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Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods
2007
IEEE Transactions on Software Engineering
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Specifically, we build a Bayesian network (BN) model to relate object-oriented software metrics to software fault content and fault proneness. ...
We present a methodology for Bayesian analysis of software quality. ...
ACKNOWLEDGMENTS The authors would like to thank Mike Chapman and the NASA Metrics Data Program for providing the data set used for the empirical analysis. ...
doi:10.1109/tse.2007.70722
fatcat:emminrskgnbztfypdm37z4ul2a
Analysis of CK Metrics to Predict Software Fault-Proneness using Bayesian Inference
2013
International Journal of Computer Applications
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The focus of the study is to design Bayesian Inference graph and predict faults for next piece of software. ...
All faults prediction techniques get a help in this study with the designing of Logistic regression model and Bayesian inference altogether. ...
Pai, Member, IEEE, and Joanne Bechta Dugan ," Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods",IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 33, NO. 10, OCTOBER ...
doi:10.5120/12854-9152
fatcat:qnjng4dek5g27dkh7rpogysmsq
Automation Architecture for Bayesian Network Based Test Case Prioritization and Execution
2016
2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC)
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The architecture is implemented as an integration of a series of tools and called Bayesian Network based test case prioritization and execution platform. ...
The platform is triggered by a change in the source code, then it collects necessary information to be supplied to Bayesian Network and uses Bayesian Network evaluation results to run high priority unit ...
They have proposed a unified model based on probability that uses Bayesian Networks (BN). Their proposed model utilizes data of source code changes, software fault proneness and test coverage. ...
doi:10.1109/compsac.2016.71
dblp:conf/compsac/UfuktepeT16
fatcat:dmsk4p4x7vbafblkoki6ue3cnu
Empirical analysis of change metrics for software fault prediction
2018
Computers & electrical engineering
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This research identifies a set of metrics to measure and classify the faults from the software systems. ...
Software metrics can be used to collect information regarding structural properties of a software design which can be further statistically analyzed, interpreted and linked to its quality. ...
There is need to define metrics based on the technique of discriminate Analysis as a tool for the formal specifications so that they can be Theoretically as detection of fault-prone programs is explored ...
doi:10.1016/j.compeleceng.2018.02.043
fatcat:dfmfpi6w4bhv3p3izuqg2d7sla
Software metrics: successes, failures and new directions
1999
Journal of Systems and Software
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The history of software metrics is almost as old as the history of software engineering. Yet, the extensive research and literature on the subject has had little impact on industrial practice. ...
Our approach uses Bayesian Belief nets, which are increasingly seen as the best means of handling decisionmaking under uncertainty. The approach is already having an impact in Europe. ...
Acknowledgements The work was supported, in part, by the EPSRCfunded project IMPRESS, and the ESPRIT-funded projects DEVA and SERENE. ...
doi:10.1016/s0164-1212(99)00035-7
fatcat:e3mias6tqrahpim4zyjv7zrege
Fault detection and prediction in an open-source software project
2009
Proceedings of the 5th International Conference on Predictor Models in Software Engineering - PROMISE '09
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We present an analysis of this information, showing that Pareto's Law holds and we evaluate the usefulness of the Chidamber and Kemerer metrics for identifying the fault-prone classes in the system analysed ...
One area where such efforts are useful is in the identification of the parts of the source-code of a software system that are most likely to contain faults and thus require changes. ...
I would also like to acknowledge the support of the CSIS Department, University of Limerick. ...
doi:10.1145/1540438.1540462
dblp:conf/promise/EnglishERC09
fatcat:vbjoqlfbm5eozarfmfwsl32c3y
Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers
2013
IEEE Transactions on Software Engineering
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Software testing is a crucial activity during software development and fault prediction models assist practitioners herein by providing an upfront identification of faulty software code by drawing upon ...
The results, both in terms of the AUC and the recently introduced H-measure, are rigorously tested using the statistical framework of Dem sar. ...
This motivates the use of software fault prediction models, which provide an upfront indication of whether code is likely to contain faults, i.e., is fault prone. ...
doi:10.1109/tse.2012.20
fatcat:5b542lskc5a73nvswdwbajbrje
Fault Prediction Using Statistical and Machine Learning Methods for Improving Software Quality
2012
Journal of Information Processing Systems
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In this paper, we predict a model to estimate fault proneness using Object Oriented CK metrics and QMOOD metrics. ...
An empirical assessment of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in the early phases of software development and to ensure corrective ...
They used univariate and multivariate analysis to find the individual and the combined effect of object oriented metrics and fault proneness. ...
doi:10.3745/jips.2012.8.2.241
fatcat:iakwyqdqnnetfe4j24i4v76ptq
Machine Learning Techniques for Software Quality Assurance: A Survey
[article]
2021
arXiv
pre-print
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Closely related to estimating defect-prone parts of a software system is the question of how to select and prioritize test cases, and indeed test case prioritization has been extensively researched as ...
We also review recently proposed machine learning methods for test case prioritization (TCP), and their ability to reduce the cost of regression testing without negatively affecting fault detection capabilities ...
processing if
the test history is long
[25]
Bayesian Network
-Changes of the source code,
-Coverage degree,
-Fault-proneness of the software
Hight value of APFD
(Average Percentage Faults Detected ...
arXiv:2104.14056v1
fatcat:b6hch7gimbefhpz6n5552waq5i
An empirical evaluation of fault-proneness models
2002
Proceedings of the 24th international conference on Software engineering - ICSE '02
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This paper reports an empirical study of the validity of multivariate models for predicting software fault-proneness across different applications. ...
It shows that suitably selected multivariate models can predict fault-proneness of modules of different software packages. ...
ACKNOWLEDGMENTS We would like to thank Sandro Morasca for the fruitful discussions and suggestions, Andrea Brambilla for contributing to the execution of the experiments, Roy Fielding and Apache.org for ...
doi:10.1145/581339.581371
dblp:conf/icse/DenaroP02
fatcat:edqz4y37tjgi5jge5bpgtczvce
An empirical evaluation of fault-proneness models
2002
Proceedings of the 24th international conference on Software engineering - ICSE '02
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This paper reports an empirical study of the validity of multivariate models for predicting software fault-proneness across different applications. ...
It shows that suitably selected multivariate models can predict fault-proneness of modules of different software packages. ...
ACKNOWLEDGMENTS We would like to thank Sandro Morasca for the fruitful discussions and suggestions, Andrea Brambilla for contributing to the execution of the experiments, Roy Fielding and Apache.org for ...
doi:10.1145/581368.581371
fatcat:ktnvzmrfkbfjzkbyb2dvhv2hni
Investigating Implications of Metric Based Predictive Data Mining Approaches towards Software Fault Predictions
2018
International Journal of Engineering & Technology
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Context: Since 1990, various researches have been working in the area of software fault prediction but yet it is difficult to assess the impacts and progressive path of this research field. ...
Objective: In this research work, author's major objective is to investigate the context and dimensions of research studies performed by different researchers in the area of software fault prediction. ...
Method: It is a study with critical analysis on publication from year 1979 to 1996. Findings: Study recommends use of Bayesian network model. ...
doi:10.14419/ijet.v7i3.12.16122
fatcat:cbnkzf5tzfbonmx3bbkp755mxm
Software Defect Prediction using Adaptive Neural Networks
2012
International Journal of Applied Information Systems
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To demonstrate the usefulness of ARNN, we used dataset from promisedata.org. This dataset contains 121 modules out of which 112 are not defected and 9 are defected. ...
The task is accomplished using Adaptive Resonance Neural Network (ARNN), a special case of unsupervised learning. ...
Munson et al. used discriminant analysis for classifying programs as fault-prone within a large medical-imaging software system. ...
doi:10.5120/ijais12-450612
fatcat:qd6ptmnhxrhgtgzxvduygvkuwe
Predicting aging-related bugs using software complexity metrics
2013
Performance evaluation (Print)
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Finally, by using such metrics as predictor variables and machine learning algorithms, we built fault prediction models that can be used to predict which source code files are more prone to Aging-Related ...
Then, a set of software complexity metrics were selected and extracted from the three projects. ...
This work has been supported by the Finmeccanica industrial group in the context of the Italian project "Iniziativa Software" (http://www.iniziativasoftware.it), and by the European Commission in the context ...
doi:10.1016/j.peva.2012.09.004
fatcat:z2juvyyfzbfxvjbnsvbbjixeee
An exploratory study of the impact of antipatterns on class change- and fault-proneness
2011
Empirical Software Engineering
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We investigate the impact of antipatterns on classes in object-oriented systems by studying the relation between the presence of antipatterns and the change-and fault-proneness of the classes. ...
We show that, in almost all releases of the four systems, classes participating in antipatterns are more changeand fault-prone than others. ...
Some approaches for complex software analysis use visualisation (Dhambri et al. 2008; Simon et al. 2001) . ...
doi:10.1007/s10664-011-9171-y
fatcat:cbxtumvqevc67c662qyf44ckuu
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