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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

A New Learning to Rank Approach for Software Defect Prediction

Author 1: Sara Al-omari
Author 2: Yousef Elsheikh
Author 3: Mohammed Azzeh

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130893

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 8, 2022.

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Abstract: Software defect prediction is one of the most active research fields in software development. The outcome of defect prediction models provides a list of the most likely defect-prone modules that need a huge effort from quality assurance teams. It can also help project managers to effectively allocate limited resources to validating software products and invest more effort in defect-prone modules. As the size of software projects grows, error prediction models can play an important role in assisting developers and shortening the time it takes to create more reliable software products by ranking software modules based on their defects. Therefore, there is need a learning-to-rank approach that can prioritize and rank defective modules to reduce testing effort, cost, and time. In this paper, a new learning to rank approach was developed to help the QA team rank the most defect-prone modules using different regression models. The proposed approach was evaluated on a set of standardized datasets using well-known evaluation measures such as Fault-Percentile Average (FPA), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Cumulative Lift Chart (CLC). Also, our proposed approach was compared with some other regression models that are used for software defect prediction, such as Random Forest (RF), Logistic Regression (LR), Support Vector Regression (SVR), Zero Inflated Regression (ZIR), Zero Inflated Poisson (ZIP), and Negative Polynomial Regression (NPR). Based on the results, the measurement criteria were different than each other as there was a gap in the accuracy obtained for defects prediction due to the nature of the random data, and thus was higher for RF and SVR, as well as FPA achieved better results than MAE and RMSE in this research paper.

Keywords: Software engineering; software testing; software defect prediction; learning to rank approach

Sara Al-omari, Yousef Elsheikh and Mohammed Azzeh, “A New Learning to Rank Approach for Software Defect Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 13(8), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130893

@article{Al-omari2022,
title = {A New Learning to Rank Approach for Software Defect Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130893},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130893},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {8},
author = {Sara Al-omari and Yousef Elsheikh and Mohammed Azzeh}
}


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