Biblio

A well-defined structure is essential in all software development, thus providing an avenue for smooth execution of the processes involved during various software development phases. One of the potential benefits provided by a well-defined structure is systematic reuse of software artifacts. Requirements pattern approach provides guidelines and modality that enables a systematic way of specifying and documenting requirements, which in turn supports a systematic reuse. Although there is a great deal of research concerning requirements pattern in the literature, the research focuses are not on requirement engineering (RE) activities of SPLE. In this paper, we proposed a software requirement pattern (SRP) structure based on RePa Requirements Pattern Template, which was adapted to best suit RE activities in SPLE. With this requirement pattern structure, RE activities such as elicitation and identification of common and variable requirements as well as the specification, documentation, and reuse in SPLE could be substantially improved.

Today's software systems are too complex to ensure security after the fact – security has to be built into systems by design. To this end, model-based techniques such as UMLsec support the design-time specification and analysis of security requirements by providing custom model annotations and checks. Yet, a particularly challenging type of complexity arises from the variability of software product lines. Analyzing the security of all products separately is generally infeasible. In this work, we propose SecPL, a methodology for ensuring security in a software product line. SecPL allows developers to annotate the system design model with product-line variability and security requirements. To keep the exponentially large configuration space tractable during security checks, SecPL provides a family-based security analysis. In our experiments, this analysis outperforms the naive strategy of checking all products individually. Finally, we present the results of a user study that indicates the usability of our overall methodology.

The development of variable software, in general, and feature models, in particular, is an error-prone and time-consuming task. It gets increasingly more challenging with industrial-size models containing hundreds or thousands of features and constraints. Each change may lead to anomalies in the feature model such as making some features impossible to select. While the detection of anomalies is well-researched, giving explanations is still a challenge. Explanations must be as accurate and understandable as possible to support the developer in repairing the source of an error. We propose an efficient and generic algorithm for explaining different anomalies in feature models. Additionally, we achieve a benefit for the developer by computing short explanations expressed in a user-friendly manner and by emphasizing specific parts in explanations that are more likely to be the cause of an anomaly. We provide an open-source implementation in FeatureIDE and show its scalability for industrial-size feature models.

Testing a software product line such as Linux implies building the source with different configurations. Manual approaches to generate configurations that enable code of interest are doomed to fail due to the high amount of variation points distributed over the feature model, the build system and the source code. Research has proposed various approaches to generate covering configurations, but the algorithms show many drawbacks related to run-time, exhaustiveness and the amount of generated configurations. Hence, analyzing an entire Linux source can yield more than 30 thousand configurations and thereby exceeds the limited budget and resources for build testing. In this paper, we present an approach to fill the gap between a systematic generation of configurations and the necessity to fully build software in order to test it. By merging previously generated configurations, we reduce the number of necessary builds and enable global variability-aware testing. We reduce the problem of merging configurations to finding maximum cliques in a graph. We evaluate the approach on the Linux kernel, compare the results to common practices in industry, and show that our implementation scales even when facing graphs with millions of edges.

Defect-prediction techniques can enhance the quality assurance activities for software systems. For instance, they can be used to predict bugs in source files or functions. In the context of a software product line, such techniques could ideally be used for predicting defects in features or combinations of features, which would allow developers to focus quality assurance on the error-prone ones. In this preliminary case study, we investigate how defect prediction models can be used to identify defective features using machine-learning techniques. We adapt process metrics and evaluate and compare three classifiers using an open-source product line. Our results show that the technique can be effective. Our best scenario achieves an accuracy of 73 % for accurately predicting features as defective or clean using a Naive Bayes classifier. Based on the results we discuss directions for future work.

FeatureIDE is an open-source framework to model, develop, and analyze feature-oriented software product lines. It is mainly developed in a cooperation between University of Magdeburg and Metop GmbH. Nevertheless, many other institutions contributed to it in the past decade. Goal of this tutorial is to illustrate how FeatureIDE can be used to clean variable code, whereas we will focus on dependencies in feature models and on variability implemented with preprocessors. The hands-on tutorial will be highly interactive and is devoted to practitioners facing problems with variability, lecturers teaching product lines, and researchers who want to safe resources in building product line tools.

Defect-prediction techniques can enhance the quality assurance activities for software systems. For instance, they can be used to predict bugs in source files or functions. In the context of a software product line, such techniques could ideally be used for predicting defects in features or combinations of features, which would allow developers to focus quality assurance on the error-prone ones. In this preliminary case study, we investigate how defect prediction models can be used to identify defective features using machine-learning techniques. We adapt process metrics and evaluate and compare three classifiers using an open-source product line. Our results show that the technique can be effective. Our best scenario achieves an accuracy of 73 % for accurately predicting features as defective or clean using a Naive Bayes classifier. Based on the results we discuss directions for future work.