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Global Decision Making Over Deep Variability in Feedback-Driven Software Development

Abstract : To succeed with the development of modern software, organizations must have the agility to adapt faster to constantly evolving environments to deliver more reliable and optimized solutions that can be adapted to the needs and environments of their stakeholders including users, customers, business, development, and IT. However, stakeholders do not have sufficient automated support for global decision making, considering the increasing variability of the solution space, the frequent lack of explicit representation of its associated variability and decision points, and the uncertainty of the impact of decisions on stakeholders and the solution space. This leads to an ad-hoc decision making process that is slow, error-prone, and often favors local knowledge over global, organization-wide objectives. The Multi-Plane Models and Data (MP-MODA) framework explicitly represents and manages variability, impacts, and decision points. It enables automation and tool support in aid of a multi-criteria decision making process involving different stakeholders within a feedback-driven software development process where feedback cycles aim to reduce uncertainty. We present the conceptual structure of the framework, discuss its potential benefits, and enumerate key challenges related to tool supported automation and analysis within MP-MODA.
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https://hal.inria.fr/hal-03770004
Contributor : Benoit Combemale Connect in order to contact the contributor
Submitted on : Tuesday, September 6, 2022 - 9:28:28 AM
Last modification on : Tuesday, September 20, 2022 - 9:21:29 AM

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ase22-132.pdf
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Jörg Kienzle, Benoit Combemale, Gunter Mussbacher, Omar Alam, Francis Bordeleau, et al.. Global Decision Making Over Deep Variability in Feedback-Driven Software Development. ASE 2022 - 37th IEEE/ACM International Conference on Automated Software Engineering, Oct 2022, Rochester, MI, United States. pp.1-6, ⟨10.1145/3551349.3559551⟩. ⟨hal-03770004⟩

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