Abstract—A software product line is a set of software products that are distinguished in terms of features (i.e., end-user–visible units of behavior). Feature interactions —situations in which the combination of features leads to emergent and possibly critical behavior — are a major source of failures in software product lines. We explore how feature-aware verification can improve the automatic detection of feature interactions in software product lines. Feature-aware verification uses product-line–verification techniques and supports the specification of feature properties along with the features in separate and composable units. It integrates the technique of variability encoding to verify a product line without generating and checking a possibly exponential number of feature combinations. We developed the tool suite SPLVERIFIER for feature-aware verification, which is based on standard model-checking technology. We applied it to an e-mail system that incorporates domain knowledge of AT&T. We found that feature interactions can be detected automatically based on specifications that have only local knowledge. I.

In many projects, lexical preprocessors are used to manage different variants of the project (using conditional compilation) and to define compile-time code transformations (using macros). Unfortunately, while being a simple way to implement variability, conditional compilation and lexical macros hinder automatic analysis, even though such analysis is urgently needed to combat variability-induced complexity. To analyze code with its variability, we need to parse it without preprocessing it. However, current parsing solutions use unsound heuristics, support only a subset of the language, or suffer from exponential explosion. As part of the TypeChef project, we contribute a novel variability-aware parser that can parse almost all unpreprocessed code without heuristics in practicable time. Beyond the obvious task of detecting syntax errors, our parser paves the road for further analysis, such as variability-aware type checking. We implement variability-aware parsers for Java and GNU C and demonstrate practicability by parsing the product line MobileMedia and the entire X86 architecture of the Linux kernel with 6065 variable features.

During Software Product Line (SPL) maintenance tasks, Virtual Separation of Concerns (VSoC) allows the programmer to focus on one feature and hide the others. However, since features depend on each other through variables and control-flow, feature modularization is compromised since the maintenance of one feature may break another. In this context, emergent interfaces can capture dependencies between the feature we are maintaining and the others, making developers aware of dependencies. To better understand the impact of feature dependencies during SPL maintenance, we have investigated the following two questions: how often methods with preprocessor directives contain feature dependencies? How feature dependencies impact maintenance effort when using VSoC and emergent interfaces? Answering the former is important for assessing how often we may face feature dependency problems. Answering the latter is important to better understand to what extent emergent interfaces complement VSoC during maintenance tasks. To answer them, we analyze 43 SPLs of different domains, size, and languages. The data we collect from them complement previous work on preprocessor usage.

Abstract—A software product line is a set of software products that are distinguished in terms of features (i.e., end-user–visible units of behavior). Feature interactions —situations in which the combination of features leads to emergent and possibly critical behavior — are a major source of failures in software product lines. We explore how feature-aware verification can improve the automatic detection of feature interactions in software product lines. Feature-aware verification uses product-line verification techniques and supports the specification of feature properties along with the features in separate and composable units. It integrates the technique of variability encoding to verify a product line without generating and checking a possibly exponential number of feature combinations. We developed the tool suite SPLVERIFIER for feature-aware verification, which is based on standard model-checking technology. We applied it to an e-mail system that incorporates domain knowledge of AT&T. We found that feature interactions can be detected automatically based on specifications that have only feature-local knowledge, and that variability encoding significantly improves the verification performance when proving the absence of interactions. I.

Abstract—A software product line is a set of program variants, typically generated from a common code base. Feature models describe variability in product lines by documenting features and their valid combinations. In product-line engineering, we need to reason about variability and program variants for many different tasks. For example, given a feature model, we might want to determine the number of all valid feature combinations or compute specific feature combinations for testing. However, we found that contemporary reasoning approaches can only reason about feature combinations, not features into account. Abstract features are features used to structure a feature model that, however, do not have any impact at implementation level. Using existing feature-model reasoning mechanisms for program variants leads to incorrect results. Hence, although abstract features represent domain decisions that do not affect the generation of a program variant. We raise awareness of the problem of abstract features for different kinds of analyses on feature models. We argue that, in order to reason about program variants, abstract features should be made explicit in feature models. We present a technique based on propositional formulas that enables to reason about program variants rather than feature combinations. In practice, our technique can save effort that is caused by considering the same program variant multiple times, for example, in product-line testing. Keywords-Software product lines, program families, feature modeling, feature model, automated analyses. I.

Modularity of feature representations has been a long standing goal of feature-oriented software development. While some researchers regard feature modules and corresponding composition mechanisms as a modular solution, other researchers have challenged the notion of feature modularity and pointed out that most feature-oriented implementation mechanisms lack proper interfaces and support neither modular type checking nor separate compilation. We step back and reflect on the feature-modularity discussion. We distinguish two notions of modularity, cohesion without interfaces and information hiding with interfaces, and point out the different expectations that, we believe, are the root of many heated discussions. We discuss whether feature interfaces should be desired and weigh their potential benefits and costs, specifically regarding crosscutting, granularity, feature interactions, and the distinction between closed-world and open-world reasoning. Because existing evidence for and against feature modularity and feature interfaces is shaky and inconclusive, more research is needed, for which we outline possible directions.

Abstract—Superimposition is a composition technique that has been applied successfully in many areas of software development. Although superimposition is a general-purpose concept, it has been (re)invented and implemented individually for various kinds of software artifacts. We unify languages and tools that rely on superimposition by using the language-independent model of feature structure trees (FSTs). On the basis of the FST model, we propose a general approach to the composition of software artifacts written in different languages. Furthermore, we offer a supporting framework and tool chain, called FEATUREHOUSE. We use attribute grammars to automate the integration of additional languages. In particular, we have integrated Java, C#, C, Haskell, Alloy, and JavaCC. A substantial number of case studies demonstrate the practicality and scalability of our approach and reveal insights into the properties that a language must have in order to be ready for superimposition. We discuss perspectives of our approach and demonstrate how we extended FEATUREHOUSE with support for XML languages (in particular, XHTML, XMI/UML, and Ant) and alternative composition approaches (in particular, aspect weaving). Rounding off our previous work, we provide here a holistic view of the FEATUREHOUSE approach based on rich experience with numerous languages and case studies and reflections on several years of research. Index Terms—FEATUREHOUSE, feature structure trees, software composition, superimposition, language independence

Software product line engineering is an efficient means to generate a set of tailored software products from a common implementation. However, adopting a product-line approach poses a major challenge and significant risks, since typically legacy code must be migrated toward a product line. Our aim is to lower the adoption barrier by providing semiautomatic tool support—called variability mining—to support developers in locating, documenting, and extracting implementations of product-line features from legacy code. Variability mining combines prior work on concern location, reverse engineering, and variability-aware type systems, but is tailored specifically for the use in product lines. Our work extends prior work in three important aspects: (1) we provide a consistency indicator based on a variability-aware type system, (2) we mine features at a fine level of granularity, and (3) we exploit domain knowledge about the relationship between features when available. With a quantitative study, we demonstrate that variability mining can efficiently support developers in locating features. 1

Abstract—A software product line is a family of similar software products that share a common set of assets. The advent of proper variability management and generator technology enables end-users to derive individual products solely based on a selection of desired features. This gives rise to a huge configuration space of possible products. But the high degree of variability comes at a cost: classic analysis methods do not scale any more; there are simply too many potential products to analyze. Hence, researchers have begun to develop variabilityaware analyses, which exploit the similarities of the products of a product line to reduce analysis effort. However, while being promising, variability-aware analyses have not been applied to real-world product lines so far. We close this gap by developing and applying two full-fledged analyses to two real-world, largescale systems: the Busybox tool suite and the Linux kernel. We report on our experience with making variability-aware analysis ready for the real world, and with applying it to large-scale product lines. A key result is that variability-aware analysis can outperform even very limited sampling heuristics with respect to analysis time. I.

In this paper, we develop a vision of software evolution based on a feature-oriented perspective. From the fact that features provide a common ground to all stakeholders, we derive a hypothesis that changes can be effectively managed in a feature-oriented manner. Assuming that the hypothesis holds, we argue that feature-oriented software evolution relying on automatic traceability, analyses, and recommendations reduces existing challenges in understanding and managing evolution. We illustrate these ideas using an automotive example and raise research questions for the community. Categories and Subject Descriptors D.2.8 [Software Engineering]: Metrics—complexity measures;