Supporting Online Updates of Software Product Lines: A Controlled Experiment

Michalik, Bartosz; Weyns, Danny; Boucké, Nelis; Helleboogh, Alexander

doi:10.1109/esem.2011.27

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…Before starting the our empirical study, a pilot project was performed, in which the participants selected 2 random papers: Michalik et al (2011a) and Murashkin et al (2013a), which are not included in the 30 papers of the study, to calibrate and equalize the evaluation form of each participant.…”

Section: Execution Preparationmentioning

confidence: 99%

Comparing Approaches for Quality Evaluation of Software Engineering Experiments: An Empirical Study on Software Product Line Experiments

Furtado¹,

Vignando²,

França³

et al. 2019

Journal of Computer Science

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The Software Engineering (SE) research area must provide results of a certain quality for the sake of value. High quality research results may ensure experience and knowledge, which are essential for the technology to be transferred to the industry. One of the means to obtain such quality results is experimentation. Experimentation is a scientific method that aims to provide evidence of a theory over real-world observations establishing a cause-effect relation. Well conducted, auditable and repeatable experiments are vital for scientific evolution and novelty. Quality evaluation of controlled experiments and quasi-experiments in SE has been recently discussed in the literature as researchers desire to assess whether such experiments have improved by reporting information that enables the experiments to be replicated and the reader can understand the experiment and validate results. Thus, this work empirically compares four approaches for quality evaluation of SE experiments in the context of Software Product Lines (SPL). In addition, we are interested on verifying the quality of reporting experiments in a well-discussed reuse technique as SPL. The Pearson technique supported the correlation between pairs of evaluation approaches. In addition, the T-Test and Mann-Whitney-Wilcoxon U test were applied to the samples to verify whether there was a difference in the quality of experiments when using an experimental template. Preliminary results show a strong positive correlation between them, the hypothesis tests confirmed there is such a difference in quality when using experimental template and the SPL experiments report more the planning phase than the analysis and interpretation phase. Based on our results, we provide initial evidence two approaches are the best to reporting SPL experiments.

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Section: Execution Preparationmentioning

confidence: 99%

Comparing Approaches for Quality Evaluation of Software Engineering Experiments: An Empirical Study on Software Product Line Experiments

Furtado¹,

Vignando²,

França³

et al. 2019

Journal of Computer Science

View full text Add to dashboard Cite

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“…The approach comprises of two complementary parts: (1) an update viewpoint that defines the conventions for constructing and using architecture views to deal with multiple update concerns; and (2) a supporting framework that provides an extensible infrastructure supporting integrators of a SPL. The approach has been empirically validated for live updates of products derived from an industrial SPL for logistic systems [26].…”

Section: Existing Approaches To Support Runtime Changementioning

confidence: 99%

Design for Sustainability = Runtime Adaptation ∪ Evolution

Weyns

Caporuscio

Vogel

et al. 2015

Proceedings of the 2015 European Conference on Software Architecture Workshops

Self Cite

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Continuous change changes everything; it introduces various uncertainties, which may harm the sustainability of software systems. We argue that integrating runtime adaptation and evolution is crucial for the sustainability of software systems. Realising this integration calls for a radical change in the way software is developed and operated. Our position is that we need to Design for Sustainability. To that end, we present: (i) the AdEpS model (Adaptation and Evolution processes for Sustainability) to handle and mitigate uncertainties by means of integrating runtime adaptation and evolution, and (ii) a set of engineering principles to design software systems that facilitate the application of the AdEpS model to build sustainable software.

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