In today’s world, services are essential in daily life, and identifying each person’s unique needs is key to creating a human-centered society. Traditional research has used machine learning to recommend services based on user behavior logs without directly detecting individual needs. This study introduces a system called Needs Companion, which automatically detects individual service needs, laying the groundwork for accurate needs sensing. The system defines a needs data model based on the 6W1H framework, uses virtual agents for needs elicitation, and applies large language models (LLMs) to analyze and automatically extract needs. Experiments showed that the system could detect needs accurately and quickly. This research provides interpretable data for personalized services and contributes to fields like machine learning, human-centered design, and requirements engineering.

Software upcycling, a form of software reuse, is a concept that efficiently generates novel, innovative, and value-added development projects by utilizing knowledge extracted from past projects. However, how to integrate the materials derived from these projects for upcycling remains uncertain. This study defines a systematic model for upcycling cases and develops the Sharing Upcycling Cases with Context and Evaluation for Efficient Software Development (SUCCEED) system to support the implementation of new upcycling initiatives by effectively sharing cases within the organization. To ascertain the efficacy of upcycling within our proposed model and system, we formulated three research questions and conducted two distinct experiments. Through surveys, we identified motivations and characteristics of shared upcycling-relevant development cases. Development tasks were divided into groups, those that employed the SUCCEED system and those that did not, in order to discern the enhancements brought about by upcycling. As a result of this research, we accomplished a comprehensive structuring of both technical and experiential knowledge beneficial for development, a feat previously unrealizable through conventional software reuse, and successfully realized reuse in a proactive and closed environment through construction of the wisdom of crowds for upcycling cases. Consequently, it becomes possible to systematically perform software upcycling by leveraging knowledge from existing projects for streamlining of software development.

In this study, aimed at achieving Society 5.0, we explore efficient development methods for services tailored to individual user needs. The diversification of user demographics has led to swiftly changing service demands, increasing the necessity for personalized adaptation. However, developing services tailored to individual users requires specialized knowledge and incurs high costs. This research focuses on developing technologies that enable service development through a concrete understanding of user needs. Utilizing an interactive needs extraction system with a virtual agent (VA) developed in prior studies, along with the Sharing Upcycling Cases with Context and Evaluation for Efficient Software Development (SUCCEED) system, we aim to automate the extraction of user needs and provide developers with relevant insights. Prior research has found it challenging to grasp the outline of services to be developed from user statements. However, a new method using large language models (LLM) has enabled the automatic extraction of novel service needs. Experiments have shown the potential to automatically provide development insights for 67% of ideal needs.

Rule-based systems, which are the typical technology used to realize context-aware services, have been independently implemented in various smart services. The challenges of these systems are the versatility of action, looseness, and the coding that is needed to describe the conditional branches. The purpose of this study was to support the realization of service coordination and smart services using context-aware technology by converting rule-based systems into services. In the proposed method, we designed and implemented the architecture of a new service: Unified Rule-Based Message Delivery Service (Uni-messe), which is an application-neutral rule management and evaluation service for rule-based systems. The core part of the Uni-messe proposal is the combination of a Pub/Sub and a rule-based system, and the proposal of a new event–condition–route (ECR) rule-based system. We applied Uni-messe to an audio information presentation system (ALPS) and indoor location sensing technology to construct concrete smart services, and then compared and evaluated the implementation to “if this then that” (IFTTT), which is a typical service coordination technology. Moreover, we analyzed the characteristics of other rule-based systems that have been serviced in previous studies and compared them to Uni-messe. This study shows that Uni-messe can provide services that simultaneously combine versatility, ease of conditional description, looseness, context independence, and user interface (UI), which cannot be achieved using conventional rule-based system services. By using Uni-messe, advanced heterogeneous distributed service coordination using rule-based systems and the construction of context-aware services can be performed easily.

In software maintenance and updating, the technical debt accumulated from past development has become a significant operating cost problem. One method to repay this debt is software upcycling, which converts project materials into valuable software assets. In this study, we examine case sharing system that summarizes upcycling achievements and ideas for efficient implementation of software upcycling. The core part of our proposal is the case data model for all upcycling materials, including source code and design documents. Moreover, we design the architecture of the case sharing system. By using the proposed system, it is expected to implement efficient upcycling based on past cases and to chain upcycling through a positive cycle of inspiration of innovative ideas.