Application of neural networks for software quality prediction using object-oriented metrics

By Mie Mie Thet Thwin, Tong-Seng Quah. International Conference on Software Maintenance, 2003. ICSM 2003. Proceedings., 2003

340 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/1043808936cbe4820362226ec002197d66e6b6e3

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Citations in the corpus

Using the Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems (2008, background): “…to be useful in different tasks [24], including the assessment of design quality [5], [13], productivity, design, and reuse effort [18], prediction of software quality, fault prediction [30], [39], [69], modularization of software [14], [54], and identification of reusable of components [32], [50].”
A systematic review of software fault prediction studies (2009, neutral): “Many researchers used different approaches such as genetic programming (Evett, Khoshgoftaar, Chien, & Allen, 1998), neural networks (Thwin & Quah, 2003), case-based reasoning (El Emam, Benlarbi, Goel, & Rai, 2001), fuzzy logic (Yuan, Khoshgoftaar, Allen, & Ganesan, 2000), Dempster–Shafer networks…”
Dynamic coupling measurement for object-oriented software (2004, background): “static coupling measurement to assess the maintainability of object-oriented systems [25, 31 ].”
Software fault prediction: A literature review and current trends (2011, methodology): “Thwin and Quah (2003) predicted software quality based on General Regression Neural Network (GRNN) and Ward Neural Networks.”
Using Class Imbalance Learning for Software Defect Prediction (2013, methodology): “A variety of machine learning methods have been proposed and compared for SDP problems, such as decision trees [28], neural networks [10], [29], Naive Bayes [3], [30], support vector machines [31], and Artificial Immune Systems [4].”
Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults (2006, background): “Previous research on OO design metrics has shown some of them to be useful for predicting the fault-proneness of classes [1], [2], [3], [4], [5], [6], [7], [8], [9], yet has also made the point that this usefulness depends upon their being valid whatever the degree of severity of the fault [6].”