Optimization of Variability in Software Product Lines

Loesch, Felix; Ploedereder, Erhard

doi:10.1109/spline.2007.4339264

Cited by 30 publications

(39 citation statements)

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“…Our preceding survey [5] showed that feature models were among the most popular notations, but also that a wide range of notations and tools is used. It also confirmed the existence of large models-which have been reported before [39,38,29], but without any further characteristics, such as the use or complexity of constraints. information on adoption practices, organizational structures, and architectures, but offer little insight into the use of variability models, their sizes and contents, and the techniques used to build them.…”

Section: Background and Related Worksupporting

confidence: 85%

“…Exceptions are industrial experience reports. Grünbacher et al [17] emphasize that techniques need to be customized to the organizational context in which they are used; Reiser et al [29] request compliance constraints for the same purpose; Riebisch et al [30] point out the use of feature models by nonsoftware developers; Gillan et al [16] identify a lack of documented methodologies to create feature models. These reports are complementary to our study, but cannot provide a coherent picture.…”

Section: Background and Related Workmentioning

confidence: 99%

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Three Cases of Feature-Based Variability Modeling in Industry

Berger

Nair

Rublack

et al. 2014

Lecture Notes in Computer Science

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Abstract. Large software product lines need to manage complex variability. A common approach is variability modeling-creating and maintaining models that abstract over the variabilities inherent in such systems. While many variability modeling techniques and notations have been proposed, little is known about industrial practices and how industry values or criticizes this class of modeling. We attempt to address this gap with an exploratory case study of three companies that apply variability modeling. Among others, our study shows that variability models are valued for their capability to organize knowledge and to achieve an overview understanding of codebases. We observe centralized model governance, pragmatic versioning, and surprisingly little constraint modeling, indicating that the effort of declaring and maintaining constraints does not always pay off.

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Section: Background and Related Worksupporting

confidence: 85%

Section: Background and Related Workmentioning

confidence: 99%

Three Cases of Feature-Based Variability Modeling in Industry

Berger

Nair

Rublack

et al. 2014

Lecture Notes in Computer Science

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“…However, ScenTED does not test the core asset to see how it is flexible enough to support the variations as specially mentioned by the participants. Evolution of variability is also reported as one of the challenges by the participants; however, only three approaches, FDL [27], Ye'05 [28] and Loesch'07 [16], which are concerned with evolution of variability were found. These approaches only provide very limited support for evolution of variability, a systematic approach to provide a comprehensive support for variability evolution is not available.…”

Section: Discussionmentioning

confidence: 99%

Variability Management in Software Product Lines: An Investigation of Contemporary Industrial Challenges

Chen

Babar

2010

Lecture Notes in Computer Science

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Abstract. Variability management is critical for achieving the large scale reuse promised by the software product line paradigm. It has been studied for almost 20 years. We assert that it is important to explore how well the body of knowledge of variability management solves the challenges faced by industrial practitioners, and what are the remaining and (or) emerging challenges. To gain such understanding of the challenges of variability management faced by practitioners, we have conducted an empirical study using focus group as data collection method. The results of the study highlight several technical challenges that are often faced by practitioners in their daily practices. Different from previous studies, the results also reveal and shed light on several non-technical challenges that were almost neglected by existing research.

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“…Although industries reported FMs with hundreds or thousands of features (Loesch & Ploedereder, 2007;Steger et al, 2004), authors typically published only a small excerpt of their FMs. Large FMs are difficult to find for a thorough evaluation.…”

Section: Discussionmentioning

confidence: 99%

“…However, these approaches suffer from an NP-hard problem of feature combinatorics and take a long time to perform with large FMs (Batory, Benavides, & Ruiz-Cortes, 2006). Reports from industry have shown that practical FMs could have hundreds or thousands of features (Loesch & Ploedereder, 2007;Steger et al, 2004), so existing approaches can take hours or days to detect inconsistencies.…”

Section: Introductionmentioning

confidence: 99%

Consistency maintenance for evolving feature models

Guo

Wang

Trinidad

et al. 2012

Expert Systems with Applications

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a b s t r a c tSoftware product line (SPL) techniques handle the construction of customized systems. One of the most common representations of the decisions a customer can make in SPLs is feature models (FMs). An FM represents the relationships among common and variable features in an SPL. Features are a representation of the characteristics in a system that are relevant to customers.FMs are subject to change since the set of features and their relationships can change along an SPL lifecycle. Due to this evolution, the consistency of FMs may be compromised. There exist some approaches to detect and explain inconsistencies in FMs, however this process can take a long time for large FMs.In this paper we present a complementary approach to dealing with inconsistencies in FM evolution scenarios that improves the performance for existing approaches reducing the impact of change to the smallest part of an FM that changes. To achieve our goal, we formalize FMs from an ontological perspective and define constraints that must be satisfied in FMs to be consistent. We define a set of primitive operations that modify FMs and which are responsible for the FM evolution, analyzing their impact on the FM consistency. We propose a set of predefined strategies to keep the consistency for error-prone operations.As a proof-of-concept we present the results of our experiments, where we check for the effectiveness and efficiency of our approach in FMs with thousands of features. Although our approach is limited by the kinds of consistency constraints and the primitive operations we define, the experiments present a significant improvement in performance results in those cases where they are applicable.

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Optimization of Variability in Software Product Lines

Cited by 30 publications

References 0 publications

Three Cases of Feature-Based Variability Modeling in Industry

Three Cases of Feature-Based Variability Modeling in Industry

Variability Management in Software Product Lines: An Investigation of Contemporary Industrial Challenges

Consistency maintenance for evolving feature models

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