scholarly journals Django Web Framework Software Metrics Measurement Using Radon and Pylint

2018 ◽  
Author(s):  
Suryadiputra Liawatimena ◽  
Harco Leslie Hendric Spits Warnars ◽  
Agung Trisetyarso ◽  
Edi Abdurahman ◽  
Benfano Soewito ◽  
...  
Keyword(s):  
Software Metrics ◽  
Web Framework

Neural Network Model to Calculate the Creep Data Using Size

2017 ◽  
Vol 7 (8) ◽  
pp. 257
Author(s):  
Seetharam .K ◽  
Sharana Basava Gowda ◽  
. Varadaraj
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Software Engineering ◽  
Network Model ◽  
Neural Network Model ◽  
Software Metrics ◽  
Creep Data ◽  
Engineering Software ◽  
Function Points ◽  
Different Levels

In Software engineering software metrics play wide and deeper scope. Many projects fail because of risks in software engineering development[1]t. Among various risk factors creeping is also one factor. The paper discusses approximate volume of creeping requirements that occur after the completion of the nominal requirements phase. This is using software size measured in function points at four different levels. The major risk factors are depending both directly and indirectly associated with software size of development. Hence It is possible to predict risk due to creeping cause using size.

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.

Computation of External view based Software Metrics: Java Based Tool

2017 ◽  
Vol 5 (8) ◽  
pp. 33-43
Author(s):  
P.L. Powar ◽  
M.P. Singh ◽  
Jawwad Wasat Shareef ◽  
Bharat Solanki
Keyword(s):  
Software Metrics ◽  
External View

Software Metrics for Total Development Cycle Evaluation

1991 ◽  
Author(s):  
Ross Grable
Keyword(s):  
Software Metrics ◽  
Development Cycle

Recommendations for an Initial Set of Software Metrics

1989 ◽  
Author(s):  
Shari L. Pfleeger
Keyword(s):  
Software Metrics

Comparison of threshold identification techniques for object-oriented software metrics

IET Software ◽  
2020 ◽  
Vol 14 (6) ◽  
pp. 727-738
Author(s):  
Raed Shatnawi
Keyword(s):  
Software Metrics ◽  
Object Oriented ◽  
Identification Techniques

scholarly journals Copula-based software metrics aggregation

2021 ◽  
Author(s):  
Maria Ulan ◽  
Welf Löwe ◽  
Morgan Ericsson ◽  
Anna Wingkvist
Keyword(s):  
Open Source Software ◽  
Software Metrics ◽  
Information Quality ◽  
Empirical Studies ◽  
Probabilistic Approach ◽  
Software Systems ◽  
Quality Model ◽  
Quality Models ◽  
Joint Distributions ◽  
Abstract Notion

AbstractA quality model is a conceptual decomposition of an abstract notion of quality into relevant, possibly conflicting characteristics and further into measurable metrics. For quality assessment and decision making, metrics values are aggregated to characteristics and ultimately to quality scores. Aggregation has often been problematic as quality models do not provide the semantics of aggregation. This makes it hard to formally reason about metrics, characteristics, and quality. We argue that aggregation needs to be interpretable and mathematically well defined in order to assess, to compare, and to improve quality. To address this challenge, we propose a probabilistic approach to aggregation and define quality scores based on joint distributions of absolute metrics values. To evaluate the proposed approach and its implementation under realistic conditions, we conduct empirical studies on bug prediction of ca. 5000 software classes, maintainability of ca. 15000 open-source software systems, and on the information quality of ca. 100000 real-world technical documents. We found that our approach is feasible, accurate, and scalable in performance.

On diminishing the vibrant confusion in software metrics

1997 ◽  
Vol 32 (2) ◽  
pp. 35-38
Author(s):  
Lem O. Ejiogu
Keyword(s):  
Software Metrics

Five principles for the formal validation of models of software metrics

1993 ◽  
Vol 28 (8) ◽  
pp. 67-76 ◽  
Author(s):  
Lem O. Ejiogu
Keyword(s):  
Software Metrics ◽  
Formal Validation ◽  
Validation Of Models

Software metrics—an experimental analysis

1990 ◽  
Vol 25 (2) ◽  
pp. 35-41 ◽  
Author(s):  
B. Mehndiratta ◽  
P. S. Grover
Keyword(s):  
Experimental Analysis ◽  
Software Metrics
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