Toward automated quality-centric product line configuration using intentional variability

Noorian, Mahdi; Bagheri, Ebrahim; Du, Weichang

doi:10.1002/smr.1870

Cited by 19 publications

(12 citation statements)

References 71 publications

(166 reference statements)

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“…Below, we present general rules that can be used to transform complex generic structures of extended feature models into linear constraints. The transformation of a feature model into a set of linear constraints has been approached in previous contributions [11,12]. How-ever, these works do not consider the transformation of extended feature models.…”

Section: Transformation Of the Variability Model Into Linear Constraintsmentioning

confidence: 99%

“…This formulation is based on a mapping between extended feature models and linear constraints, which is part of our solution to combine proactive control with DSPL. Although previous works have proposed this kind of mapping [11,12], they only consider basic feature models. This kind of feature models does not sup-port numerical features and they only consider simple cross-tree constraints (i.e., include or exclude), so they cannot be applied in several real case studies.…”

Section: Introductionmentioning

confidence: 99%

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ProDSPL: Proactive self-adaptation based on Dynamic Software Product Lines

Ayala

Papadopoulos

Amor

et al. 2021

Journal of Systems and Software

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Dynamic Software Product Lines (DSPLs) are a well-accepted approach for self-adaptation at runtime. In the context of DSPLs, there are plenty of reactive approaches that apply countermeasures as soon as a context change happens, but they often imply making many reconfigurations, which makes the system more unstable. In this paper we propose a proactive approach, ProDSPL, that exploits an automatically learnt model of the system, which anticipates future variations of the system, and generates the best DSPL configuration that can soften the negative impact of future events on the quality requirements of the system. ProDSPL formulates the problem of the generation of dynamic reconfigurationas as a proactive controller over a prediction horizon, which includes a mapping of the valid configurations of the DSPL into linear constraints. Our approach is evaluated and compared against a reactive approach, DAGAME, based also on a DSPL, which uses a genetic algorithm to generate quasi-optimal feature model configurations at runtime. The evaluation with a mobile game and randomly generated feature models shows that ProDSPL gives good results with regard to the quality of the configurations generated that tries to anticipate future events and it always enforces the system to make the least possible reconfigurations.

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Section: Transformation Of the Variability Model Into Linear Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ProDSPL: Proactive self-adaptation based on Dynamic Software Product Lines

Ayala

Papadopoulos

Amor

et al. 2021

Journal of Systems and Software

View full text Add to dashboard Cite

show abstract

“…Olaechea et al first addressed the SPL configuration optimization problem using an exact (guided improved algorithm) and approximate (IBEA) algorithms and compare their accuracy, time consumption, scalability, and parameter setting requirements on cases with increasing complexity. Noorian et al proposed an approach for the configuration process that seeks to satisfy the stakeholders' requirements and the FMs' structural and integrity constraints. The most important is that they formalize the structure of the FM, the stakeholers' objectives, and their preferences in the form of an integer linear program to conduct a semiautomated FM configuration process.…”

Section: Related Workmentioning

confidence: 99%

A parallel portfolio approach to configuration optimization for large software product lines

Shi

Guo

et al. 2018

Softw Pract Exp

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Summary Software product line (SPL) engineering demands for optimal or near‐optimal products that balance multiple often competing and conflicting objectives. A major challenge for large SPLs is to efficiently explore a huge space of various products and satisfy a large number of predefined constraints simultaneously. To improve the optimality and convergence speed, we propose a parallel portfolio approach, called IBEAPORT, which designs three algorithm variants by incorporating constraint solving into the indicator‐based evolutionary algorithm in different ways and performs these variants by utilizing parallelization techniques. Our approach utilizes the exploration capabilities of different algorithms and improves optimality as far as possible within a limited time budget. We evaluate our approach on five large‐scale real‐world SPLs. Empirical results demonstrate that our approach significantly outperforms the state of the art for all five SPLs on a quality indicator and a diversity indicator. Moreover, IBEAPORT quickly converges to a relatively stable hypervolume value even for the largest SPL with 6888 features.

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“…This increases complexity, cost and effort for reliability after configuration in cloud-based component software development applications [13], [22], [23]. The healthcare body sensor continuously conveys the patient's condition to doctors and medical staff.…”

Section: Introductionmentioning

confidence: 99%

Towards Pattern-Based Change Verification Framework for Cloud-Enabled Healthcare Component-Based

et al. 2020

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To survive in the competitive environment, most organizations have adopted componentbased software development strategies in the rapid technology advancement era and the proper utilization of cloud-based services. To facilitate the continuous configuration, reduce complexity, and faster system delivery for higher user satisfaction in dynamic scenarios. In cloud services, customers select services from web applications dynamically. Healthcare body sensors are commonly used for diagnosis and monitoring patients continuously for their emergency treatment. The healthcare devices are connected with mobile or laptop etc. on cloud environment with network and frequently change applications. Thus, organizations rely on regression testing during changes and implementation to validate the quality and reliability of the system after the alteration. However, for a large application with limited resources and frequently change component management activities in the cloud computing environment, component-based system verification is difficult and challenging due to irrelevant and redundant test cases and faults. In this study, proposed a test case selection and prioritization framework using a design pattern to increase the faults detection rate. First, we select test cases on frequently accessed components using observer patterns and, secondly, prioritize test cases on adopting some strategies. The proposed framework was validated by an experiment and compared with other techniques (previous faults based and random priority). Hence, experimental results show that the proposed framework successfully verified changes. Subsequently, the proposed framework increases the fault detection rate (i.e., more than 90%) than previous faults based and random priority (i.e., more than 80% respectively).

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Toward automated quality-centric product line configuration using intentional variability

Cited by 19 publications

References 71 publications

ProDSPL: Proactive self-adaptation based on Dynamic Software Product Lines

ProDSPL: Proactive self-adaptation based on Dynamic Software Product Lines

A parallel portfolio approach to configuration optimization for large software product lines

Towards Pattern-Based Change Verification Framework for Cloud-Enabled Healthcare Component-Based

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