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

TL;DR

This paper proposes a comprehensive multimodal framework for automated software evolution in context-rich intelligent applications, integrating all development phases to address complexity and heterogeneity.

Contribution

It introduces a novel conceptual framework and the 3S model for categorizing research, advancing the understanding of automated software evolution in intelligent systems.

Findings

Developed the 3S model for research categorization

Proposed a conceptual framework for ASEv in intelligent applications

Framework serves as a practical guideline for practitioners

Abstract

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.