takes the assets of a product line as input and determines the products that fall into the category denoted by the given predictor (e.g., products with high memory consumption). In other words, we suggest using predictors to make the process of finding “acceptable ” products more efficient. We discuss and compare several strategies to incorporate predictors in the sampling process
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