Techniques and Prediction Models for Sustainable Product-Line Engineering

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Software product line engineering has gained considerable momentum in recent years, both in industry and in academia. Companies and institutions such as NASA, Hewlett Packard, General Motors, Boeing, Nokia, and Philips apply product-line technology with great success to sustain their development by broadening their product portfolio, improving software quality, shorting time to market, and being able to react faster to market changes. However, pursuing a product-line approach implies often an up-front investment for future benefits. Product-line developers have to anticipate which features will be desired by customers in the future. So, prediction models play an important role to avoid uneconomic developments. However, contemporary prediction models largely ignore structural and behavioral properties of the architecture and implementation assets of a product line. For example, modifying the transaction management of a database system is by far more expensive and risky than modifying its command-line interface. We propose to rethink contemporary prediction models and to employ state-of-the-art analysis techniques to create a richer knowledge base for predictions based on implementation knowledge, including software metrics, static analysis, mining techniques, measurements of non-functional properties, and feature-interaction analysis.