Analysis and Classification Of Software Fault-Proneness And Vulnerabilities

Evaluation of Software Fault Proneness with a Support Vector Machine and Biomedical Applications

10.1201/9781003054405-4 ◽

2021 ◽

pp. 77-103

Author(s):

Renu Dalal ◽

Manju Khari ◽

Dimple Chandra

Keyword(s):

Support Vector Machine ◽

Biomedical Applications ◽

Support Vector ◽

Software Fault ◽

Fault Proneness

Novel Grey Relational Feature Extraction Algorithm for Software Fault-Proneness Using BBO (B-GRA)

Arabian Journal for Science and Engineering ◽

10.1007/s13369-020-04445-2 ◽

2020 ◽

Vol 45 (4) ◽

pp. 2645-2662

Author(s):

Aarti ◽

Geeta Sikka ◽

Renu Dhir

Keyword(s):

Feature Extraction ◽

Feature Extraction Algorithm ◽

Extraction Algorithm ◽

Software Fault ◽

Grey Relational ◽

Fault Proneness

Deriving models of software fault-proneness

Proceedings of the 14th international conference on Software engineering and knowledge engineering - SEKE '02 ◽

10.1145/568760.568824 ◽

2002 ◽

Cited By ~ 11

Author(s):

Giovanni Denaro ◽

Sandro Morasca ◽

Mauro Pezzè

Keyword(s):

Software Fault ◽

Fault Proneness

Grey relational classification algorithm for software fault proneness with SOM clustering

International Journal of Data Mining Modelling and Management ◽

10.1504/ijdmmm.2020.10027275 ◽

2020 ◽

Vol 12 (1) ◽

pp. 28

Author(s):

Geeta Sikka ◽

Renu Dhir ◽

N.A. Aarti

Keyword(s):

Classification Algorithm ◽

Software Fault ◽

Som Clustering ◽

Grey Relational ◽

Fault Proneness ◽

Relational Classification

Efficient prediction of software fault proneness modules using support vector machines and probabilistic neural networks

2011 Malaysian Conference in Software Engineering ◽

10.1109/mysec.2011.6140679 ◽

2011 ◽

Cited By ~ 8

Author(s):

Hamdi A. Al-Jamimi ◽

Lahouari Ghouti

Keyword(s):

Neural Networks ◽

Support Vector Machines ◽

Support Vector ◽

Probabilistic Neural Networks ◽

Vector Machines ◽

Efficient Prediction ◽

Software Fault ◽

Fault Proneness

Software Fault Proneness Prediction with Group Lasso Regression: On Factors that Affect Classification Performance

2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC) ◽

10.1109/compsac.2019.10229 ◽

2019 ◽

Cited By ~ 3

Author(s):

Katerina Goseva-Popstojanova ◽

Mohammad Ahmad ◽

Yasser Alshehri

Keyword(s):

Classification Performance ◽

Group Lasso ◽

Lasso Regression ◽

Software Fault ◽

Fault Proneness

Software fault proneness prediction: a comparative study between bagging, boosting, and stacking ensemble and base learner methods

International Journal of Data Analysis Techniques and Strategies ◽

10.1504/ijdats.2017.10003991 ◽

2017 ◽

Vol 9 (1) ◽

pp. 1 ◽

Cited By ~ 7

Author(s):

Iyad Alazzam ◽

Izzat Alsmadi ◽

Mohammed Akour

Keyword(s):

Comparative Study ◽

Software Fault ◽

Base Learner ◽

Fault Proneness

Applying Machine Learning to Predict Software Fault Proneness Using Change Metrics, Static Code Metrics, and a Combination of Them

SoutheastCon 2018 ◽

10.1109/secon.2018.8478911 ◽

2018 ◽

Cited By ~ 3

Author(s):

Yasser Ali Alshehri ◽

Katerina Goseva-Popstojanova ◽

Dale G. Dzielski ◽

Thomas Devine

Keyword(s):

Machine Learning ◽

Code Metrics ◽

Change Metrics ◽

Software Fault ◽

Fault Proneness

Estimating software fault-proneness for tuning testing activities

Proceedings of the 2000 International Conference on Software Engineering. ICSE 2000 the New Millennium ◽

10.1109/icse.2000.870474 ◽

2002 ◽

Cited By ~ 1

Author(s):

G. Denaro

Keyword(s):

Software Fault ◽

Fault Proneness

Towards Industrially Relevant Fault-Proneness Models

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194003001366 ◽

2003 ◽

Vol 13 (04) ◽

pp. 395-417 ◽

Cited By ~ 12

Author(s):

Giovanni Denaro ◽

Mauro Pezzè ◽

Sandro Morasca

Keyword(s):

Experimental Validation ◽

Cross Validation ◽

Software Analysis ◽

Software Modules ◽

Software Fault ◽

Reducing Costs ◽

The Many ◽

Industrial Needs ◽

Interesting Alternative ◽

Fault Proneness

Estimating software fault-proneness early, i.e., predicting the probability of software modules to be faulty, can help in reducing costs and increasing effectiveness of software analysis and testing. The many available static metrics provide important information, but none of them can be deterministically related to software fault-proneness. Fault-proneness models seem to be an interesting alternative, but the work on these is still biased by lack of experimental validation. This paper discusses barriers and problems in using software fault-proneness in industrial environments, proposes a method for building software fault-proneness models based on logistic regression and cross-validation that meets industrial needs, and provides some experimental evidence of the validity of the proposed approach.