The proliferation of diverse kinds of programming languages and platforms makes it a common need to have the same functionality implemented in different languages for different platforms, such as Java for Android applications and C# for Windows phone applications. Although versions of code written in different languages appear syntactically quite different from each other, they are intended to implement the same software and typically contain many code snippets that implement similar functionalities, which we call cross-language clones. When the version of code in one language evolves according to changing functionality requirements and/or bug fixes, its cross-language clones may also need be changed to maintain consistent implementations for the same functionality. Thus, it is needed to have automated ways to locate and track cross-language clones within the evolving software. In the literature, approaches for detecting cross-language clones are only for languages that share a common intermediate language (such as the .NET language family) because they are built on techniques for detecting single-language clones. To extend the capability of cross-language clone detection to more diverse kinds of languages, we propose a novel automated approach, CLCMiner, without the need of an intermediate language. It mines such clones from revision histories, based on our assumption that revisions to different versions of code implemented in different languages may naturally reflect how programmers change cross-language clones in practice, and that similarities among the revisions (referred to as clones in diffs or diff clones) may indicate actual similar code. We have implemented a prototype and applied it to ten open source projects implementations in both Java and C#. The reported clones that occur in revision histories are of high precisions (89% on average) and recalls (95% on average). Compared with token-based code clone detection tools that can treat code as plain texts, our tool can detect significantly more cross-language clones. All the evaluation results demonstrate the feasibility of revision-history based techniques for detecting cross-language clones without intermediates and point to promising future work.

As one of the popular social media that many people turn to in recent years, collaborative encyclopedia Wikipedia provides information in a more “Neutral Point of View” way than others. Towards this core principle, plenty of efforts have been put into collaborative contribution and editing. The trajectories of how such collaboration appears by revisions are valuable for group dynamics and social media research, which suggest that we should extract the underlying derivation relationships among revisions from chronologically-sorted revision history in a precise way. In this paper, we propose a revision graph extraction method based on supergram decomposition in the document collection of near-duplicates. The plain text of revisions would be measured by its frequency distribution of supergram, which is the variable-length token sequence that keeps the same through revisions. We show that this method can effectively perform the task than existing methods.

Change history in project revisions provides helpful information on handling bugs. Existing studies on predicting bugs mainly focus on resulting bug patterns, not these change patterns. When a code hunk is copied onto several files, the set of original and copied hunks often need to be consistently maintained. We assume that it is a normal state when all of hunks survive or die in a specific revision. When partial change occurs on some duplicated hunks, they are regarded as suspicious hunks. Based on these assumptions, suspicious cases can be predicted and the project's developers can be alerted. In this paper, we propose a practical approach to detect various change smells based on revision history and code hunk tracking. The change smells are suspicious change patterns that can result in potential bugs, such as partial death of hunks, missed refactoring or fix, backward or late change. To detect these change smells, three kinds of hunks – add, delete, and modify – are tracked and analyzed by an automated tool. Several visualized graphs for each type have been suggested to improve the applicability of the proposed technique. We also conducted experiments on large-scale open projects. The case study results show the applicability of the proposed approach.

In this paper, we propose an agent architecture for a combination of speculative computation and abduction. Speculative computation is a tentative computation when complete information for performing computation is not obtained. We use a default value to complement such incomplete information. Unlike usual default reasoning, the real value for the information can be obtained during the computation and the computation can be revised on the fly. In the previous work, we applied this technique to handling distributed problem solving under incomplete communication environments in the context of multi-agent systems and proposed correct procedures in abductive logic programming in terms of perfect model semantics. In the previous work, however, we regarded assumptions as defaults and used these assumptions for speculative computation. Thus, we could not perform hypothetical reasoning, that is, the original usage of abduction. In this paper, we extend our framework so that speculative computation and abduction can be both performed. As a result, our procedure becomes an extension of the abductive procedure developed by Kakas and Mancarella augmented by dynamic belief revision mechanism about outside world.

We introduce a method, so called FC method, for maintaining software resources, such as source codes and design documents, in consumer electronics products. Because a consumer electronics product is frequently and rapidly revised, software components in such product are also revised in the same way. However, it is not so easy for software engineers to follow the revision of the product because requirements changes for the product, including the changes of its functionalities and its hardware components, are largely independent of the structure of current software resources. FC method lets software engineers to restructure software resources, especially design documents, stepwise so as to follow the requirements changes for the product easily. We report an application of this method in our company to validate it. From the application, we can confirm that the quality of software was improved about in twice, and that efficiency of development process was also improved over four times.

This study presents a method that can be used to manage individual pieces of information in large scale distributed geographic information systems (GIS). In a distributed GIS, ordinary users usually cannot alter any of the contents on the server. The method in this study can be used to alter the content or add individual datums onto these types of non-write-permitted data sets. The authors have called it a 'Geographic Differential Script File' (GDSF). A client creates a GDSF, which contains private information that is to be added onto the served data. The client keeps this file on a local disk. When the user employs the data, he applies the differential script sequence onto the downloaded data in order to retrieve the information. GDSF is a collection of graphic operation commands which insert and delete objects as well as modify operations. GDSF also contains modifications of the attribute information of geographic entities. This method can also be used to revise information that is published on ROM media, e. g. CD-ROM or DVD-ROM, as well as in a distributed environment. In this paper, the method and results of applying it are presented.

In this paper, we present a unified model for dialogue understanding involving various sorts of ambiguities, such as lexical, syntactic, semantic, and plan ambiguities. This model is able to estimate and revise the most preferable interpretation of utterances as a dialogue progresses. The model's features successfully capture the dynamic nature of dialogue management. The model consists of two main portions: (1) an extension of first-order logic for maintaining multiple interpretations of ambiguous utterances in a dialogue; (2) a device which estimates and revises the most preferable interpretation from among these multiple interpretations. Since the model is logic-based, it provides a good basis for formulating a rational justification of its current interpretation, which is one of the most desirable aspects in generating helpful responses. These features (contained in our model) are extremely useful for interactive dialogue management.

Student contradictions are the essentials of concepts and knowledge acquisition processes of a student, in the course of tutoring. This paper presents a new perspective to represent student contradictions and a student modeling architecture to capture them. The formulation of a student modeling mechanism enables flexible decision making by using information obtained from students. A nonmonotonic and inductive student model inference system HSMIS has been developed and formulated to cope with modeling contradictions, which basically embodies advanced representation power, sufficiently high adaptability and generality. The HSMIS is evaluated and compared with other representative systems in order to demonstrate its effectiveness.