Predicting effort for requirement changes during software development
In any software development life cycle, requirement and software changes are inevitable. One of the factors that influences the effectiveness of the change acceptance decision is the accuracy of the effort prediction for requirement changes. There are two current models that have been widely used to...
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| 主要な著者: | , , , |
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| フォーマット: | Conference or Workshop Item |
| 出版事項: |
Association for Computing Machinery
2016
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| 主題: | |
| オンライン･アクセス: | http://eprints.utm.my/id/eprint/72942/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007560310&doi=10.1145%2f3011077.3011096&partnerID=40&md5=71a31a4c59af54efaed1d54ef4f932a8 |
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| 要約: | In any software development life cycle, requirement and software changes are inevitable. One of the factors that influences the effectiveness of the change acceptance decision is the accuracy of the effort prediction for requirement changes. There are two current models that have been widely used to predict rework effort for requirement changes which are algorithmic and nonalgorithmic models. The algorithmic model is known for its formal and structural way of prediction and best suited for Traditional software development methodology. While nonalgorithmic model is widely adopted for Agile software development methodology of software projects due to its easiness and requires less work in term of effort predictability. Nevertheless, none of the existing effort prediction models for requirement changes are proven to suit both, Traditional and Agile software development methodology. Thus, this paper proposes an algorithmic-based effort prediction model for requirement changes that uses change impact analysis method which is applicable for both Traditional and Agile software development methodologies. The proposed model uses a current selected change impact analysis method for software development phase. The proposed model is evaluated through an extensive experimental validation using case study of six real Traditional and Agile methodologies software projects. The evaluation results confirmed a significance accuracy improvement of the proposed model over the existing approaches for both Traditional and Agile methodologies. |
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