A Data-to-Product Multimodal Conceptual Framework to Achieve Automated Software Evolution for Context-rich Intelligent Applications

With the advancements of Artifical Intelligence (AI) and Natural Language Processing (NLP) in the past decades, especially the rising of Large Language Model (LLM) and multimodality learning, softwrare engineering fields welcome AI techniques to be employed to every aspects of software cycles. Meanwhile, the research of intelligent applications has continuously been a hotspot (Zhao et al., 2021) because of the increasing amount of data of multimodalities generated in various domains. This type of software is designed to adapt to constantly changing scenarios of rich context (Zhao et al., 2021; Yue and Smith, 2021), and some examples are listed in part C of figure 1. One primary characteristic of those applications is that a great portion of their system behaviors is learned from continuous interaction with the users and environment involving detection and analysis of states and activities (Tzafestas, 2012; Yang and Newman, 2013; Cassavia et al., 2017), unlike applications of banking or insurance with more matured and stable business logic. The rapid evolution of hardware and software wheels bring more capabilities to intelligent applications meanwhile making the creation and maintenance of that software more intricate (Chu et al., 2021; Zheng et al., 2023), both fields of software engineering and intelligent applications are eager for breakthroughs in higher-level automation (HLA) - collaboratively resolving the challenges by benefiting from AI techniques.