Automatic maintenance of category hierarchy

Category hierarchy is an abstraction mechanism for efficiently managing large-scale resources. In an open environment, a category hierarchy will inevitably become inappropriate for managing resources that constantly change with unpredictable pattern. An inappropriate category hierarchy will mislead the management of resources. The increasing dynamicity and scale of online resources increase the requirement of automatically maintaining category hierarchy. Previous studies about category hierarchy mainly focus on either the generation of category hierarchy or the classification of resources under a pre-defined category hierarchy. The automatic maintenance of category hierarchy has been neglected. Making abstraction among categories and measuring the similarity between categories are two basic behaviours to generate a category hierarchy. Humans are good at making abstraction but limited in ability to calculate the similarities between large-scale resources. Computing models are good at calculating the similarities between large-scale resources but limited in ability to make abstraction. To take both advantages of human view and computing ability, this paper proposes a two-phase approach to automatically maintaining category hierarchy within two scales by detecting the internal pattern change of categories. The global phase clusters resources to generate a reference category hierarchy and gets similarity between categories to detect inappropriate categories in the initial category hierarchy. The accuracy of the clustering approaches in generating category hierarchy determines the rationality of the global maintenance. The local phase detects topical changes and then adjusts inappropriate categories with three local operations. The global phase can quickly target inappropriate categories top-down and carry out cross-branch adjustment, which can also accelerate the local-phase adjustments. The local phase detects and adjusts the local-range inappropriate categories that are not adjusted in the global phase. By incorporating the two complementary phase adjustments, the approach can significantly improve the topical cohesion and accuracy of category hierarchy. A new measure is proposed for evaluating category hierarchy considering not only the balance of the hierarchical structure but also the accuracy of classification. Experiments show that the proposed approach is feasible and effective to adjust inappropriate category hierarchy. The proposed approach can be used to maintain the category hierarchy for managing various resources in dynamic application environment. It also provides an approach to specialize the current online category hierarchy to organize resources with more specific categories

Research on the automatic construction of the resource space model for scientific literature

The resource space model is a semantic data model to organize Web resources based on a classification of resources. The scientific resource space is an application of the resource space model on massive scientific literature resources. The construction of a scientific resource space needs to build a category (or concept) hierarchy and classify resources. Manual design suffers from heavy workload and low efficiency. In this thesis, we propose novel methods to solve the following two problems in the construction of a scientific resource space: 1. Automatic maintenance of a category hierarchy. A category hierarchy needs to evolve dynamically with new resources continually arriving so as to satisfy the dynamic re-quirements of the organization and management of resources. We propose an automatic maintenance approach to modifying the category hierarchy according to the hierarchical clustering of resources and show the effectiveness of this method by a series of comparison experiments on multiple datasets. 2. Automatic construction of a concept hierarchy. We propose a joint extraction model based on a deep neural network to extract entities and relations from scientific articles and build a concept hierarchy. Experimental results show the effectiveness of the joint model on the Semeval 2017 Task 10 dataset. We also implement a prototype system of the scientific resource space. The prototype system enables the comparative summarization on scientific articles. A set of novel comparative summarization methods based on the differential topic models (dTM) are proposed in this thesis. The effectiveness of the dTM-based methods is shown by a series of experimental results

RePOR: Mimicking humans on refactoring tasks. Are we there yet?

Refactoring is a maintenance activity that aims to improve design quality while preserving the behavior of a system. Several (semi)automated approaches have been proposed to support developers in this maintenance activity, based on the correction of anti-patterns, which are `poor' solutions to recurring design problems. However, little quantitative evidence exists about the impact of automatically refactored code on program comprehension, and in which context automated refactoring can be as effective as manual refactoring. Leveraging RePOR, an automated refactoring approach based on partial order reduction techniques, we performed an empirical study to investigate whether automated refactoring code structure affects the understandability of systems during comprehension tasks. (1) We surveyed 80 developers, asking them to identify from a set of 20 refactoring changes if they were generated by developers or by a tool, and to rate the refactoring changes according to their design quality; (2) we asked 30 developers to complete code comprehension tasks on 10 systems that were refactored by either a freelancer or an automated refactoring tool. To make comparison fair, for a subset of refactoring actions that introduce new code entities, only synthetic identifiers were presented to practitioners. We measured developers' performance using the NASA task load index for their effort, the time that they spent performing the tasks, and their percentages of correct answers. Our findings, despite current technology limitations, show that it is reasonable to expect a refactoring tools to match developer code

Software support for manufacturing operations in Belgian SMEs: one size fits all?

Manufacturing companies face a big challenge to bridge the gap between their business and manufacturing processes. The urge to increase efficiency makes it necessary to align the business and manufacturing processes. Small and Medium-sized Enterprises (SMEs) experience several barriers to adopt software support for manufacturing operations. This paper gives an overview of a research study conducted in Belgian SMEs. The research studied the current adoption of software support for manufacturing operations and the barriers that SMEs experience to invest in this type of software. The study is concluded with a number of considerations to enable the adoption of software support for manufacturing operations by SMEs

An application of machine learning to the organization of institutional software repositories

Software reuse has become a major goal in the development of space systems, as a recent NASA-wide workshop on the subject made clear. The Data Systems Technology Division of Goddard Space Flight Center has been working on tools and techniques for promoting reuse, in particular in the development of satellite ground support software. One of these tools is the Experiment in Libraries via Incremental Schemata and Cobweb (ElvisC). ElvisC applies machine learning to the problem of organizing a reusable software component library for efficient and reliable retrieval. In this paper we describe the background factors that have motivated this work, present the design of the system, and evaluate the results of its application

A meta-analysis approach to refactoring and XP

The mechanics of seventy-two different Java refactorings are described fully in Fowler's text. In the same text, Fowler describes seven categories of refactoring, into which each of the seventy-two refactorings can be placed. A current research problem in the refactoring and XP community is assessing the likely time and testing effort for each refactoring, since any single refactoring may use any number of other refactorings as part of its mechanics and, in turn, can be used by many other refactorings. In this paper, we draw on a dependency analysis carried out as part of our research in which we identify the 'Use' and 'Used By' relationships of refactorings in all seven categories. We offer reasons why refactorings in the 'Dealing with Generalisation' category seem to embrace two distinct refactoring sub-categories and how refactorings in the 'Moving Features between Objects' category also exhibit specific characteristics. In a wider sense, our meta-analysis provides a developer with concrete guidelines on which refactorings, due to their explicit dependencies, will prove problematic from an effort and testing perspective

Enabling Multi-Stakeholder Cooperative Modelling in Automotive Software Development and Implications for Model Driven Software Development

One of the motivations for a model driven approach to software development is to increase the involvement for a range of stakeholders in the requirements phases. This inevitably leads to a greater diversity of roles being involved in the production of models, and one of the issues with such diversity is that of providing models which are both accessible and appropriate for the phenomena being modelled. Indeed, such accessibility issues are a clear focus of this workshop. However, a related issue when producing models across multiple parties,often at dierent sites, or even dierent organisations is the management of such model artefacts. In particular, different parties may wish to experiment with model choices. For example, this idea of prototypingprocesses by experimenting with variants of models is one which has been used for many years by business process modellers, in order to highlight the impact of change, and thus improve alignment of process and supporting software specications. The problem often occurs when such variants needed to be merged, for example, to be used within a shared repository. This papers reports upon experiences and ndings of this merging problem as evaluated at Bosch Automotive. At Bosch we have dierent sites where modellers will make changes to shared models, and these models will subsequently require merging into a common repository. Currently, this work has concentrated on one type of diagram, the class diagram. However, it seems clear that the issue of how best to merge models where collaborative multi-party working takes places is one which has a significant potential impact upon the entire model driven process, and, given the diversity of stakeholders, could be particularly problematic for the requirements phase. In fact, class diagrams can also be used for information or data models created in the system analysis step. Hence, we believe that the lessons learned from this work will be valuable in tackling the realities of a commercially viable model driven process

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Structuring Wikipedia Articles with Section Recommendations

Sections are the building blocks of Wikipedia articles. They enhance readability and can be used as a structured entry point for creating and expanding articles. Structuring a new or already existing Wikipedia article with sections is a hard task for humans, especially for newcomers or less experienced editors, as it requires significant knowledge about how a well-written article looks for each possible topic. Inspired by this need, the present paper defines the problem of section recommendation for Wikipedia articles and proposes several approaches for tackling it. Our systems can help editors by recommending what sections to add to already existing or newly created Wikipedia articles. Our basic paradigm is to generate recommendations by sourcing sections from articles that are similar to the input article. We explore several ways of defining similarity for this purpose (based on topic modeling, collaborative filtering, and Wikipedia's category system). We use both automatic and human evaluation approaches for assessing the performance of our recommendation system, concluding that the category-based approach works best, achieving precision@10 of about 80% in the human evaluation.Comment: SIGIR '18 camera-read