Verification of cyber-physical systems is challenging with the continuous dynamics of increasing complexity. Falsification has been considered as a promising pragmatic approach by using optimization techniques to search for input signals that lead to violation of a quantified formal specification. However, current falsification methods run a search every time from scratch given any variation in the target model. We propose a learning-based method that builds a falsifier for a family of models with a preliminary learning process. We adapt reinforcement learning techniques for this purpose. We evaluated the performance of the proposed method with a major falsification tool.

To facilitate understanding the architecture of a software system, we developed SArF Map technique that visualizes software architecture from feature and layer viewpoints using a city metaphor. SArF Map visualizes implicit software features using our previous study, SArF dependency-based software clustering algorithm. Since features are high-level abstraction units of software, a generated map can be directly used for high-level decision making such as reuse and also for communications between developers and non-developer stakeholders. In SArF Map, each feature is visualized as a city block, and classes in the feature are laid out as buildings reflecting their software layer. Relevance between features is represented as streets. Dependency links are visualized lucidly. Through open source and industrial case studies, we show that the architecture of the target systems can be easily overviewed and that the quality of their packaging designs can be quickly assessed.

Software clustering is one of the important techniques to comprehend software systems. However, presented techniques to date require human interactions to refine clustering results. In this paper, we proposed a novel dependency-based software clustering algorithm, SArF. SArF has two characteristics. First, SArF eliminates the need of the omnipresent-module-removing step which requires human interactions. Second, the objective of SArF is to gather relevant software features or functionalities into a cluster. To achieve them, we defined the Dedication score to infer the importance of dependencies and utilized Modularity Maximization to cluster weighted directed graphs. Two case studies and extensive comparative evaluations using open source and industrial systems show that SArF could successfully decompose the systems fitting to the authoritative decompositions from a feature viewpoint without any tailored setups and that SArF was superior to existing dependency-based software clustering studies. Besides, the case studies show that there exist measurable authoritativeness limits and that SArF nearly reached the limits.

Nowadays, structured data such as sales and business forms are stored in data warehouses for decision makers to use. Further, unstructured data such as emails, HTML texts, images, videos, and office documents are increasingly accumulated in personal computer storage due to spread of mailing, WWW, and word processing. Such unstructured data, or what we call multimedia documents, are larger in volume than structured data and precious as corporate assets as well. So we need a document warehouse as a software framework where multimedia documents are analyzed and managed for corporate-wide information sharing and reuse like a data warehouse for structured data. We describe a prototype document warehouse system, which supports management of simple and compound documents, keyword-based and content-based retrieval, rule-based classification, SOM-based clustering, and XML data query and view rules.

New multimedia applications, such as digital libraries and document warehousing, require next-generation database systems enabling users to efficiently and flexibly develop and execute such networked applications. To this end, we focus on development of a database system which enables flexible and efficient acquisition, storage, access and retrieval, and distribution and presentation of large amounts of heterogeneous media data. We propose a multimedia database system for networked multimedia applications, based on an OODB model extended with agents. We describe an early prototype multimedia database system to verify the proposed approach, which supports multimedia scripts, keyword-based and content-based view retrieval with QoS control, self-organizing map-based clustering and WWW integration.

New multimedia applications have emerged on top of information infrastructures, such as on-demand services, digital libraries and museums, online shopping and document management, which require new databases. That is, next-generation database systems must enable users to efficiently and flexibly develop and execute such advanced multimedia applications. We focus on development of a database system which enables flexible and efficient acquisition, storage, access and retrieval, and distribution and presentation of large amounts of heterogeneous media data. We take an approach based on an object-oriented database, which is more suitable for the description of media structures and operations than a traditional relational database. We extend the object-oriented approach by providing temporal and spatial operators, and control of distributing computing and QOS (quality of service). In this paper, we describe a multimedia data model and its efficient implementation.