Software metrics thresholds calculation techniques to predict fault-proneness: An empirical comparison

2018 ◽  
Vol 96 ◽  
pp. 38-67 ◽  
Author(s):  
Alexandre Boucher ◽  
Mourad Badri
Keyword(s):  
Software Metrics ◽  
Empirical Comparison ◽  
Fault Proneness

scholarly journals Software Metrics Evaluation Based on Entropy

2010 ◽  
pp. 139-151 ◽  
Author(s):  
R. Selvarani ◽  
T.R.Gopalakrishnan Nair ◽  
Muthu Ramachandran ◽  
Kamakshi Prasad
Keyword(s):  
Software Development ◽  
Software Metrics ◽  
Object Oriented ◽  
Fault Prediction ◽  
Design Metrics ◽  
Incremental Model ◽  
Class Level ◽  
Development Life Cycle ◽  
Agile Software ◽  
Fault Proneness

The complexity of modern software, the commercial constraints and the expectation for high quality product demands the accurate fault prediction based on OO design metrics in the class level in the early stages of software development. The object oriented class metrics are used as quality predictors in the entire OO software development life cycle even when a highly iterative, incremental model or agile software process is employed. Recent research has shown some of the OO design metrics are useful for predicting fault-proneness of classes. In this chapter the empirical validation of a set of metrics proposed by Chidamber and Kemerer is performed to assess their ability in predicting the software quality in terms of fault proneness and degradation. The authors have also proposed the design complexity of object-oriented software with Weighted Methods per Class metric (WMC-CK metric) expressed in terms of Shannon entropy, and error proneness.

An Empirical Study on Software Fault Prediction Using Product and Process Metrics

2021 ◽  
Vol 14 (1) ◽  
pp. 62-78
Author(s):  
Raed Shatnawi ◽  
Alok Mishra
Keyword(s):  
Software Quality ◽  
Software Metrics ◽  
Fault Prediction ◽  
Software Fault Prediction ◽  
Product Metrics ◽  
Software Models ◽  
Software Fault ◽  
Process Metrics ◽  
Product And Process ◽  
Fault Proneness

Product and process metrics are measured from the development and evolution of software. Metrics are indicators of software fault-proneness and advanced models using machine learning can be provided to the development team to select modules for further inspection. Most fault-proneness classifiers were built from product metrics. However, the inclusion of process metrics adds evolution as a factor to software quality. In this work, the authors propose a process metric measured from the evolution of software to predict fault-proneness in software models. The process metrics measures change-proneness of modules (classes and interfaces). Classifiers are trained and tested for five large open-source systems. Classifiers were built using product metrics alone and using a combination of product and the proposed process metric. The classifiers evaluation shows improvements whenever the process metrics were used. Evolution metrics are correlated with quality of software and helps in improving software quality prediction for future releases.

An empirical review of gambling expansion and gambling-related harm

2018 ◽  
Vol 64 (5-6) ◽  
pp. 295-306 ◽  
Author(s):  
Debi A. LaPlante ◽  
Heather M. Gray ◽  
Pat M. Williams ◽  
Sarah E. Nelson
Keyword(s):  
Literature Review ◽  
Peer Review ◽  
Empirical Data ◽  
Empirical Studies ◽  
Expansion Method ◽  
Responsible Gambling ◽  
Review Literature ◽  
Evidence Based ◽  
Empirical Comparison ◽  
Related Harm

Abstract. Aims: To discuss and review the latest research related to gambling expansion. Method: We completed a literature review and empirical comparison of peer reviewed findings related to gambling expansion and subsequent gambling-related changes among the population. Results: Although gambling expansion is associated with changes in gambling and gambling-related problems, empirical studies suggest that these effects are mixed and the available literature is limited. For example, the peer review literature suggests that most post-expansion gambling outcomes (i. e., 22 of 34 possible expansion outcomes; 64.7 %) indicate no observable change or a decrease in gambling outcomes, and a minority (i. e., 12 of 34 possible expansion outcomes; 35.3 %) indicate an increase in gambling outcomes. Conclusions: Empirical data related to gambling expansion suggests that its effects are more complex than frequently considered; however, evidence-based intervention might help prepare jurisdictions to deal with potential consequences. Jurisdictions can develop and evaluate responsible gambling programs to try to mitigate the impacts of expanded gambling.

Empirical comparison of the adjustable spanner and the adaptive toolbox models of choice.

2019 ◽  
Vol 45 (7) ◽  
pp. 1151-1165 ◽  
Author(s):  
Antonia Krefeld-Schwalb ◽  
Chris Donkin ◽  
Ben R. Newell ◽  
Benjamin Scheibehenne
Keyword(s):  
Empirical Comparison ◽  
Adaptive Toolbox

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.

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