Software Fault Proneness Prediction Using Support Vector Machines
نویسندگان
چکیده
Empirical validation of software metrics to predict quality using machine learning methods is important to ensure their practical relevance in the software organizations. In this paper, we build a Support Vector Machine (SVM) model to find the relationship between object-oriented metrics given by Chidamber and Kemerer and fault proneness. The proposed model is empirically evaluated using public domain KC1 NASA data set. The performance of the SVM method was evaluated by Receiver Operating Characteristic (ROC) analysis. Based on these results, it is reasonable to claim that such models could help for planning and performing testing by focusing resources on fault-prone parts of the design and code. Thus, the study shows that SVM method may also be used in constructing software quality models.
منابع مشابه
Software Fault-proneness Prediction using Random Forest
Many metric-based classification models have been developed and applied to software fault-proneness prediction. This paper presents a novel prediction model using Random Forest classifier. Random Forest (RF) can be a promising candidate for software quality prediction because it is one of the most accurate classification algorithms available and has strengths in noise handling and efficient run...
متن کاملEffect of Principle Component Analysis and Support Vector Machine in Software Fault Prediction
Machine Learning (ML) approaches have a great impact in fault prediction. Demand for producing quality assured software in an organization has been rapidly increased during the last few years. This leads to increase in development of machine learning algorithms for analyzing and classifying the data sets, which can be used in constructing models for predicting the important quality attributes s...
متن کاملA Comparative Study of Extreme Learning Machines and Support Vector Machines in Prediction of Sediment Transport in Open Channels
The limiting velocity in open channels to prevent long-term sedimentation is predicted in this paper using a powerful soft computing technique known as Extreme Learning Machines (ELM). The ELM is a single Layer Feed-forward Neural Network (SLFNN) with a high level of training speed. The dimensionless parameter of limiting velocity which is known as the densimetric Froude number (Fr) is predicte...
متن کاملPrediction of Fault-Prone Software Modules using Statistical and Machine Learning Methods
Demand for producing quality software has rapidly increased during the last few years. This is leading to increase in development of machine learning methods for exploring data sets, which can be used in constructing models for predicting quality attributes such as fault proneness, maintenance effort, testing effort, productivity and reliability. This paper examines and compares logistic regres...
متن کاملEvaluation of Classifiers in Software Fault-Proneness Prediction
Reliability of software counts on its fault-prone modules. This means that the less software consists of fault-prone units the more we may trust it. Therefore, if we are able to predict the number of fault-prone modules of software, it will be possible to judge the software reliability. In predicting software fault-prone modules, one of the contributing features is software metric by which one ...
متن کامل