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.
BibTeX
@inproceedings{Kienzle-al:ASE22,
author = {J{\"{o}}rg Kienzle and
Beno{\^{\i}}t Combemale and
Gunter Mussbacher and
Omar Alam and
Francis Bordeleau and
Lola Burgue{\~{n}}o and
Gregor Engels and
Jessie Galasso and
Jean{-}Marc J{\'{e}}z{\'{e}}quel and
Bettina Kemme and
S{\'{e}}bastien Mosser and
Houari A. Sahraoui and
Maximilian Schiedermeier and
Eugene Syriani},
title = {Global Decision Making Over Deep Variability in {Feedback-Driven} Software Development},
booktitle = {ASE},
pages = {178:1--178:6},
publisher = {{ACM}},
year = {2022},
}