Towards Multi-view Feature-Based Configuration

Hubaux, Arnaud; Heymans, Patrick; Schobbens, Pierre‐Yves; Deridder, Dirk

doi:10.1007/978-3-642-14192-8_12

Cited by 31 publications

(29 citation statements)

References 9 publications

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“…Hubaux et al [66] propose feature configuration workflows as a new formalism for supporting configuration of large-scale systems. They also propose a technique to specify concerns in feature diagrams and to automatically build concern-specific configuration views [67]. The issue of multi-level configuration is also addressed by Rabiser et al [18] who demonstrate how decision models can be used to support the configuration of a complex system across multiple levels of software vendor, customers and end users.…”

Section: Related Workmentioning

confidence: 99%

Supporting distributed product configuration by integrating heterogeneous variability modeling approaches

Galindo

Dhungana

Rabiser

et al. 2015

Information and Software Technology

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Context: In industrial settings products are developed by more than one organization. Software vendors and suppliers commonly typically maintain their own product lines, which contribute to a larger (multi) product line or software ecosystem. It is unrealistic to assume that the participating organizations will agree on using a specific variability modeling technique-they will rather use different approaches and tools to manage the variability of their systems. Objective: We aim to support product configuration in software ecosystems based on several variability models with different semantics that have been created using different notations. Method: We present an integrative approach that provides a unified perspective to users configuring products in multi product line environments, regardless of the different modeling methods and tools used internally. We also present a technical infrastructure and a prototype implementation based on web services. Results: We show the feasibility of the approach and its implementation by using it with the three most widespread types of variability modeling approaches in the product line community, i.e., feature-based, OVM-style, and decision-oriented modeling. To demonstrate the feasibility and flexibility of our approach, we present an example derived from industrial experience in enterprise resource planning. We further applied the approach to support the configuration of privacy settings in the Android ecosystem based on multiple variability models. We also evaluated the performance of different model enactment strategies used in our approach. Conclusions: Tools and techniques allowing stakeholders to handle variability in a uniform manner can considerably foster the initiation and growth of software ecosystems from the perspective of software reuse and configuration.

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Section: Related Workmentioning

confidence: 99%

Supporting distributed product configuration by integrating heterogeneous variability modeling approaches

Galindo

Dhungana

Rabiser

et al. 2015

Information and Software Technology

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“…ConfDiagDetector injects one mis-configured option at a time, so it has no special treatment for such cases. A possible way to address this limitation is incorporating a feature model [24] that encodes the relationship between configuration options. Doing so might enable ConfDiagDetector to detect even more inadequate diagnostic messages.…”

Section: Limitationsmentioning

confidence: 99%

“…ConfDiagDetector does not consider relations between different options and thus incorrectly flagged this message as inadequate. One way to address this limitation is to incorporate a feature model [24] that encodes such a relation across different options.…”

Section: Detected Inadequate Diagnostic Messagesmentioning

confidence: 99%

Proactive detection of inadequate diagnostic messages for software configuration errors

Zhang

Ernst

2015

Proceedings of the 2015 International Symposium on Software Testing and Analysis

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This paper presents a technique to detect inadequate (i.e., missing or ambiguous) diagnostic messages for configuration errors issued by a configurable software system.The technique injects configuration errors into the software under test, monitors the software outcomes under the injected configuration errors, and uses natural language processing to analyze the output diagnostic message caused by each configuration error. The technique reports diagnostic messages that may be unhelpful in diagnosing a configuration error.We implemented the technique for Java in a tool, ConfDiagDetector. In an evaluation on 4 real-world, mature configurable systems, ConfDiagDetector reported 43 distinct inadequate diagnostic messages (25 missing and 18 ambiguous). 30 of the detected messages have been confirmed by their developers, and 12 more have been identified as inadequate by users in a user study. On average, ConfDiagDetector required 5 minutes of programmer time and 3 minutes of compute time to detect each inadequate diagnostic message.

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“…By contrast, also views on parts of large feature models can be used to treat the complexity [8,10,14]. Configurations in such views related to different concerns of one feature model must be merged by specific rules and checked for consistency.…”

Section: Related Workmentioning

confidence: 99%

Automated analysis of dependent feature models

Schröter

Thüm

Siegmund

et al. 2013

Proceedings of the Seventh International Workshop on Variability Modelling of Software-Intensive Systems

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Feature models specify valid combinations of features in software product lines. With dependent feature models (DFMs), we apply separation of concerns to feature models for two main benefits. First, we can modularize feature models into parts relevant to groups of stakeholders. Second, we are able to model dependencies between different software product lines in a multi-product-line scenario. To ensure consistency and correctness of DFMs, we have to apply analyses, such as dead-feature detection. We discuss why DFMs challenge the detection of inconsistencies, present how to reuse existing analyses for DFMs, and propose new analyses to supplement existing ones. We apply automated analyses in five steps and evaluate the approach using DFMs specified in VELVET by our prototype VeAnalyzer.

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Towards Multi-view Feature-Based Configuration

Cited by 31 publications

References 9 publications

Supporting distributed product configuration by integrating heterogeneous variability modeling approaches

Supporting distributed product configuration by integrating heterogeneous variability modeling approaches

Proactive detection of inadequate diagnostic messages for software configuration errors

Automated analysis of dependent feature models

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