Support vector machine

Data mining

Computer science

Software quality

Software

Software metric

Naive Bayes classifier

Software bug

Artificial intelligence

Decision tree

Software regression

Software engineering

Programming language

Artificial neural network

Software development

Machine learning

International Journal of Advanced Computer Science and Applications

Software Bug Prediction (SBP) is an important issue in software development and maintenance processes, which concerns with the overall of software successes.This is because predicting the software faults in earlier phase improves the software quality, reliability, efficiency and reduces the software cost.However, developing robust bug prediction model is a challenging task and many techniques have been proposed in the literature.This paper presents a software bug prediction model based on machine learning (ML) algorithms.Three supervised ML algorithms have been used to predict future software faults based on historical data.These classifiers are Naïve Bayes (NB), Decision Tree (DT) and Artificial Neural Networks (ANNs).The evaluation process showed that ML algorithms can be used effectively with high accuracy rate.Furthermore, a comparison measure is applied to compare the proposed prediction model with other approaches.The collected results showed that the ML approach has a better performance.

Software Bug Prediction using Machine Learning Approach