Sahand: A Software Fault-Prediction Method Using Autoencoder Neural Network and K-Means Algorithm

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dc.authoridKiani, Farzad/0000-0002-0354-9344
dc.authoridArasteh, Bahman/0000-0001-5202-6315
dc.authorwosidKiani, Farzad/O-3363-2013
dc.contributor.authorArasteh, Bahman
dc.contributor.authorGolshan, Sahar
dc.contributor.authorShami, Shiva
dc.contributor.authorKiani, Farzad
dc.date.accessioned2024-05-19T14:46:43Z
dc.date.available2024-05-19T14:46:43Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractSoftware is playing a growing role in many safety-critical applications, and software systems dependability is a major concern. Predicting faulty modules of software before the testing phase is one method for enhancing software reliability. The ability to predict and identify the faulty modules of software can lower software testing costs. Machine learning algorithms can be used to solve software fault prediction problem. Identifying the faulty modules of software with the maximum accuracy, precision, and performance are the main objectives of this study. A hybrid method combining the autoencoder and the K-means algorithm is utilized in this paper to develop a software fault predictor. The autoencoder algorithm, as a preprocessor, is used to select the effective attributes of the training dataset and consequently to reduce its size. Using an autoencoder with the K-means clustering method results in lower clustering error and time. Tests conducted on the standard NASA PROMIS data sets demonstrate that by removing the inefficient elements from the training data set, the proposed fault predictor has increased accuracy (96%) and precision (93%). The recall criteria provided by the proposed method is about 87%. Also, reducing the time necessary to create the software fault predictor is the other merit of this study.en_US
dc.identifier.doi10.1007/s10836-024-06116-8
dc.identifier.issn0923-8174
dc.identifier.issn1573-0727
dc.identifier.scopus2-s2.0-85190088218en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org10.1007/s10836-024-06116-8
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5581
dc.identifier.wosWOS:001201327500001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Electronic Testing-Theory and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectSoftware Fault Predictionen_US
dc.subjectClusteringen_US
dc.subjectAutoencoderen_US
dc.subjectK-Meansen_US
dc.subjectAccuracyen_US
dc.titleSahand: A Software Fault-Prediction Method Using Autoencoder Neural Network and K-Means Algorithmen_US
dc.typeArticleen_US

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