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

Omar Alam
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  • PersonId : 978497
Loli Burgueño
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  • PersonId : 1081828
Gregor Engels
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  • PersonId : 1101210
Jessie Galasso
Sébastien Mosser
Houari Sahraoui
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  • PersonId : 864118

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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Dates and versions

hal-03770004 , version 1 (06-09-2022)

Identifiers

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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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