An Architectural Framework for Quality-Driven Adaptive Continuous Experimentation

Jiménez, Miguel; Rivera, Luis; Villegas, Norha M.; Tamura, Gabriel; Müller, Hausi A.; Bencomo, Nelly

doi:10.1109/rcose/ddree.2019.00012

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…They also argue that the optimality of adaptation decisions should be considered as a range of values, not just a single value at runtime. The quality-driven adaptive continuous experimentation approach in [12] aims to reduce development risks and operational costs through experiments at runtime to investigate variants in infrastructure configuration and architectural design.…”

Section: Related Workmentioning

confidence: 99%

Expressing the Adaptation Intent as a Sustainability Goal

Gerostathopoulos

Raibulet

Lago

2022

2022 IEEE/ACM 44th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)

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Adaptation and sustainability are two key challenges leading the development of software-systems nowadays. Adaptation denotes the capacity of a system to cope with variations and uncertainties at runtime in order to continue providing its functionalities with certain quality levels, notwithstanding change. But how can adaptation and its intent be expressed at design time so that to analyze its possible impact at runtime over a long period of time? To answer this question we look at adaptation from the sustainability point of view. Sustainability denotes the capacity of a system to both endure and preserve its function over time. We propose an approach which uses decision maps to make sustainability-driven decisions for adaptation in a systematic way. The proposed approach is illustrated through two self-adaptive exemplars as illustrative cases.

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Section: Related Workmentioning

confidence: 99%

Expressing the Adaptation Intent as a Sustainability Goal

Gerostathopoulos

Raibulet

Lago

2022

2022 IEEE/ACM 44th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)

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“…in Hoos 2012;Nair et al 2018) assume that the optimization is done before deployment during compilation in a controlled environment. Some recent work has moved the optimization to run time and studied the required software architecture and infrastructure for cyber-physical systems (Jiménez et al 2019;Gerostathopoulos et al 2018) and implications (Mattos et al 2017;Gerostathopoulos et al 2018) of this change.…”

Section: Automated Experimentation With Optimization Algorithmsmentioning

confidence: 99%

Data-driven software design with Constraint Oriented Multi-variate Bandit Optimization (COMBO)

Ros

Hammar²

2020

Empir Software Eng

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Context Software design in e-commerce can be improved with user data through controlled experiments (i.e. A/B tests) to better meet user needs. Machine learning-based algorithmic optimization techniques extends the approach to large number of variables to personalize software to different user needs. So far the optimization techniques has only been applied to optimize software of low complexity, such as colors and wordings of text. Objective In this paper, we introduce the COMBO toolkit with capability to model optimization variables and their relationship constraints specified through an embedded domain-specific language. The toolkit generates personalized software configurations for users as they arrive in the system, and the configurations improve over time in in relation to some given metric. COMBO has several implementations of machine learning algorithms and constraint solvers to optimize the model with user data by software developers without deep optimization knowledge. Method The toolkit was validated in a proof-of-concept by implementing two features that are relevant to Apptus, an e-commerce company that develops algorithms for web shops. The algorithmic performance was evaluated in simulations with realistic historic user data. Results The validation shows that the toolkit approach can model and improve relatively complex features with many types of variables and constraints, without causing noticeable delays for users. Conclusions We show that modeling software hierarchies in a formal model facilitates algorithmic optimization of more complex software. In this way, using COMBO, developers can make data-driven and personalized software products.

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Multi-valued logic in graph transformation theory and self-adaptive systems

Maximov

Ryvkin

2019

Ann Math Artif Intell

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An Architectural Framework for Quality-Driven Adaptive Continuous Experimentation

Cited by 5 publications

References 8 publications

Expressing the Adaptation Intent as a Sustainability Goal

Expressing the Adaptation Intent as a Sustainability Goal

Data-driven software design with Constraint Oriented Multi-variate Bandit Optimization (COMBO)

Multi-valued logic in graph transformation theory and self-adaptive systems

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