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A Conceptual Framework for Software Fault Prediction Using Neural Networks

  • Camelia SerbanEmail author
  • Florentin BotaEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1126)

Abstract

Software testing is a very expensive and critical activity in the software systems’ life-cycle. Finding software faults or bugs is also time-consuming, requiring good planning and a lot of resources. Therefore, predicting software faults is an important step in the testing process to significantly increase efficiency of time, effort and cost usage.

In this study we investigate the problem of Software Faults Prediction (SFP) based on Neural Network. The main contribution is to empirically establish the combination of Chidamber and Kemer software metrics that offer the best accuracy for faults prediction with numeric estimations by using feature selection. We also proposed a conceptual framework that integrates the model for fault prediction.

Keywords

Software faults Software metrics Machine learning 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Babes-Bolyai UniversityCluj-NapocaRomania

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