Download PDFOpen PDF in browser

Enhancing Deep Learning Capabilities with Genetic Algorithm for Detecting Software Defects

EasyChair Preprint no. 3760

11 pagesDate: July 6, 2020


Regardless of existing and well-defined processes, some defects are inevitable, resulting in software performance degradation. The use of traditional machine learning techniques can automate the prediction of software defects. This automated approach significantly improves the quality of the finished product and reduces the cost incurred during development and maintenance stages. The accuracy of artificial neural networks for the automatic prediction of software bugs, can be further enhanced with the use of metaheuristics algorithms. We propose a hybrid approach which combines Genetic Algorithm (GA) and Deep Neural Network (DNN) to better classify software defects. GA is used as a pre-learning phase to automatically optimize the input features for the DNN, as irrelevant variables have a substantial negative impact on the prediction accuracy. Results from experiments using the PROMISE dataset, demonstrates that a DNN consuming optimized features yields better results.

Keyphrases: Deep Neural Network, Genetic Algorithm, machine learning, software defect prediction

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Kajal Tameswar and Geerish Suddul and Kumar Dookhitram},
  title = {Enhancing Deep Learning Capabilities with Genetic Algorithm for Detecting Software Defects},
  howpublished = {EasyChair Preprint no. 3760},

  year = {EasyChair, 2020}}
Download PDFOpen PDF in browser