The Experts below are selected from a list of 50889 Experts worldwide ranked by ideXlab platform
Qianxiang Zhou - One of the best experts on this subject based on the ideXlab platform.
-
Assessment of space station on-orbit Maintenance Task complexity
Reliability Engineering & System Safety, 2019Co-Authors: Xiangyu Ge, Qianxiang ZhouAbstract:Abstract The safe operation of a space station relies on a large amount of on-orbit Maintenance. On-orbit Maintenance Task complexity (OOMTC) plays an important role in Maintenance processes, Maintenance program assessments, astronaut training and scheduling, and space station maintainability design. However, only a few studies involve on-orbit Maintenance complexity assessments. We proposed an OOMTC assessments model based on graph entropy. The model framework consisted of two aspects: inherent Maintenance complexity and external environmental influences. Inherent factors include: Maintenance operation logic, Maintenance operations, Maintenance knowledge support, and Maintenance of human-machine interface; external influence factors take into account tools, operating space, visual occlusion, spacesuit restrictions and time pressure. In addition, an experiment based on the space station technology verification module was designed to verify the effectiveness of the model. The data analysis shows that the Maintenance Task complexity value (R = 0.9244) is a good predictor of Maintenance operation time. Moreover, Maintenance complexity can be well classified. The proposed Maintenance Task complexity assessment model can provide guidance for Maintenance manuals and planning assessments, astronaut crew training and staffing arrangements, cargo spacecraft launch schedules, and space station maintainability design.
Yun Li - One of the best experts on this subject based on the ideXlab platform.
-
MSR4SM: Using topic models to effectively mining software repositories for software Maintenance Tasks☆☆☆
Information & Software Technology, 2015Co-Authors: Bixin Li, Hareton Leung, Bin Li, Yun LiAbstract:Abstract Context Mining software repositories has emerged as a research direction over the past decade, achieving substantial success in both research and practice to support various software Maintenance Tasks. Software repositories include bug repository, communication archives, source control repository, etc. When using these repositories to support software Maintenance, inclusion of irrelevant information in each repository can lead to decreased effectiveness or even wrong results. Objective This article aims at selecting the relevant information from each of the repositories to improve effectiveness of software Maintenance Tasks. Method For a Maintenance Task at hand, maintainers need to implement the Maintenance request on the current system. In this article, we propose an approach, MSR4SM, to extract the relevant information from each software repository based on the Maintenance request and the current system. That is, if the information in a software repository is relevant to either the Maintenance request or the current system, this information should be included to perform the current Maintenance Task. MSR4SM uses the topic model to extract the topics from these software repositories. Then, relevant information in each software repository is extracted based on the topics. Results MSR4SM is evaluated for two software Maintenance Tasks, feature location and change impact analysis, which are based on four subject systems, namely jEdit, ArgoUML, Rhino and KOffice. The empirical results show that the effectiveness of traditional software repositories based Maintenance Tasks can be greatly improved by MSR4SM. Conclusions There is a lot of irrelevant information in software repositories. Before we use them to implement a Maintenance Task at hand, we need to preprocess them. Then, the effectiveness of the software Maintenance Tasks can be improved.
Diane M. Strong - One of the best experts on this subject based on the ideXlab platform.
-
Experience as a Moderating Variable in a Task-Technology Fit Model
2008Co-Authors: Mark T. Dishaw, Diane M. StrongAbstract:We test the addition of experience with Maintenance tools and with the Maintenance Task to our previously tested Task-technology fit model for software Maintenance tool use. Tool experience is significant as both a main and moderating effect, but Task experience adds little to the explanatory power of the model.
-
supporting software Maintenance with software engineering tools a computed Task technology fit analysis
Journal of Systems and Software, 1998Co-Authors: Mark T. Dishaw, Diane M. StrongAbstract:Abstract Management has turned to software engineering tools designed to support software Maintenance as a potential solution to Maintenance productivity and quality problems. Once adopted by an organisation, however, these tools are often not used. A research model, based on a Task–technology fit (TTF) model, is developed to explain the factors which lead to the use of the software Maintenance support tools. Our model, which examines the nature of the fit between software tool functionality and Maintenance Task demands, consists of a TTF model augmented with a model of the Maintenance Task and a model of software Maintenance tool functionality. Such an augmented model is necessary for moving beyond isolated exploratory studies of Maintenance and for building on the existing research in software development and software tool utilization. Using this model, fit between Maintenance Task and technology characteristics is computed for two dimensions of fit derived from the Task and technology models. Tests of hypotheses derived from the model demonstrate that Task–technology fit, computed using methods for computing strategic fit, is associated with increased tool utilization. Our findings provide direction for the development of better Maintenance support tools. This research extends the application and usefulness of TTF models and Maintenance Task and technology models so that future software Maintenance research can build on tested models.
Xiangyu Ge - One of the best experts on this subject based on the ideXlab platform.
-
Assessment of space station on-orbit Maintenance Task complexity
Reliability Engineering & System Safety, 2019Co-Authors: Xiangyu Ge, Qianxiang ZhouAbstract:Abstract The safe operation of a space station relies on a large amount of on-orbit Maintenance. On-orbit Maintenance Task complexity (OOMTC) plays an important role in Maintenance processes, Maintenance program assessments, astronaut training and scheduling, and space station maintainability design. However, only a few studies involve on-orbit Maintenance complexity assessments. We proposed an OOMTC assessments model based on graph entropy. The model framework consisted of two aspects: inherent Maintenance complexity and external environmental influences. Inherent factors include: Maintenance operation logic, Maintenance operations, Maintenance knowledge support, and Maintenance of human-machine interface; external influence factors take into account tools, operating space, visual occlusion, spacesuit restrictions and time pressure. In addition, an experiment based on the space station technology verification module was designed to verify the effectiveness of the model. The data analysis shows that the Maintenance Task complexity value (R = 0.9244) is a good predictor of Maintenance operation time. Moreover, Maintenance complexity can be well classified. The proposed Maintenance Task complexity assessment model can provide guidance for Maintenance manuals and planning assessments, astronaut crew training and staffing arrangements, cargo spacecraft launch schedules, and space station maintainability design.
Lu Chuan - One of the best experts on this subject based on the ideXlab platform.
-
Modeling And Simulating Technology of Maintenance Task based on MTN
Computer Simulation, 2006Co-Authors: Lu ChuanAbstract:Maintenance Task process model is the important basis and method for maintainability and Maintenance Task analysis and evaluation, the modeling technology in existence is nor intuitionigtic or abundance for describing Maintenance process and the information concerned. Based on Petri Net, a modeling technology--Maintenance Task Net is presented for describing Maintenance process. The method is recounted, which makes use of Maintenance Task cells and Task relationships. Two modeling modes are presented for different initial information of Maintenance Task. Based on the modeling technology, a software is developed and some applied research works are carried out in MTA( Maintenance Task Analysis) and VM( Virtual Maintenance) ,and the practicability of MTN is validated.
