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Title EFFORT ESTIMATION IN AGILE SOFTWARE DEVELOPMENT USING ENSEMBLE LEARNING MODEL

Author(s) Habiba Sattar

Abstract In the domain of software development, effort estimating is an essential procedure that entails projecting the size and schedule of a particular project. It becomes necessary to create an estimate before beginning any software project. Obtaining the required approvals and evaluating the project depends on this preliminary assessment. The importance of this procedure cannot be emphasized since a project's success or failure is solely dependent on how precisely and accurately effort is estimated. There are various cost and effort methods and techniques. These techniques have been utilized to construct several effort estimation models that are used in the software development process in the traditional model. This research explores the application of ensemble learning techniques, specifically stacking, to enhance the accuracy of effort estimation in Agile environments. Stacking involves combining multiple diverse base estimators to create a meta-estimator that outperforms individual models. This study includes a crucial step for gathering datasets because old dataset size is small and old. The proposed approach is assessed using real-world Agile project datasets, proving the advantages of the stacking model over agile software development estimation techniques.

Type Thesis/Dissertation

Faculty Engineering and Computer Science

Department Engineering

Language English

Publication Date 2025-03-18

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