While AI is extensively transforming Software Engineering (SE) fields, SE is still in need of a framework to overall consider all phases to facilitate Automated Software Evolution (ASEv), particularly for intelligent applications that are context-rich, instead of conquering each division independently. 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 (3S) 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.

翻译：尽管人工智能正在深刻变革软件工程领域，软件工程仍缺乏一个能够全面考虑所有阶段以促进自动化软件演进的框架，特别是针对上下文丰富的智能应用，而非独立攻克各个分支。其复杂性源于智能应用的错综复杂、数据源的异构性以及上下文的持续变化。本研究提出了一种实现自动化软件演进的概念框架，强调了多模态学习的重要性。基于该概念框架，我们开发了选择性序贯范围模型（3S模型），该模型可对覆盖不同软件工程阶段与多模态学习任务的现有及未来研究进行分类。本研究是迈向更高级别自动化软件演进蓝图的初步探索。所提出的概念框架可作为从业者深入该领域的实践指南。尽管本研究聚焦于智能应用，但随着人工智能为其生命周期注入更多智能元素，该框架与分析方法或可适用于其他类型的软件。