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Empirical studies on software product maintainability pbkp_rediction: A systematic mapping and review

Abstract:

Background: Software product maintainability pbkp_rediction (SPMP) is an important task to control software maintenance activity, and many SPMP techniques for improving software maintainability have been proposed. In this study, we performed a systematic mapping and review on SPMP studies to analyze and summarize the empirical evidence on the pbkp_rediction accuracy of SPMP techniques in current research. Objective: The objective of this study is twofold: (1) to classify SPMP studies reported in the literature using the following criteria: publication year, publication source, research type, empirical approach, software application type, datasets, independent variables used as pbkp_redictors, dependent variables (e.g. how maintainability is expressed in terms of the variable to be pbkp_redicted), tools used to gather the pbkp_redictors, the successful pbkp_redictors and SPMP techniques, (2) to analyze these studies from three perspectives: pbkp_rediction accuracy, techniques reported to be superior in comparative studies and accuracy comparison of these techniques. Methodology: We performed a systematic mapping and review of the SPMP empirical studies published from 2000 up to 2018 based on an automated search of nine electronic databases. Results: We identified 82 primary studies and classified them according to the above criteria. The mapping study revealed that most studies were solution proposals using a history-based empirical evaluation approach, the datasets most used were historical using object-oriented software applications, maintainability in terms of the independent variable to be pbkp_redicted was most frequently expressed in terms of the number of changes made to the source code, maintainability pbkp_redictors most used were those provided by Chidamber and Kemerer (C&K), Li and Henry (L&H) and source code size measures, while the most used techniques were ML techniques, in particular artificial neural networks. Detailed analysis revealed that fuzzy & neuro fuzzy (FNF), artificial neural network (ANN) showed good pbkp_rediction for the change topic, while multilayer perceptron (MLP), support vector machine (SVM), and group method of data handling (GMDH) techniques presented greater accuracy pbkp_rediction in comparative studies. Based on our findings SPMP is still limited. Developing more accurate techniques may facilitate their use in industry and well-formed, generalizable results be obtained. We also provide guidelines for improving the maintainability of software.