[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124086-en":3,"doc-seo-124086-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124086,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","A Framework for Early Detection of Agile Software Development Project Failures using Machine Learning Algorithms - Article","Agile project failure early identification reduces risk and supports better delivery outcomes. This study proposes an early detection framework that trains and tests multiple machine learning models, using a dataset of 13,238 observations from 12 software companies with 15 project-performance variables. Models include decision tree, bagging classifier, weighted bagging classifier, random forest classifier, weighted random forest classifier, decision tree estimator, and bagging estimator. Testing accuracy ranges between 45% and 55%, and feature/algorithm refinement did not yield significant improvement. Results indicate the need for further refinement to improve accuracy and reliability in detecting potential failures during the Agile lifecycle.","International Journal of Human  \nComputing Studies e-ISSN:2615-8159  \np-ISSN: 2615-1898  \nVolume: 7 Issue: 1 | January 2025  \n[https://journals.researchparks.org/index.php/IJHCS](https://journals.researchparks.org/index.php/IJHCS)  \nArticle  \nA Framework for Early Detection of Agile Software Development Project Failures using Machine Learning Algorithms  \nDomaka N. Nanwin1, Agaji Iorshase2, Ogala, Emmanuel3, Gbaden, Tivlumun4  \nCitation: Nanwin, D. N. A Framework for Early Detection of Agile Software Development Project Failures using Machine  \nLearning Algorithms.  \nInternational Journal of Human Computing Studies 2025, 7 (1), 7- 18.  \nReceived: 01th March 2025  \nRevised: 02th March 2025  \nAccepted: 03th March 2025  \nPublished: 05th March 2025  \nCopyright: © 2024 by the authors. Submitted for open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license  \n([https://creativecommons.org/l](https://creativecommons.org/l)[icenses/by/4.0/](icenses/by/4.0/))  \n1. Computer Science Department, Faculty of Natural and Applied Sciences, Ignatius Ajuru University of Education, Rumuolumeni, Port Harcourt, Rivers State, Nigeria  \n2. Department of Computer Science, Joseph Sarwuan Tarka University Makurdi, Benue State, Nigeria  \n3. Department of Computer Science, Joseph Sarwuan Tarka University Makurdi, Benue State, Nigeria  \n4. Department of Computer Science, Joseph Sarwuan Tarka University Makurdi, Benue State, Nigeria  \n* [Correspondence: ](Correspondence: kakusman@yahoo.com)[kakusman@yahoo.com](Correspondence: kakusman@yahoo.com)  \nAbstract: In the realm of software development, the early identification of project failures is crucial for ensuring project success and minimizing risks. This study developed an early detection framework using various machine learning models to anticipate potential failures. Agile software projects were used for the study. The framework employed a range of machine learning models including decision tree, bagging classifier, weighted bagging classifier, random forest classifier, weighted random forest classifier, decision tree estimator, and bagging estimator. These models are trained and tested using a dataset comprising 13,238 observations from 12 different software companies, each with 15 variables relevant to project performance and outcomes. Initial training of the different models yielded promising results, with performance ranging between 45% to 55% accuracy during testing. Despite attempts to enhance the model's performance, including refinement of features and algorithms, there were no significant improvements observed. The evaluation results highlight the need for further refinement and optimization of the models used in the framework. In conclusion, while the decision tree classifier, bagging classifier, and random forest exhibited outstanding performance in the trainingresults, the overallevaluation suggests that more work is required to improve the effectiveness of the early detection framework for Agile software project failures. Further research and refinement of the models are necessary to enhance accuracy and reliability in identifying potential project failures early in the Agile software development lifecycle.  \nKeywords: Agile, Framework, Software, Detection, Project Failure, Machine Learning, Software Development.  \n1. Introduction  \nIn the dynamic landscape of software development, Agile methodologies have emerged as a popular approach due to their flexibility and adaptability. Despite its advantages, Agile projects are not immune to failure. According to Osegi et al. (2018) recognizing the importance of early intervention, there is need to presents an efficient framework tailored for the early detection of failures for Agile software development projects which will empower teams with proactive measures to mitigate risks, optimize performance, and ultimately enhance project success rates. A structure placed on the creation of a software product","cbCaibsWFNtrXaom","https://ap.wps.com/l/cbCaibsWFNtrXaom","pdf",466766,1,12,"English","en",105,"# Introduction\n## Background and motivation\n## Challenges in existing approaches\n# Proposed framework and machine learning approach\n## Model selection and ensembles\n## Dataset description and training/testing setup\n# Experimental results and discussion\n## Performance evaluation\n## Feature and algorithm refinement\n# Conclusion and future work","[{\"question\":\"What problem does the framework address in Agile software projects?\",\"answer\":\"It targets early identification of potential Agile software development project failures to reduce risk and support proactive mitigation.\"},{\"question\":\"Which machine learning models are used in the proposed framework?\",\"answer\":\"The framework evaluates decision tree, bagging classifier, weighted bagging classifier, random forest classifier, weighted random forest classifier, decision tree estimator, and bagging estimator.\"},{\"question\":\"How effective were the models during testing?\",\"answer\":\"Testing performance ranged between 45% and 55% accuracy, and attempts to improve results through refinement did not show significant gains.\"}]","A Framework for Early Detection of Agile Software Development Project Failures using Machine Learning Algorithms - 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