A Systematic Literature Review On Fault Prediction Performance In Software Engineering

Many software defect prediction datasets, methods and frameworks are published disparate and complex, thus a comprehensive picture of the current state of defect prediction research that exists is missing. This literature review aims to identify and analyze the research trends, datasets, methods and frameworks used in software defect prediction research betweeen 20. Simulated Annealing Neural Network for Software Failure Prediction. Software Fault Prediction Using Quad Tree-Based K-Means Clustering Algorithm.

However, remote access to EBSCO's databases from non-subscribing institutions is not allowed if the purpose of the use is for commercial gain through cost reduction or avoidance for a non-subscribing institution.

ORCID i D Dian Nuswantoro Indonesia Romi Satria Wahono. D in Software Engineering and Machine Learning from Universiti Teknikal Malaysia Melaka. IEEE Transactions on Evolutionary Computation, 7(6), 561–575.

In addition, 64.79% of the research studies used public datasets and 35.21% of the research studies used private datasets.

Nineteen different methods have been applied to predict software defects.

Expert Systems with Applications, 36(4), 7346–7354.

A systematic review of software fault prediction studies.

Feature selection has been applied to these combinations when models are performing particularly well.

Conclusion: The methodology used to build models seems to be influential to predictive performance.

The results of this research also identified three frameworks that are highly cited and therefore influential in the software defect prediction field.

Expert Systems with Applications, 38(4), 4626–4636.

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