Linear Software Models for Well-composed Systems

Iaakov Exman

Abstract

Modularity is essential for automatic composition of real software systems from ready-made components. But given ready-made components do not necessarily correspond exactly to the units and functionality of designed software system architecture modules. One needs a neat composition procedure that guarantees the necessary and sufficient components to provide required units. Linear Software Models are rigorous theoretical standards subsuming modularity. The Linear-Reducible model is proposed as a model of well-composed software systems, above and beyond software variability. Indeed, case studies of representative systems recognized as well-composed, be they small, intermediate building blocks or large scale, are shown to be Linear-Reducible. The paper lays down theoretical foundations – upon exact linear independence and reducible matrix concepts – providing new precise meanings to familiar modularity ideas, such as the single responsibility theorem. The theory uses a Modularity Matrix – linking independent software structors to composable software functionals in a Linear Model.

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Paper Citation


in Harvard Style

Exman I. (2012). Linear Software Models for Well-composed Systems . In Proceedings of the 7th International Conference on Software Paradigm Trends - Volume 1: ICSOFT, ISBN 978-989-8565-19-8, pages 92-101. DOI: 10.5220/0004085500920101


in Bibtex Style

@conference{icsoft12,
author={Iaakov Exman},
title={Linear Software Models for Well-composed Systems},
booktitle={Proceedings of the 7th International Conference on Software Paradigm Trends - Volume 1: ICSOFT,},
year={2012},
pages={92-101},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004085500920101},
isbn={978-989-8565-19-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Software Paradigm Trends - Volume 1: ICSOFT,
TI - Linear Software Models for Well-composed Systems
SN - 978-989-8565-19-8
AU - Exman I.
PY - 2012
SP - 92
EP - 101
DO - 10.5220/0004085500920101