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Predicting fault-prone components in a java legacy system
2006
Proceedings of the 2006 ACM/IEEE international symposium on International symposium on empirical software engineering - ISESE '06
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A number of measures including code quality, class structure, changes in class structure, and the history of classlevel changes and faults are included as candidate predictors of class fault-proneness. ...
On the basis of the cost-effectiveness analysis we show that change and fault data from previous releases is paramount to developing a practically useful prediction model. ...
Measures of the cumulated difference in structural properties over time can also be significant predictors of fault proneness [13] . ...
doi:10.1145/1159733.1159738
dblp:conf/isese/ArisholmB06
fatcat:oyopq2tudzbd7ayw2qoekmeb4y
Empirical Studies of Quality Models in Object-Oriented Systems
[chapter]
2002
Advances in Computers
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Constructive guidelines are also provided to facilitate the work of future studies, thus facilitating the development of an empirical body of knowledge. ...
This chapter has for objective to summarize, in a structured and detailed fashion, the empirical results that have been reported so far with modeling external system quality based on structural design ...
Though predicted defect detection probabilities are clearly not realistic based on actual fault data, the fault-proneness class ranking is accurate. ...
doi:10.1016/s0065-2458(02)80005-5
fatcat:6gzeqijsifdjlkbvotsgcvijsm
The impact of software complexity on cost and quality - A comparative analysis between Open source and proprietary software
[article]
2017
arXiv
pre-print
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We found that fault proneness and maintainability are most frequently investigated attributes. ...
This paper presented a systematic review on the influence of software complexity metrics on quality attributes. ...
Therefore, we accept the null hypothesis: with spearman test, there is no positive impact of NOC, DIT or LCOM on fault proneness. The complete list of metrics is given in [29] . ...
arXiv:1712.00675v1
fatcat:jk6d36u33bg7bgmzg6rsaic6za
Some SonarQube Issues have a Significant but SmallEffect on Faults and Changes. A large-scale empirical study
[article]
2019
arXiv
pre-print
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Our aim is to analyze the diffuseness of Technical Debt (TD) items in software systems and to assess their impact on code changes and fault-proneness, considering also the type of TD items and their severity ...
Clean classes (classes not affected by TD items) are less change-prone than dirty ones, but the difference between the groups is small. ...
[19] used ML to predict the change-proneness of classes based on SonarQube violations and their evolution. ...
arXiv:1908.11590v1
fatcat:u4o3htbuuvczbesavgkb7ekjh4
Exploring the relationships between design measures and software quality in object-oriented systems
2000
Journal of Systems and Software
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When predicting fault-prone classes, the best model shows a percentage of correct classifications higher than 80% and finds more than 90% of faulty classes. ...
Besides the size of classes, the frequency of method invocations and the depth of inheritance hierarchies seem to be the main driving factors of fault proneness. ...
Khaled El Emam, Bernd Freimut, and Isabella Wieczorek for their helpful comments on drafts of this report. Special thanks also go to Drs. ...
doi:10.1016/s0164-1212(99)00102-8
fatcat:qw4w7rtogjcblcc57443c6jvte
Anti-pattern Mutations and Fault-proneness
2014
2014 14th International Conference on Quality Software
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Specifically, the study analyzes the mutations of anti-patterns, the changes that they undergo, and the relation between anti-pattern evolution behaviors and fault-proneness. ...
Results show that (1) anti-patterns mutate from one type of anti-patterns to another, (2) structural changes are behind these mutations, and (3) some mutations are more risky in terms of fault-proneness ...
We investigated the impact of anti-patterns on classes in object-oriented systems by studying the relation between the presence of anti-patterns and the change-and fault-proneness of the classes. ...
doi:10.1109/qsic.2014.45
dblp:conf/qsic/JaafarKGZ14
fatcat:xig5s7mprfbx7psl7ltas6mxb4
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. ...
giving us access to the data required for our experiments. ...
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. ...
giving us access to the data required for our experiments. ...
doi:10.1145/581368.581371
fatcat:ktnvzmrfkbfjzkbyb2dvhv2hni
Risk chain prediction metrics for predicting fault proneness in Software Systems
2012
IOSR Journal of Engineering
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We empirically advised ICBO and ILCOM5 for admiration fault proneness of classes in a ample accessible antecedent arrangement and compared these metrics with a host of absolute structural and risk chain ...
Our aboriginal metric, Ideal Coupling between Object classes (ICBO), is based on the acclaimed CBO coupling metric, while the added metric, Ideal Lack of Cohesion on Methods (ILCOM5), is based on the LCOM5 ...
The fault prediction sensitivity measure as Sensitivity= Classes correctly predicted as fault prone Classes actually fault prone PCA analysis of the CBO&LCOM5 and ICBO&ILCOM5 Fig 2: Fault prediction sensitivity ...
doi:10.9790/3021-0281190195
fatcat:pfdmlb5hj5gbbm6nu6cv4nos6i
The prediction of faulty classes using object-oriented design metrics
2001
Journal of Systems and Software
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The study used data collected from one version of a commercial Java application for constructing a prediction model. The model was then validated on a subsequent release of the same application. ...
For object-oriented applications, prediction models using design metrics can be used to identify faulty classes early on. ...
The selected metric would be the one that has the largest change in odds ratio (i.e., the largest impact on fault-proneness). ...
doi:10.1016/s0164-1212(00)00086-8
fatcat:fowycn72gfexbbnkhbnpcserfm
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
A Study On Early Prediction Of Fault Proneness In Software Modules Using Genetic Algorithm
2010
Zenodo
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Fault-proneness of a software module is the probability that the module contains faults. ...
The results show that the fusion of requirement and code metric is the best prediction model for detecting the faults as compared with commonly used code based model. ...
Prediction of fault-prone modules provides one way to support software quality engineering through improved scheduling and project control. ...
doi:10.5281/zenodo.1075440
fatcat:kp4eckp4bzh7flizpvyfq7uv3m
Investigating quality factors in object-oriented designs
1999
Proceedings of the 21st international conference on Software engineering - ICSE '99
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For example, the frequency of method invocations appears to be the main driving factor of fault-proneness in all systems. ...
In order to draw more general conclusions and to (dis)confirm the results obtained there, we now replicated the study using data collected on an industrial system developed by professionals. ...
data could not have been collected. ...
doi:10.1145/302405.302654
dblp:conf/icse/BriandWIL99
fatcat:gq2zgo52tjcjrkebozvyk26o4u
The Impact of Coupling on the Fault-Proneness of Aspect-Oriented Programs: An Empirical Study
2010
2010 IEEE 21st International Symposium on Software Reliability Engineering
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As a result, these metrics are unlikely to provide optimal predictions of pivotal quality attributes such as fault-proneness. ...
This impacts further by restraining the assessments of AOP empirical studies. ...
ACKNOWLEDGEMENTS We would like to thank Andrew Camilleri from Lancaster University (UK), Eduardo Figueiredo from UFMG (Brazil) and Nélio Cacho from UFRN (Brazil) for their help while analysing the MobileMedia ...
doi:10.1109/issre.2010.33
dblp:conf/issre/BurrowsFLGT10
fatcat:xz52khgz5vaqpa4cazdup62utu
Best Suited Machine Learning Techniques for Software Fault Prediction
2020
International journal of recent technology and engineering
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All software fault prediction techniques depend on base learners used and also nature of fault dataset. ...
It is always preferred to determine the count of faults rather than classifying each software module as fault-free and fault-prone. ...
Research studies have searched the use of computational intelligence models to predict fault proneness class as regression problems. ...
doi:10.35940/ijrte.f9456.038620
fatcat:4bxbol7ux5dwna55q7ep6w5e2y
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