Deep Learning Approach for Software Maintainability Metrics Prediction

Sudan Jha, Raghvendra Kumar, Le Hoang Son, Mohamed Abdel-Basset, Ishaani Priyadarshini, Rohit Sharma, Hoang Viet Long

Research output: Contribution to journalArticlepeer-review

87 Citations (Scopus)


Software maintainability predicts changes or failures that may occur in software after it has been deployed. Since it deals with the degree to which an application may be understood, repaired, or enhanced, it also takes into account the overall cost of the project. In the past, several measures have been taken into account for predicting metrics that influence software maintainability. However, deep learning is yet to be explored for the same. In this paper, we perform deep learning for software maintainability metrics' prediction on a large number of datasets. Unlike the previous research works, we have relied on large datasets from 299 software and subsequently applied various metrics and functions to the same; 29 object-oriented metrics have been considered along with their impact on software maintainability of open source software. Several metrics have been analyzed and descriptive statistics of these metrics have been pointed out. The proposed long short term memory has been evaluated using measures, such as mean absolute error, root mean square error and accuracy. Five machine learning algorithms, namely, ridge regression with variable selection, decision tree, quantile regression forest, support vector machine, and principal component analysis have been applied to the original datasets, as well as, to the refined datasets. It was found that this paper provides results in the form of metrics that may be used in the prediction of software maintenance and the proposed deep learning model outperforms all of the other methods that were considered. Furthermore, the results of experiment affirm the efficiency of the proposed deep learning model for software maintainability prediction.

Original languageEnglish
Article number8698760
Pages (from-to)61840-61855
Number of pages16
JournalIEEE Access
Publication statusPublished - 25 Apr 2019
Externally publishedYes


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