Prediction based cost estimation technique in agile development. (January 2023) - Butt, Shariq Aziz; Ercan, Tuncay; Binsawad, Muhammad; Ariza-Colpas, Paola-Patricia; Diaz-Martinez, Jorge; Piñeres-Espitia, Gabriel; De-La-Hoz-Franco, Emiro; Melo, Marlon Alberto Pineres; Ortega, Roberto Morales; De-La-Hoz-Hernández, Juan-David
Highlights: `Agile cost estimation in scrum development. Cost estimation methods in scrum and limitations. Suggested a cost estimation model. Abstract: Agile has been invented to improve and overcome the deficiencies of efficient software development. At present, the agile model is used in software development vastly due to its support to both developers and clients resourcefully. Agile methodology increases the interaction between the developer and client to make the software product defect-free. The agile model is getting to be a well-known life cycle model because of its particular features and most owing is to allow changes at any level of the project from the product owner. However, on other hand, this novel feature is a disadvantage of the agile model due to frequent change requests from the client has increased the cost and time. To overcome cost and time estimation issues different cost estimation techniques are being used in agile development but no one is pertinent for accurate estimation. Therefore, this study has proposed a cost estimation technique. The proposed estimation technique is predictions-based and has different categorizations of projects based on user stories complexities and the developer's expertise. We have applied the suggested technique to ongoing projects to find the results and effectiveness. We have used two projects with different sizes and user stories. Both projects have different modules and developers with different expertise. We have used the proposed estimation technique on projects and done a survey session with the teams. This survey session's main objective is to reveal the statistical findings of the proposed solution. We have designed the 12 hypotheses for statistical analysis.