Hybrid Maintainability Prediction using Soft Computing Techniques

Abstract

Effective software maintenance is a crucial factor to measure that can be achieved with the help of software metrics. In this paper, authors derived a new approach for measuring the maintainability of software based on hybrid metrics that takes advantages of both i.e. static metrics and dynamic metrics in an object-oriented environment whereas, dynamic metrics capture the run time features of object-oriented languages i.e. run time polymorphism, dynamic binding etc. which is not covered by static metrics. To achieve this, the authors proposed a model based on static and hybrid metrics to measure maintainability factor by using soft computing techniques and it is found that the proposed neuro-fuzzy model was trained well and predict adequate results with MAE 0.003 and RMSE 0.009 based on hybrid metrics. Additionally, the proposed model was validated on two test datasets and it is concluded that the proposed model performed well, based on hybrid metrics.

Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components

“…In the same context, Schnappinger et al [23] resorted to engaging experts in order to manually label a set of data regarding their maintainability degree and made use of a set of various metrics to evaluate maintainability. Other recent research works examine various metrics and approaches for evaluating maintainability, such as the examination of open source projects and their maintainability degree [24], the combination of machine learning techniques such as the Bayesian networks and association rules [25], the use of soft computing techniques such as the neuro-fuzzy model [26] or employing ensembles to predict maintainability on imbalanced data [27]. Other approaches involve the analysis of software releases as a way for evaluating the maintainability degree of software.…”