Abstract
Software maintenance is one of the key stages in the software lifecycle and it includes a variety of activities that consume the significant portion of the costs of a software project. Previous research suggest that future software maintainability can be predicted, based on various source code aspects, but most of the research focuses on the prediction based on the present state of the code and ignores its history. While taking the history into account in software maintainability prediction seems intuitive, the research empirically testing this has not been done, and is the main goal of this paper. This paper empirically evaluates the contribution of historical measurements of the Chidamber & Kemerer (C&K) software metrics to software maintainability prediction models. The main contribution of the paper is the building of the prediction models with classification and regression trees and random forest learners in iterations by adding historical measurement data extracted from previous releases gradually. The maintainability prediction models were built based on software metric measurements obtained from real-world open-source software projects. The analysis of the results show that an additional amount of historical metric measurements contributes to the maintainability prediction. Additionally, the study evaluates the contribution of individual C&K software metrics on the performance of maintainability prediction models.
Highlights
Modern societies are highly dependent on complex, large-scale, software-intensive systems that operate increasingly within an environment of continuous availability, which is challenging to maintain and evolves in response to the inevitable changes in stakeholder goals and requirements of the systems [1]
The main goal of the study was to evaluate the contribution of this information to the performance of software maintainability prediction models
The latter was achieved by increasingly adding changes in the Chidamber & Kemerer (C&K) software metrics measurements of historic releases to the maintainability prediction model
Summary
Modern societies are highly dependent on complex, large-scale, software-intensive systems that operate increasingly within an environment of continuous availability, which is challenging to maintain and evolves in response to the inevitable changes in stakeholder goals and requirements of the systems [1]. Such software products undergo constant change to adapt to a changing environment [2]. Software evolution is an important aspect in the field of software development that has to be addressed properly [3]. Software maintainability is one of the most important aspects when evaluating the quality of a software product [6] and is one of key stages in the software development lifecycle [7]
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