Configuring Software Product Line Feature Models Based on Stakeholders’ Soft and Hard Requirements

Bagheri, Ebrahim; Noia, Tommaso Di; Ragone, Azzurra; Gašević, Dragan

doi:10.1007/978-3-642-15579-6_2

Cited by 34 publications

(31 citation statements)

References 14 publications

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“…Authors also verified their approach on the feature models in the rang of 100 to 5000 features. Bagheri et al shows the approach to construct feature models with its constraints using a propositional formulae [46]. They also explained the formalism to configure a semi-automated feature model.…”

Section: Related Workmentioning

confidence: 99%

Traceability Analyses between Features and Assets in Software Product Lines

Narwane

Galindo

Krishna

et al. 2016

Entropy

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Abstract:In a Software Product Line (SPL), the central notion of implementability provides the requisite connection between specifications and their implementations, leading to the definition of products. While it appears to be a simple extension of the traceability relation between components and features, it involves several subtle issues that were overlooked in the existing literature. In this paper, we have introduced a precise and formal definition of implementability over a fairly expressive traceability relation. The consequent definition of products in the given SPL naturally entails a set of useful analysis problems that are either refinements of known problems or are completely novel. We also propose a new approach to solve these analysis problems by encoding them as Quantified Boolean Formulae (QBF) and solving them through Quantified Satisfiability (QSAT) solvers. QBF can represent more complex analysis operations, which cannot be represented by using propositional formulae. The methodology scales much better than the SAT-based solutions hinted in the literature and were demonstrated through a tool called SPLAnE (SPL Analysis Engine) on a large set of SPL models.

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Section: Related Workmentioning

confidence: 99%

Traceability Analyses between Features and Assets in Software Product Lines

Narwane

Galindo

Krishna

et al. 2016

Entropy

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“…More specifically most of these techniques have been formulated to address the automated configuration of software product line feature models based on stakeholders' functional and non-functional requirements [33,34,35,36,37]. Feature model configurations have been often derived, validated and verified using Logic Truth Maintenance Systems (LTMS).…”

Section: Related Workmentioning

confidence: 99%

Dynamic decision models for staged software product line configuration

Bagheri

Ensan

2013

Requirements Eng

Self Cite

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Abstract. Software product line engineering practices offer desirable characteristics such as rapid product development, reduced time-to-market and more affordable development costs as a result of systematic representation of the variabilities of a domain of discourse that leads to methodical reuse of software assets. The development lifecycle of a product line consists of two main phases: domain engineering, which deals with the understanding and formally modeling of the target domain; and application engineering that is concerned with the configuration of a product line into one concrete product based on the preferences and requirements of the stakeholders. The work presented in this paper focuses on the application engineering phase and builds both the theoretical and technological tools to assist the stakeholders in (1) understanding the complex interactions of the features of a product line; (2) eliciting the utility of each feature for the stakeholders and hence exposing the stakeholders' otherwise implicit preferences in a way that they can more easily make decisions; and (3) dynamically building a decision model through interaction with the stakeholders and by considering the structural characteristics of software product line feature models, which will guide the stakeholders through the product configuration process. Initial exploratory empirical experiments that we have performed show that our proposed approach for helping stakeholders understand their feature preferences and its associated staged feature model configuration process is able to positively impact the quality of the end results of the application engineering process within the context of the limited number of participants. In addition, it has been observed that the offered tooling support is able to ease the staged feature model configuration process.

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“…Furthermore, our work is in line, and has the potential to be integrated, with the work in [38] and the FARE method proposed in [39], that show possible ways to integrate features with domain goals and knowledge to help for eliciting and justifying features. Recent work [40] focuses on the selection of feature model configurations that take into account soft constraints that represent desired quality attributes. Unlike us, such approach does not explicitly consider contextual factors, which are fundamental to discriminate between possible and inapplicable alternatives.…”

Section: Related Workmentioning

confidence: 99%

Requirements-driven deployment

2012

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Deployment is a main development phase which configures a software to be ready for use in a certain environment. The ultimate goal of deployment is to enable users to achieve their requirements while using the deployed software. However, requirements are not uniform and differ between deployment environments. In one environment, certain requirements could be useless or redundant, thereby making some software functionalities superfluous. In another environment, instead, some requirements could be impossible to achieve and, thus, additional functionalities would be required. We advocate that ensuring fitness between requirements and the system environment is a basic and critical step to achieve a comprehensive deployment process. We propose a tool-supported modelling and analysis approach to tailor a requirements model to each environment in which the system is to be deployed. We study the case of contextual goal model, which is a requirements model that captures the relationship between the variability of requirements (goal variability space) and the varying states of a deployment environment (context variability space). Our analysis relies on sampling a deployment environment to discover its context variability space and use it to identify loci in the contextual goal model where a modification has to take place. Finally, we apply our approach in practice and report on the obtained results.

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Configuring Software Product Line Feature Models Based on Stakeholders’ Soft and Hard Requirements

Cited by 34 publications

References 14 publications

Traceability Analyses between Features and Assets in Software Product Lines

Traceability Analyses between Features and Assets in Software Product Lines

Dynamic decision models for staged software product line configuration

Requirements-driven deployment

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