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Software-product-line engineering has gained considerable momentum in the recent years, both in industry and in academia. A software product line is a family of software products that share a common set of features. Software product lines challenge traditional analysis techniques, such as type checking, model checking, and theorem proving, in their quest of ensuring correctness and reliability of software. Simply creating and analyzing all products of a product line is usually not feasible, due to the potentially exponential number of valid feature combinations. Recently, researchers began to develop analysis techniques that take the distinguishing properties of software product lines into account, for example, by checking feature-related code in isolation or by exploiting variability information during analysis. The emerging field of product-line analyses is both broad and diverse, so it is difficult for researchers and practitioners to understand their similarities and differences. We propose a classification of product-line analyses to enable systematic research and application. Based on our insights with classifying and comparing a corpus of 123 research articles, we develop a research agenda to guide future research on product-line analyses.

Building software product lines (SPLs) with features is a challenging task. Many SPL implementations support features with coarse granularitye.g., the ability to add and wrap entire methods. However, fine-grained extensions, like adding a statement in the middle of a method, either require intricate workarounds or obfuscate the base code with annotations. Though many SPLs can and have been implemented with the coarse granularity of existing approaches, fine-grained extensions are essential when extracting features from legacy applications. Furthermore, also some existing SPLs could benefit from fine-grained extensions to reduce code replication or improve readability. In this paper, we analyze the effects of feature granularity in SPLs and present a tool, called Colored IDE (CIDE), that allows features to implement coarsegrained and fine-grained extensions in a concise way. In two case studies, we show how CIDE simplifies SPL development compared to traditional approaches.

Feature-oriented software development (FOSD) is a paradigm for the construction, customization, and synthesis of large-scale software systems. In this survey, we give an overview and a personal perspective on the roots of FOSD, connections to other software development paradigms, and recent developments in this field. Our aim is to point to connections between different lines of research and to identify open issues.

Almost every complex software system today is configurable. While configurability has many benefits, it challenges performance prediction, optimization, and debugging. Often, the influences of individual configuration options on performance are unknown. Worse, configuration options may interact, giving rise to a configuration space of possibly exponential size. Addressing this challenge, we propose an approach that derives a performance-influence model for a given configurable system, describing all relevant influences of configuration options and their interactions. Our approach combines machine-learning and sampling heuristics in a novel way. It improves over standard techniques in that it (1) represents influences of options and their interactions explicitly (which eases debugging), (2) smoothly integrates binary and numeric configuration options for the first time, (3) incorporates domain knowledge, if available (which eases learning and increases accuracy), (4) considers complex constraints among options, and (5) systematically reduces the solution space to a tractable size. A series of experiments demonstrates the feasibility of our approach in terms of the accuracy of the models learned as well as the accuracy of the performance predictions one can make with them.