User　stories　are　widely　adopted　in　agile　development.　Generally,　user　stories　are　written　by　users　or　customers　to　describe　their　needs　for　the　to-be　software　system.　User　stories　are　written　in　structured　natural　languages,　which　may　suffer　from　ambiguity　and　inconsistency.　These　user　story　defects　make　it　difficult　for　agile　development　teams　to　understand　requirements　and　make　development　plans.　User　story　quality　assessment　has　been　investigated　for　years　to　mitigate　this　problem.　Most　of　the　current　works　focus　on　grammatical　defect　checking　of　a　user　story,　but　lack　semantic　defect　checking　between　user　stories.　In　this　paper,　we　propose　a　multi-dimensional　user　story　quality　assessment　framework,　which　is　an　ongoing　study.　Specifically,　our　proposal　considers　three　dimensions,　including　completeness,　testability,　and　consistency.　11　quality　criteria　are　used　to　observe　user　story　quality.　Our　method　combines　typical　NLP　techniques　and　a　novel　iStar　modeling-based　analysis　to　discover　defects　in　user　stories　from　the　three　dimensions.　©　2022　Copyright　for　this　paper　by　its　authors.　Use　permitted　under　Creative　Commons　License　Attribution　4.0　International　(CC　BY　4.0)