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Machine-learning (ML) software engineering design patterns encapsulate reusable solutions to commonly occurring problems within the given contexts of ML systems and software design. These ML patterns should help develop and maintain ML systems and software from the design perspective. However, to the best of our knowledge, there is no study on the practitioners’ insights on the use of ML patterns for design of their ML systems and software. Herein we report the preliminary results of a literature review and a questionnaire-based survey on ML system developers’ state-of-practices with concrete ML patterns.

Question-answering (QA) systems have recently shown impressive results in terms of accurately answering user questions in such situations as domain specific user questions. However, we have identified many real situations where QA systems must cope with not a single question-answering situation but rather a sequence of consecutive questions. In such cases, users often ask questions on the basis of the previous answer they have received, so the context of the questions changes on a certain level. The commonly used method to handle this problem when using a QA system is to append the current question to the previous question (append method). However, the append method is not designed to detect such context changes. To deal with such context changes, we have designed a hierarchical context supplementation QA System (HCSQ). The HCSQ handles consecutive questions by matching the current question with the hierarchical domain knowledge database structure of the previous answer and then supplements the context of the current question with the required keywords. We also show that our method can be further applied to the initial question to supplement omitted context. Experimental results show that our method substantially outperforms the state-of-the-art methods.

As the development of embedded systems grows in scale, it is becoming more important for engineers to share development documents such as requirements, design specifications and testing specifications, and to accurately circulate and understand the information necessary for development. Also, many defects that can be originated in the surface expression of the documents are reported through investigations of causes of defects in embedded systems development, In this paper, we highlight improper surface@expressions of Japanese documents, and define quality criteria and critiquing rules to detect problems such as ambiguous expressions or omissions of information. We also carry out visual quality inspections and evaluate detection performance, correlations and working time. Then, we verify the validity of the critiquing rules we have defined and apply them to the document critiquing tool to evaluate the quality of the actual documents used in the development of embedded systems. And we quantify the quality of these documents by automatically detecting improper expression. We also apply supplemental critiquing rules to the document critiquing tool for use by non-native speakers of Japanese, and verify its efficacy at improving the quality of Japanese documents created by foreigners.

We describe a new approach to modeling and analyzing software development documents that are typically written using conventional office applications. Our approach brings automation to content extraction, quality checking and measurement of massive document artifacts that tend to be handled by labor-intensive manual work in industry today. Rather than seeking an approach based on creation or rewriting of contents using more rigid, machine-friendly representations such as standardized formal models and restricted languages, we provide a method to deal with the diversity of document artifacts by making use of project-specific formalism that exists in target documents. We demonstrate that such project-specific formalism often tends to "naturally" exist at syntactic levels, and it is possible to define a "document model", a logical data representation gained by transformation rule from the physical, syntactic structure to the logical, semantic structure. With this transformation, various quality checking rules for completeness, consistency, traceability, etc., are realized by evaluating constraints for data items in the logical structure, and measurement of these quality aspects is automated. We developed a tool to allow a user to easily define document models and checking rules, and provide the insights on transformations when defining document models for various industry specification documents written in word processor files, spreadsheets and presentations. We also demonstrate the use of natural language processing can improve document modeling and quality checking by compensating for a weakness of formalism and applying analysis to specific parts of the target documents.

Efficient utilization of global resources is crucial for globally integrated enterprises to improve their business performance. Rising costs of language translation by professional translators are inhibiting the growth of global delivery of services from and to countries and geographies where most documents need to be translated. The quality levels of current machine translation engines are inadequate for such business processes, since translation errors may cause significant business problems. Human-quality translations are needed, and we need tools for enhancing these human translation efforts. We have developed an architecture and some tools to help people create translations more efficiently. The framework provides tools for interactively creating translations by aggregating the information necessary for translating the documents, for checking the quality of the source documents to be translated, and for effectively gathering translation-related resources from existing documents. We conducted several experiments to evaluate the performance of the tools, and confirmed that the framework and tools are valuable in assisting human translation work.

Natural language is the preferred form for writing use cases. While a few linguistic techniques exist that extract or validate structured information from unstructured natural language use case, they often cannot be extended beyond their primary language. Extending linguistic analysis and automated validation capabilities across multiple languages is necessary not only for widespread industrial adoption but it helps in analyzing a collection of multilingual use cases (quite frequent in multi-national projects) that need to be aggregated. We have published a UIMA (Unstructured Information Management Architecture) based linguistic engine for analyzing English use cases. In this paper, we report on extension of our linguistic technique to Japanese and effect of such an extension on the automated requirement validation suite.