Predicting Quality Attributes of Software Product Lines Using Software and Network Measures and Feature Sampling

kaestner@cs.cmu.edu (S. Kolesnikov, S. Apel, N. Siegmund, S. Sobernig, C. Kästner, S. Senkaya)
Fri, 10 Jan 2025 13:48:21 GMT

Sergiy S. Kolesnikov, Sven Apel, Norbert Siegmund, Stefan Sobernig, Christian Kästner, and Semah Senkaya. Predicting Quality Attributes of Software Product Lines Using Software and Network Measures and Feature Sampling. In Proceedings of the 7th Int'l Workshop on Variability Modelling of Software-Intensive Systems (VaMoS), ISBN 978-1-4503-1541-8, pages 6:1--6:5, New York, NY: ACM Press, January 2013.

Software product-line engineering aims at the development of families of related products that share common assets. An important aspect is that 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. A naive approach is to measure quality attributes of every single product, and to deliver the products that fit the customers' needs. However, as product lines may consist of millions of products, this approach does not scale. In this research-in-progress report, we propose a systematic approach for the efficient and scalable prediction of quality attributes of products that consists of two steps. First, we generate predictors for certain categories of quality attributes (e.g., a predictor for low performance) based on software and network measures, and receiver operating characteristic analysis. Second, we use these predictors to guide a sampling process that takes the asset base of a product line as input and efficiently determines the products that fall into the category denoted by a given predictor (e.g., products with low performance). In other words, we use predictors to make the process of finding “acceptable” products more efficient. We discuss and compare several strategies to incorporate predictors in the sampling process.