Supporting Feature Model Evolution by Lifting Code-Level Dependencies: A Research Preview

Hinterreiter, Daniel; Feichtinger, Kevin; Linsbauer, Lukas; Prähofer, Herbert; Grünbacher, Paul

doi:10.1007/978-3-030-15538-4_12

Cited by 6 publications

(8 citation statements)

References 16 publications

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“…Because of this scenario, and based on the benefit of reuse principles, Software Product Line (SPL) has been developed as a software development paradigm to produce software inspired by product line approach. Developing an SPL system enables us to create a software structure that is customizable to various needs, by maximizing software artefacts reusability [1]. Due to the highly variable and reusable nature of SPL artefacts, it is uneconomic to develop software based on distinct requirements separately, as some of the functionalities are similar.…”

Section: Introductionmentioning

confidence: 99%

“…A unit of system function in an SPL is represented as a feature, and explicitly defined as common or variable features and utilized throughout the SPL development process. One way to model the commonalities and variabilities in an SPL is using a Feature Model (FM), based on feature modeling technique [1]. Two or more features are combined and utilized together in a single software product.…”

Section: Introductionmentioning

confidence: 99%

“…The flexibility of feature configuration process could result in unspecified and unintended system behavior. This might lead to incorrect execution [1]. Hence, it is crucial to test all possible feature configurations to reduce the potential misbehavior of interacting features.…”

Section: Introductionmentioning

confidence: 99%

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Marginal Distribution Algorithm for Feature Model Test Configuration Generation

Sahid,

Saringat,

Hamzah

et al. 2023

IJACSA

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Generating test configuration for Software Product Line (SPL) is difficult, due to the exponential effect of feature combination. Pairwise testing can generate test input for a single software product that deviates from exhaustive testing, nevertheless proven to be effective. In the context of SPL testing, to generate minimal test configuration that maximizes pairwise coverage is not trivial, especially when dealing with a huge number of features and when constraints must be satisfied, which is the case in most SPL systems. In this paper, we propose an estimation of distribution algorithm, based on pairwise testing, to alleviate this problem. Comparisons are made against a greedybased and a constraint handling based approach. The experiments demonstrate the feasibility of the proposed algorithm, such that it achieves better test configurations dissimilarity and at the same time maintain the test configuration size and pairwise coverage. This is supported by analysis using descriptive statistics.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Marginal Distribution Algorithm for Feature Model Test Configuration Generation

Sahid,

Saringat,

Hamzah

et al. 2023

IJACSA

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“…The consistency of an FM means that it remains well-formed (syntactic consistency) and defines at least one valid product (semantic consistency) [15]. Checking and resolving FM inconsistencies are essential tasks during the product line evolution [16]. Automated tools for inconsistency checking of FM and configuration are critical activities for successful SPLs [17].…”

Section: Introductionmentioning

confidence: 99%

Reviewing Automated Analysis of Feature Model Solutions for the Product Configuration

et al. 2022

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Feature models (FMs) appeared more than 30 years ago, and they are valuable tools for modeling the functional variability of systems. The automated analysis of feature models (AAFM) is currently a thriving, motivating, and active research area. The product configuration of FMs is a relevant and helpful operation, a crucial activity overall with large-scale feature models. The minimal conflict detection, the diagnosis of in-conflict configuration, and the product completion of consistent partial configuration are significant operations for obtaining consistent and well-defined products. Overall, configuring products for large-scale variability intensive systems (VIS) asks for efficient automated solutions for minimal conflict, diagnosis, and product configuration. Given the relevance of minimal conflict, diagnosis, and product configuration, and the current application of large-scale configuration and FMs for representing those systems and products, the main goals of this research paper are to establish the fundaments of the product configuration of feature models and systematically review existing solutions for the conflict detection, diagnosis, and product completion in FMs from 2010 to 2019. We can perceive that even though modern computing approaches exist for AAFM operations, no solutions exist for assisting the product configurations before 2020. This article reports that in 2020, new solutions appear regarding applying parallel computing for those goals. This research highlights research opportunities for developing new and more efficient solutions for conflict detection, diagnosis, and product completion of large-scale configurations.

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“…This model-based software development helps to visualize required features in a tree structure considering various aspects, including testability, code generation, traceability, reusability, testing workflow, constraints verification, and validation. The feature model allows feature-to-artifact mappings to illustrate how the system functions are implemented in terms of relationships, dependencies, behaviors, and constraints [24]. It supports reusability to improve development time (e.g., feature search time, verification), which are major bottlenecks of releasing embedded software even after following agile methodology.…”

Section: Introductionmentioning

confidence: 99%

Facilitating Reuse of Functions in Embedded Software

2022

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Developing software-intensive embedded systems is a significant challenge as embedded systems have become more complex and dynamic for integrating various constraints (e.g., real-time performance, design, and functional safety). In general, software practitioners reuse code to reduce development time for implementing required functionalities. However, embedded software development still lacks code reuse on a large scale due to its intricate constraints. This paper presents an approach to identifying reusable functions and their relationships from the legacy embedded software for faster development reducing functionality testing time. A python tool is developed to automatically extract the reusable functions and present them in a feature model. The search time for finding reusable functions speeds up when they are placed as features in a feature model. We conducted experiments on three GitHub embedded software projects (elevator, infotainment, and health monitoring systems). The experimental results find that our proposed approach reduces the feature search time by 59.0%, 49.8%, and 74.5%, respectively, compared to the manual searching approach for 50 features.

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Supporting Feature Model Evolution by Lifting Code-Level Dependencies: A Research Preview

Cited by 6 publications

References 16 publications

Marginal Distribution Algorithm for Feature Model Test Configuration Generation

Marginal Distribution Algorithm for Feature Model Test Configuration Generation

Reviewing Automated Analysis of Feature Model Solutions for the Product Configuration

Facilitating Reuse of Functions in Embedded Software

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