Software fault prediction is used to improve the testing efficiency and software quality by earlier identification of software faults associated with software. The identification of faults is usually carried out using the task of classification. The task of classification utilizes the code attributes and other features to predict the fault instances. The detection of software faults is prominently affected by a poor classification decision and hence an improved decision-making model is required to predict the patterns using the attributes collected out from the datasets. In the first phase of research is Bayes Decision classifier associated with the finding of error probabilities and integrals in software fault prediction. The fundamental software error is prediction using feature and classifier data and software error prediction with fault predictable region that includes Chernoff Bound and Bhattacharyya Bound. The Bayesian decision algorithm with error probabilities and integrals of fault predictions learning model is used to predict the software faults. It works on two different bounds namely Chernoff Bound and Bhattacharyya Bound. In the second phase of the researches an Ensemble-SVMGA learning model to predict software faults. It works on two different modules namely Ensemble-SVM and GA model for feature extraction and fault classification. The GA performs the former task and SVM the latter task. The performance of the methods is tested against several other machine learning classifiers over collected software fault datasets. The methods and evaluated against various performance metrics such as detection Rate or recall rate, False Alarm Rate, Balance, Area Under Curve and Accuracy. The outcome of research is GA-Ensemble weighted SVM has higher accuracy and AUC than other methods and provides good balance than other methods. The accuracy, AUC and Balance for other methods are slightly lesser than the ESVM-GA classifier for diagnosing the faults against several datasets.
Singaravel Gnanasambandam will be speaking at International Congress on Software Engineering 2021 which is scheduled to happen on 13th and 14th August 2021 at Hong Kong, HKSAR.