Application of adaptive neuro-fuzzy inference system for predicting software change proneness | IEEE Conference Publication | IEEE Xplore

Application of adaptive neuro-fuzzy inference system for predicting software change proneness


Abstract:

In this paper, we model the relationship between object-oriented metrics and software change proneness. We use adaptive neuro-fuzzy inference system (ANFIS) to calculate ...Show More

Abstract:

In this paper, we model the relationship between object-oriented metrics and software change proneness. We use adaptive neuro-fuzzy inference system (ANFIS) to calculate the change proneness for the two commercial open source software systems. The performance of ANFIS is compared with other techniques like bagging, logistic regression and decision trees. We use the area under receiver operating characteristic (ROC) curve to determine the effectiveness of the model. The present analysis shows that of all the techniques investigated, ANFIS gives the best results for both the software systems. We also calculate the sensitivity and specificity for each technique and use it as a measure to evaluate the model effectiveness. The aim of the study is to know the change prone classes in the early phases of software development so as to plan the allocation of testing resources effectively and thus improve software maintainability.
Date of Conference: 22-25 August 2013
Date Added to IEEE Xplore: 21 October 2013
ISBN Information:
Conference Location: Mysore, India

References

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