Jonas Kirchhoff, Nils Weidmann, Stefan Sauer, G. Engels
{"title":"制造企业中低代码应用程序的情景开发","authors":"Jonas Kirchhoff, Nils Weidmann, Stefan Sauer, G. Engels","doi":"10.1145/3550356.3561560","DOIUrl":null,"url":null,"abstract":"Companies show an increasing interest in low-code development platforms to facilitate application development by domain experts without sophisticated software development knowledge. Thus, companies aim for a more efficient development of more effective applications since domain experts as so-called citizen developers are no longer limited by the availability and domain knowledge of trained software developers. Nevertheless, efficiency and effectiveness of application development is traditionally also largely influenced by the use of a suitable software development method. Domain experts are, however, not trained in software development methods. This introduces a risk of domain experts creating unusable applications or exceeding the designated time frame of a project (or both). In this paper, we therefore propose an initial version of a situational software development method which supports domain experts in manufacturing companies during the low-code development of applications. The method can be tailored based on situational factors, considering application requirements, features of the used low-code development platform, and characteristics of the development team. We also present feedback corroborating the usefulness of our method and future extension points based on expert interviews.","PeriodicalId":182662,"journal":{"name":"Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Situational development of low-code applications in manufacturing companies\",\"authors\":\"Jonas Kirchhoff, Nils Weidmann, Stefan Sauer, G. Engels\",\"doi\":\"10.1145/3550356.3561560\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Companies show an increasing interest in low-code development platforms to facilitate application development by domain experts without sophisticated software development knowledge. Thus, companies aim for a more efficient development of more effective applications since domain experts as so-called citizen developers are no longer limited by the availability and domain knowledge of trained software developers. Nevertheless, efficiency and effectiveness of application development is traditionally also largely influenced by the use of a suitable software development method. Domain experts are, however, not trained in software development methods. This introduces a risk of domain experts creating unusable applications or exceeding the designated time frame of a project (or both). In this paper, we therefore propose an initial version of a situational software development method which supports domain experts in manufacturing companies during the low-code development of applications. The method can be tailored based on situational factors, considering application requirements, features of the used low-code development platform, and characteristics of the development team. We also present feedback corroborating the usefulness of our method and future extension points based on expert interviews.\",\"PeriodicalId\":182662,\"journal\":{\"name\":\"Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3550356.3561560\",\"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 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3550356.3561560","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Situational development of low-code applications in manufacturing companies
Companies show an increasing interest in low-code development platforms to facilitate application development by domain experts without sophisticated software development knowledge. Thus, companies aim for a more efficient development of more effective applications since domain experts as so-called citizen developers are no longer limited by the availability and domain knowledge of trained software developers. Nevertheless, efficiency and effectiveness of application development is traditionally also largely influenced by the use of a suitable software development method. Domain experts are, however, not trained in software development methods. This introduces a risk of domain experts creating unusable applications or exceeding the designated time frame of a project (or both). In this paper, we therefore propose an initial version of a situational software development method which supports domain experts in manufacturing companies during the low-code development of applications. The method can be tailored based on situational factors, considering application requirements, features of the used low-code development platform, and characteristics of the development team. We also present feedback corroborating the usefulness of our method and future extension points based on expert interviews.