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Mustafa Berk Duran

Researcher at McGill University

Publications -  8
Citations -  74

Mustafa Berk Duran is an academic researcher from McGill University. The author has contributed to research in topics: Reuse & Goal modeling. The author has an hindex of 5, co-authored 8 publications receiving 64 citations.

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Book ChapterDOI

On the Reuse of Goal Models

TL;DR: It is argued that reusable goal models must be specified either with real-life measurements if available or withrelative contributions, and a novel evaluation mechanism is presented that enables the reuse of goal models with relative contributions, while taking into account additional constraints on tasks in the goal model expressed with feature models.
Journal ArticleDOI

Investigation of feature run-time conflicts on goal model-based reuse

TL;DR: This paper presents a novel goal model evaluation mechanism for the selection of the most appropriate candidate, which takes into account additional configuration constraints express with feature models and run-time constraints expressed with workflow models that may affect theselection of reusable software artifacts.
Proceedings ArticleDOI

Evaluation of reusable concern-oriented goal models

TL;DR: Novel modeling constructs to enable the reuse of lower-level goal models in the context of Concern-Orientation are presented, existing propagation-based reasoning mechanisms of goal models for use in concern hierarchies are extended, and a proof-of-concept implementation of the novel modeling constructs and the extended reasoning mechanism is reported on.
Journal ArticleDOI

Reusability in goal modeling: A systematic literature review

TL;DR: How well existing goal modeling approaches support reusability with five desired characteristics of contextual and reusable goal models is evaluated and categorize these approaches based on language constructs for context modeling and connection to other modeling formalisms.
Proceedings ArticleDOI

Evaluation of Goal Models in Reuse Hierarchies with Delayed Decisions

TL;DR: The result of goal model evaluation is expanded from the typical single satisfaction value to a range of values that are still possible based on the current task selections, and the extended algorithm takes into account the delayed decisions and evaluates the best and worst possible results.