Deep learning based models for software effort estimation using story points in agile environments
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In the era of agile software development methodologies, traditional planning and software effort estimation methods are replaced to meet customer’s satisfaction in agile environments. However, software effort estimation remains a challenge. Although teams have achieved better accuracy in estimating story points effort required to implement user stories or issues, these estimations mostly rely on subjective assessments, leading to inaccuracy and impacting software project delivery. Some researchers are pointing good results by the adoption of deep learning to address this issue. Given the foregoing, this study proposes deep learning-based models for story points estimation in agile projects. Different algorithms are proposed and trained over a large dataset for story points estimation made by 16 open-source projects. In addition, we take advantage of natural language processing techniques to excavate better features from the software requirements written as user stories.
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http://purl.org/coar/resource_type/c_1843
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en