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Its complexity comes from the intricacy of the intelligent applications, the heterogeneity of the data sources, and the constant changes in the context. This study proposes a conceptual framework for achieving automated software evolution, emphasizing the importance of multimodality learning. A Selective Sequential Scope Model (3\u00a0S) model is developed based on the conceptual framework, and it can be used to categorize existing and future research when it covers different SE phases and multimodal learning tasks. This research is a preliminary step toward the blueprint of a higher-level ASEv. The proposed conceptual framework can act as a practical guideline for practitioners to prepare themselves for diving into this area. Although the study is about intelligent applications, the framework and analysis methods may be adapted for other types of software as AI brings more intelligence into their life cycles.<\/jats:p>","DOI":"10.1007\/s11334-024-00591-0","type":"journal-article","created":{"date-parts":[[2024,11,10]],"date-time":"2024-11-10T07:48:14Z","timestamp":1731224894000},"page":"1091-1105","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Multimodal conceptual framework to achieve automated software evolution for context-rich intelligent applications"],"prefix":"10.1007","volume":"21","author":[{"given":"Songhui","family":"Yue","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,10]]},"reference":[{"key":"591_CR1","doi-asserted-by":"crossref","unstructured":"Affenzeller M, Burlacu B, Dorfer V, Dorl S, Halmerbauer G, K\u00f6nigswieser T, Kommenda M, Vetter J, Winkler S (2020) White box vs. black box modeling: On the performance of deep learning, random forests, and symbolic regression in solving regression problems. 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