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Resources allocated for software quality assurance and improvement have not increased with the increasing need for software quality improvement.

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A targeted software quality inspection can detect faulty modules and reduce the number of failures that occur during operations. We present case-based reasoning (CBR) software failure prediction modeling approach, a part of the computational intelligence field that focuses on automated reasoning processes. A CBR system works like a software failure prediction model by quantifying, for a module under development, the expected number of failures based on similar modules that were previously developed.

Said system is composed of a similarity function, the number of nearest neighbor cases used for failure prediction, and a solution algorithm. The selection of a particular similarity function and solution algorithm can affect the performance accuracy of a CBR-based software failure prediction system. This article presents an empirical study investigating the effects of using three different similarity functions and two different solution algorithms on the prediction accuracy of our CBR system.

The influence of varying the number of nearest neighbor cases on performance accuracy is also explored. In addition, the benefits of using metric selection procedures for our CBR system are also evaluated. Case studies of a large legacy telecommunications system are used for our analysis. It is observed that the CBR system using the Mahalanobis distance similarity function and the inverse distance weighted solution algorithm produced the best failure prediction. Furthermore, CBR models perform better than models based on multiple linear regression.

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