{"title":"软件程序员管理:一种机器学习和人机交互框架,用于优化任务分配","authors":"Harry Raymond Joseph","doi":"10.1145/2635868.2661684","DOIUrl":null,"url":null,"abstract":"This paper attempts optimal task assignment at the enterprise-level by assigning complexity metrics to the programming tasks and predicting task completion times for each of these tasks based on a machine learning framework that factors in programmer attributes. The framework also considers real-time programmer state by using a simple EEG device to detect programmer mood. A final task assignment is made using a PDTS solver.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Software programmer management: a machine learning and human computer interaction framework for optimal task assignment\",\"authors\":\"Harry Raymond Joseph\",\"doi\":\"10.1145/2635868.2661684\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper attempts optimal task assignment at the enterprise-level by assigning complexity metrics to the programming tasks and predicting task completion times for each of these tasks based on a machine learning framework that factors in programmer attributes. The framework also considers real-time programmer state by using a simple EEG device to detect programmer mood. A final task assignment is made using a PDTS solver.\",\"PeriodicalId\":250543,\"journal\":{\"name\":\"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2635868.2661684\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2635868.2661684","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Software programmer management: a machine learning and human computer interaction framework for optimal task assignment
This paper attempts optimal task assignment at the enterprise-level by assigning complexity metrics to the programming tasks and predicting task completion times for each of these tasks based on a machine learning framework that factors in programmer attributes. The framework also considers real-time programmer state by using a simple EEG device to detect programmer mood. A final task assignment is made using a PDTS solver.