@inproceedings{
 title = {Training and Re-using Human Experience: A Recommender for More Accurate Cost Estimates in Project Planning},
 type = {inproceedings},
 year = {2018},
 keywords = {Case-based Reasoning,Case-based reasoning,Effort Estimation,Effort estimation,Experience Management,Experience management,Machine Learning,Machine learning},
 pages = {52-62},
 volume = {3},
 websites = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006893200520062},
 publisher = {SCITEPRESS - Science and Technology Publications},
 institution = {INSTICC},
 id = {fbb1c620-b5d5-3a27-ae0c-d7f74f580b0d},
 created = {2022-08-22T10:15:20.969Z},
 file_attached = {true},
 profile_id = {e4ed3238-8302-3280-bd05-0b185874fb43},
 last_modified = {2022-08-22T12:28:07.781Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {kmis18},
 source_type = {conference},
 folder_uuids = {b35e5b0c-63da-4577-ad8a-01a619ce7c0b},
 private_publication = {false},
 abstract = {In many industries, companies deliver customised solutions to their (business) customers within projects. Estimating the human effort involved in such projects is a difficult task and underestimating efforts can lead to non-billable hours, i.e. financial loss on the side of the solution provider. Previous work in this area has focused on automatic estimation of the cost of software projects and has largely ignored the interaction between automated estimation support and human project leads. Our main hypothesis is that an adequate design of such interaction will increase the acceptance of automatically derived estimates and that it will allow for a fruitful combination of data-driven insights and human experience. We therefore build a recommender that is applicable beyond software projects and that suggests job positions to be added to projects and estimated effort of such positions. The recommender is based on the analysis of similar cases (case-based reasoning), “explains” derived si milarities and allows human intervention to manually adjust the outcomes. Our experiments show that recommendations were considered helpful and that the ability of the system to explain and adjust these recommendations was heavily used and increased the trust in the system. We conjecture that the interaction of project leads with the system will help to further improve the accuracy of recommendations and the support of human learning in the future.},
 bibtype = {inproceedings},
 author = {Von Rohr, Christian Rudolf and Witschel, Hans Friedrich and Martin, Andreas},
 doi = {10.5220/0006893200520062},
 booktitle = {Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management}
}

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Estimating the human effort involved in such projects is a difficult task and underestimating efforts can lead to non-billable hours, i.e. financial loss on the side of the solution provider. Previous work in this area has focused on automatic estimation of the cost of software projects and has largely ignored the interaction between automated estimation support and human project leads. Our main hypothesis is that an adequate design of such interaction will increase the acceptance of automatically derived estimates and that it will allow for a fruitful combination of data-driven insights and human experience. We therefore build a recommender that is applicable beyond software projects and that suggests job positions to be added to projects and estimated effort of such positions. The recommender is based on the analysis of similar cases (case-based reasoning), “explains” derived si milarities and allows human intervention to manually adjust the outcomes. Our experiments show that recommendations were considered helpful and that the ability of the system to explain and adjust these recommendations was heavily used and increased the trust in the system. We conjecture that the interaction of project leads with the system will help to further improve the accuracy of recommendations and the support of human learning in the future.","bibtype":"inproceedings","author":"Von Rohr, Christian Rudolf and Witschel, Hans Friedrich and Martin, Andreas","doi":"10.5220/0006893200520062","booktitle":"Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","bibtex":"@inproceedings{\n title = {Training and Re-using Human Experience: A Recommender for More Accurate Cost Estimates in Project Planning},\n type = {inproceedings},\n year = {2018},\n keywords = {Case-based Reasoning,Case-based reasoning,Effort Estimation,Effort estimation,Experience Management,Experience management,Machine Learning,Machine learning},\n pages = {52-62},\n volume = {3},\n websites = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006893200520062},\n publisher = {SCITEPRESS - Science and Technology Publications},\n institution = {INSTICC},\n id = {fbb1c620-b5d5-3a27-ae0c-d7f74f580b0d},\n created = {2022-08-22T10:15:20.969Z},\n file_attached = {true},\n profile_id = {e4ed3238-8302-3280-bd05-0b185874fb43},\n last_modified = {2022-08-22T12:28:07.781Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {kmis18},\n source_type = {conference},\n folder_uuids = {b35e5b0c-63da-4577-ad8a-01a619ce7c0b},\n private_publication = {false},\n abstract = {In many industries, companies deliver customised solutions to their (business) customers within projects. Estimating the human effort involved in such projects is a difficult task and underestimating efforts can lead to non-billable hours, i.e. financial loss on the side of the solution provider. Previous work in this area has focused on automatic estimation of the cost of software projects and has largely ignored the interaction between automated estimation support and human project leads. Our main hypothesis is that an adequate design of such interaction will increase the acceptance of automatically derived estimates and that it will allow for a fruitful combination of data-driven insights and human experience. We therefore build a recommender that is applicable beyond software projects and that suggests job positions to be added to projects and estimated effort of such positions. The recommender is based on the analysis of similar cases (case-based reasoning), “explains” derived si milarities and allows human intervention to manually adjust the outcomes. Our experiments show that recommendations were considered helpful and that the ability of the system to explain and adjust these recommendations was heavily used and increased the trust in the system. 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