ICSE workshop on remote analysis and measurement of software systems (RAMSS)

2003 ◽  
Vol 28 (6) ◽  
pp. 10-10
Author(s):  
Alessandro Orso ◽  
Adam Porter
Keyword(s):  
Software Systems ◽  
Remote Analysis

scholarly journals Adaptive reuse of libre software systems for supporting on-line collaboration

2005 ◽  
Vol 30 (4) ◽  
pp. 1-4 ◽  
Author(s):  
Paul Adams ◽  
Cornelia Boldyreff ◽  
David Nutter ◽  
Stephen Rank
Keyword(s):  
Adaptive Reuse ◽  
Software Systems ◽  
On Line ◽  
Libre Software

Analyzing the Anatomy of GNU/Linux Distributions

2005 ◽  
pp. 27-58 ◽  
Author(s):  
Jesús M. Gonzalez-Barahona ◽  
Gregorio Robles ◽  
Miguel Ortuno-Perez ◽  
Luis Rodero-Merino ◽  
José Centeno-Gonzalez ◽  
...  
Keyword(s):  
Software Development ◽  
Programming Language ◽  
Large Fraction ◽  
Source Code ◽  
Quantitative Information ◽  
Software Systems ◽  
Package Size ◽  
Libre Software ◽  
Reliable Software ◽  
Over Time

GNU/Linux distributions are probably the largest coordinated pieces of software ever put together. Each one is in some sense a snapshot of a large fraction of the libre software development landscape at the time of the release and, therefore, its study is important to understand the appearance of that landscape. They are also the working proof of the possibility of releasing reliable software systems in the range of 50-100 millions of lines of code, even when the components of such systems are built by hundreds of independent groups of developers, with no formal connection to the group releasing the whole system. In this chapter, we provide some quantitative information about the software included in two such distributions: Red Hat and Debian. Differences in policy and organization of both distributions will show up in the results, but some common patterns will also arise. For instance, both are doubling their size every two years, and both present similar patterns in programming language usage and package size distributions. All in all, this study pretends to show how GNU/Linux distributions are with respect to their source code, and how they evolve over time. A methodology of how to make comparable and automated studies on this kind of distributions is also presented.

DOCUMENTATION OF SOFTWARE SYSTEMS

2014 ◽  
pp. 240-246
Author(s):  
V. Herwig ◽  
Keyword(s):  
Software Systems

Methods of parallel computing for multilevel fuzzy Takagi – Sugeno systems

2016 ◽  
pp. 141-149
Author(s):  
S.V. Yershov ◽  
◽  
R.М. Ponomarenko ◽  
Keyword(s):  
Dynamic Models ◽  
Fuzzy Inference ◽  
Knowledge Bases ◽  
Comparative Characteristic ◽  
Software Systems ◽  
Scientific Papers ◽  
Speed Up ◽  
Diagnostic Software ◽  
Takagi Sugeno

Parallel tiered and dynamic models of the fuzzy inference in expert-diagnostic software systems are considered, which knowledge bases are based on fuzzy rules. Tiered parallel and dynamic fuzzy inference procedures are developed that allow speed up of computations in the software system for evaluating the quality of scientific papers. Evaluations of the effectiveness of parallel tiered and dynamic schemes of computations are constructed with complex dependency graph between blocks of fuzzy Takagi – Sugeno rules. Comparative characteristic of the efficacy of parallel-stacked and dynamic models is carried out.

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

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