Perspective - (2022) Volume 9, Issue 4

Important Aspects of Coupling and Measurement of Software Data System
Christine Aventin*
Department of Social Sciences, University of Kent, Belgium
*Correspondence: Christine Aventin, Department of Social Sciences, University of Kent, Belgium, Email:

Received: 01-Aug-2022, Manuscript No. TOSOCIAL-22-75854; Editor assigned: 03-Aug-2022, Pre QC No. TOSOCIAL-22-75854 (PQ); Reviewed: 17-Aug-2022, QC No. TOSOCIAL-22-75854; Revised: 22-Aug-2022, Manuscript No. TOSOCIAL-22-75854 (R); Published: 29-Aug-2022


Computer systems research often involves the design, implementation, and measurement of complex system software and data. The availability of these artifacts is crucial for the reproducibility and reproducibility of study results. This is because system software often embodies numerous implicit assumptions and parameters that are not fully documented in the research paper itself. Artifact availability was previously associated with higher impact on papers as measured by citation counts. Still, sharing research output is not as common as its importance warrants. The main purpose of this study is to provide an exploratory statistical analysis of artifact sharing rates and related factors in the field of computer systems research. To this end, we examine a crosssectional dataset of papers from 56 concurrent system conferences. In addition to extensive data on conferences, lectures, and authors, this analytical dataset includes data on publications, continued availability, labeling, and location of research artifacts. We combine this manually curated dataset with citation counts to assess the relationships between various artifact properties and citation metrics. In addition, we take previous observations from other fields on the relationship between artifact properties and various characteristics of papers, authors and venues and apply them to this field. The overall percentage of artifact sharing found in this dataset is approximately 30%, but varies significantly by article, author, and conference factors, and approaches 43% in conferences that positively evaluated artifact sharing.


Generating a human image from a source pose to a target pose is a difficult task due to the high degree of entanglement of nonrigid body deformations. This article introduces a new framework for generating human images with shape consistency and appearance consistency. The proposed framework uses graph networks to derive global relationships between source and target poses in the graph to improve pose transfer. In addition, the source image is decomposed into various attributes (hair, clothes, pants, shoes, etc.) and combined with pose coding by manipulation method to generate a more realistic human image. We apply an alternative update strategy to facilitate mutual guidance between the pose and appearance modules to improve the image quality of the person. Qualitative and quantitative experiments were performed on the Deep Fashion dataset.


Software is a complex entity whose development requires careful planning, time, and money. Software measurements are very useful in evaluating program quality. Coupling, among existing countermeasures, is an important design tool for computing interdependencies between entities in a software system. The higher the coupling, the more complex the cognition and the more likely it is that errors will occur. Timely prediction of error-prone modules saves testing time and money. This white paper aims to capture the important aspects of coupling and to assess their effectiveness in identifying error-prone entities in software systems. We propose two coupling metrics. H. Vobel-in and Vobel-out capturing the degree of coupling and information flow. Five projects are used to empirically evaluate the effectiveness of the Vovel metric in determining error-prone classes. Model building is done using univariate logistic regression, and then Spearman’s correlation coefficient is calculated using existing coupling metrics to assess the extent of unique information. Finally, we use the minimum correlation metric to construct multivariate logistic regressions with and without the vowel metric to assess the effectiveness of the vowel metric.

Copyright: This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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