A Hybrid Data Preprocessing Technique based on Maximum Likelihood Logistic Regression with Filtering for Enhancing Software Defect Prediction

Bashir, Kamal; Hussein, Ahmed Saad

doi:10.1109/iske47853.2019.9170328

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…The authors of [35] suggested a framework that builds the prediction model using the Naive Bayes classification method and PCA for dimensionality reduction. The authors of [36] suggested a hybrid preprocessing strategy in which feature selection is followed by iterative partitioning filtering.…”

Section: Feature Selectionmentioning

confidence: 99%

“…Tomact [11], [12], [15], [16], [22], [28], [36], [38], [39], [47], [48], [50], [68] 13 18% Eclipse JDT core (JDT) [12], [13], [36]- [38], [43], [50], [51], [69], [73] 10 14% MC [13], [16], [26], [27], [33], [39], [49], [54], [66], [78] 10 14% PDE [12], [18], [36]- [38], [43], [50], [69], [73] 9 12% OpenIntents Safe [12], [30], [37], [38], [43], [50], [63], [69], [73] 9 12%…”

Section: Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

Software Defect Prediction Approaches Revisited

Shebl,

Afify,

Badr

2023

IJICIS

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A crucial field in software development and testing is Software Defect Prediction (SDP) because the quality, dependability, efficiency, and cost of the software are all improved by forecasting software defects at an earlier stage. Many existing models predict defects to facilitate software testing process for testers. A comprehensive review of these models from different perspectives is crucial to help new researchers enter this field and learn about its latest developments. Algorithms, method types, datasets, and tools were the only perspectives discussed in the current literature. A comprehensive study that takes into account a wide spectrum of viewpoints hasn't yet been published. Examining the development and advancement of SDP-related studies is the goal of this literature review. It provides a comprehensive and updated state-of-the-art that satisfies all stated criteria. Out of 591 papers retrieved from 6 reputable databases, 73 papers were eligible for analysis. This review addresses relevant research questions regarding techniques & method types, data details, tools, code syntax, semantics, structural and domain information. Motivation to conduct this comprehensive review is to equip the readers with the necessary information and keep them informed about the software defect prediction domain.

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Section: Feature Selectionmentioning

confidence: 99%