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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.

FeatureIDE is an open-source framework for feature-oriented software development (FOSD) based on Eclipse. FOSD is a paradigm for construction, customization, and synthesis of software systems. Code artifacts are mapped to features and a customized software system can be generated given a selection of features. The set of software systems that can be generated is called a software product line (SPL). FeatureIDE supports several FOSD implementation techniques such as feature-oriented programming, aspect-oriented programming, delta-oriented programming, and preprocessors. All phases of FOSD are supported in FeatureIDE, namely domain analysis, requirements analysis, domain implementation, and software generation.

Software-product-line engineering is an efficient means to generate a family of program variants for a domain from a single code base. However, because of the potentially high number of possible program variants, it is difficult to test them all and ensure properties like type safety for the entire product line. We present a product-line-aware type system that can type check an entire software product line without generating each variant in isolation. Specifically, we extend the Featherweight Java calculus with feature annotations for product-line development and prove formally that all program variants generated from a well-typed product line are well-typed. Furthermore, we present a solution to the problem of typing mutually exclusive features. We discuss how results from our formalization helped implementing our own product-line tool CIDE for full Java and report of experience with detecting type errors in four existing software-product-line implementations.

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Abstract-Two programming paradigms are gaining attention in the overlapping fields of software product lines (SPLs) and incremental software development (ISD). Feature-oriented programming (FOP) aims at large-scale compositional programming and feature modularity in SPLs using ISD. Aspect-oriented programming (AOP) focuses on the modularization of crosscutting concerns in complex software. Although feature modules, the main abstraction mechanisms of FOP, perform well in implementing large-scale software building blocks, they are incapable of modularizing certain kinds of crosscutting concerns. This weakness is exactly the strength of aspects, the main abstraction mechanisms of AOP. We contribute a systematic evaluation and comparison of FOP and AOP. It reveals that aspects and feature modules are complementary techniques. Consequently, we propose the symbiosis of FOP and AOP and aspectual feature modules (AFMs), a programming technique that integrates feature modules and aspects. We provide a set of tools that support implementing AFMs on top of Java and C++. We apply AFMs to a nontrivial case study demonstrating their practical applicability and to justify our design choices.

Program comprehension is an important cognitive process that inherently eludes direct measurement. Thus, researchers are struggling with providing suitable programming languages, tools, or coding conventions to support developers in their everyday work. In this paper, we explore whether functional magnetic resonance imaging (fMRI), which is well established in cognitive neuroscience, is feasible to more directly measure program comprehension. In a controlled experiment, we observed 17 participants inside an fMRI scanner while they were comprehending short source-code snippets, which we contrasted with locating syntax errors. We found a clear, distinct activation pattern of five brain regions, which are related to working memory, attention, and language processing-all processes that fit well to our understanding of program comprehension. Our results encourage us and, hopefully, other researchers to use fMRI in future studies to measure program comprehension and, in the long run, answer questions, such as: Can we predict whether someone will be an excellent programmer? How effective are new languages and tools for program understanding? How should we train developers?

Abstract. This paper presents FeatureC++, a novel language extension to C++ that supports Feature-Oriented Programming (FOP) and Aspect-Oriented Programming (AOP). Besides well-known concepts of FOP languages, FeatureC++ contributes several novel FOP language features, in particular multiple inheritance and templates for generic programming. Furthermore, FeatureC++ solves several problems regarding incremental software development by adopting AOP concepts. Starting our considerations on solving these problems, we give a summary of drawbacks and weaknesses of current FOP languages in expressing incremental refinements. Specifically, we outline five key problems and present three approaches to solve them: Multi Mixins, Aspectual Mixin Layers, and Aspectual Mixins that adopt AOP concepts in different ways. We use FeatureC++ as a representative FOP language to explain these three approaches. Finally, we present a case study to clarify the benefits of FeatureC++ and its AOP extensions.