scholarly journals Statistical Analysis on Software Metrics Affecting Modularity in Open Source Software

2011 ◽  
Vol 3 (3) ◽  
pp. 105-118
Author(s):  
Andi Wahju Rahardjo Emanuel ◽  
Retantyo Wardoyo ◽  
Jazi Eko Istiyanto ◽  
Khabib Mustofa
Keyword(s):  
Statistical Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Software Metrics

scholarly journals First analysis of software metrics

2015 ◽  
Author(s):  
Martin Fenner
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Metrics ◽  
Software Repositories ◽  
First Results

Last week I wrote about software.lagotto.io, an instance of the lagotto open source software collecting metrics for the about 1,400 software repositories included in Sciencetoolbox. In this post I want to report the first results analyzing the data.Number of software repositories (out of 1,404) ...

Using Design of Experiments to Analyze Open Source Software Metrics for Change Impact Estimation

2019 ◽  
Vol 10 (1) ◽  
pp. 16-33
Author(s):  
Miloud Dahane ◽  
Mustapha Kamel Abdi ◽  
Mourad Bouneffa ◽  
Adeel Ahmad ◽  
Henri Basson
Keyword(s):  
Statistical Analysis ◽  
Design Of Experiments ◽  
Open Source Software ◽  
Software Maintenance ◽  
Software Metrics ◽  
Software Evolution ◽  
Measurement Tool ◽  
Software Artifacts ◽  
Change Impact ◽  
Evolution Control

Software evolution control mostly relies on the better structure of the inherent software artifacts and the evaluation of different qualitative factors like maintainability. The attributes of changeability are commonly used to measure the capability of the software to change with minimal side effects. This article describes the use of the design of experiments method to evaluate the influence of variations of software metrics on the change impact in developed software. The coupling metrics are considered to analyze their degree of contribution to cause a change impact. The data from participant software metrics are expressed in the form of mathematical models. These models are then validated on different versions of software to estimate the correlation of coupling metrics with the change impact. The proposed approach is evaluated with the help of a set of experiences which are conducted using statistical analysis tools. It may serve as a measurement tool to qualify the significant indicators that can be included in a Software Maintenance dashboard.

scholarly journals Rail-RNA: Scalable analysis of RNA-seq splicing and coverage

2015 ◽  
Author(s):  
Abhinav Nellore ◽  
Leonardo Collado-Torres ◽  
Andrew E Jaffe ◽  
José Alquicira-Hernández ◽  
Jacob Pritt ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Web Services ◽  
Open Source ◽  
Rna Sequencing ◽  
Open Source Software ◽  
Rna Seq ◽  
Spliced Alignment ◽  
Amazon Web Services ◽  
Scalable Analysis ◽  
Multiple Samples

RNA sequencing (RNA-seq) experiments now span hundreds to thousands of samples. Current spliced alignment software is designed to analyze each sample separately. Consequently, no information is gained from analyzing multiple samples together, and it is difficult to reproduce the exact analysis without access to original computing resources. We describe Rail-RNA, a cloud-enabled spliced aligner that analyzes many samples at once. Rail-RNA eliminates redundant work across samples, making it more efficient as samples are added. For many samples, Rail-RNA is more accurate than annotation-assisted aligners. We use Rail-RNA to align 667 RNA-seq samples from the GEUVADIS project on Amazon Web Services in under 16 hours for US$0.91 per sample. Rail-RNA produces alignments and base-resolution bigWig coverage files, ready for use with downstream packages for reproducible statistical analysis. We identify expressed regions in the GEUVADIS samples and show that both annotated and unannotated (novel) expressed regions exhibit consistent patterns of variation across populations and with respect to known confounders. Rail-RNA is open-source software available at http://rail.bio.

scholarly journals Similar Characteristics of Internal Software Quality Attributes for Object-Oriented Open-Source Software Projects

2014 ◽  
Author(s):  
Mariana Santos ◽  
Rodrigo Amador ◽  
Paulo Henrique De Souza Bermejo ◽  
Heitor Costa
Keyword(s):  
Cluster Analysis ◽  
Open Source ◽  
Software Quality ◽  
Open Source Software ◽  
Software Metrics ◽  
Object Oriented ◽  
Quality Attributes ◽  
Internal Quality ◽  
Software Systems ◽  
Systems Quality

Organizations are becoming increasingly concerned about software quality. In object-oriented (OO) systems, quality is characterized by measurements of internal quality attributes. An efficient and proper method to analyze software quality in the absence of fault-prone or defective data labels is cluster analysis. The aim of this paper is to find similarities among project structures by measuring characteristics of internal software quality. In a sample of 150 open-source software systems, we evaluated software using macro and micro categories. Results obtained using cluster analysis indicated that some domains such as Graphics, Games, and Development tend to have similarities in specialization, abstraction, stability, and complexity. These results exploit the ability of OO software metrics to find similar behavior across domains. The results provide an immediate view of the trends and characteristics of internal software quality of Java systems that need to be addressed so that software systems can continue to be maintainable.

The Statistical Analysis of Misreporting on Sensitive Survey Questions

2017 ◽  
Vol 25 (2) ◽  
pp. 241-259 ◽  
Author(s):  
Gregory Eady
Keyword(s):  
Statistical Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Multivariate Regression ◽  
Large Scale ◽  
Survey Question ◽  
Direct Question ◽  
Inherent Problem ◽  
List Experiment ◽  
The Right

What explains why some survey respondents answer truthfully to a sensitive survey question, while others do not? This question is central to our understanding of a wide variety of attitudes, beliefs, and behaviors, but has remained difficult to investigate empirically due to the inherent problem of distinguishing those who are telling the truth from those who are misreporting. This article proposes a solution to this problem. It develops a method to model, within a multivariate regression context, whether survey respondents provide one response to a sensitive item in a list experiment, but answer otherwise when asked to reveal that belief openly in response to a direct question. As an empirical application, the method is applied to an original large-scale list experiment to investigate whether those on the ideological left are more likely to misreport their responses to questions about prejudice than those on the right. The method is implemented for researchers as open-source software.

scholarly journals Impact of Historical Software Metric Changes in Predicting Future Maintainability Trends in Open-Source Software Development

2020 ◽  
Vol 10 (13) ◽  
pp. 4624
Author(s):  
Mitja Gradišnik ◽  
Tina Beranič ◽  
Sašo Karakatič
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Maintenance ◽  
Software Metrics ◽  
Prediction Models ◽  
Measurement Data ◽  
Software Project ◽  
Software Projects ◽  
Software Metric ◽  
Software Maintainability

Software maintenance is one of the key stages in the software lifecycle and it includes a variety of activities that consume the significant portion of the costs of a software project. Previous research suggest that future software maintainability can be predicted, based on various source code aspects, but most of the research focuses on the prediction based on the present state of the code and ignores its history. While taking the history into account in software maintainability prediction seems intuitive, the research empirically testing this has not been done, and is the main goal of this paper. This paper empirically evaluates the contribution of historical measurements of the Chidamber & Kemerer (C&K) software metrics to software maintainability prediction models. The main contribution of the paper is the building of the prediction models with classification and regression trees and random forest learners in iterations by adding historical measurement data extracted from previous releases gradually. The maintainability prediction models were built based on software metric measurements obtained from real-world open-source software projects. The analysis of the results show that an additional amount of historical metric measurements contributes to the maintainability prediction. Additionally, the study evaluates the contribution of individual C&K software metrics on the performance of maintainability prediction models.

Sign in / Sign up

Export Citation Format

Share Document