Revealing the Effect of Coding Practices on Software Maintainability

2013 ◽  
Author(s):  
Peter Hegedus
Keyword(s):  
Software Maintainability

scholarly journals Measuring Software Maintainability with Naïve Bayes Classifier

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 136
Author(s):  
Nayyar Iqbal ◽  
Jun Sang ◽  
Jing Chen ◽  
Xiaofeng Xia
Keyword(s):  
Business Processes ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Business Value ◽  
Software Components ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Error Type ◽  
Software Maintainability

Software products in the market are changing due to changes in business processes, technology, or new requirements from the customers. Maintainability of legacy systems has always been an inspiring task for the software companies. In order to determine whether the software requires maintainability by reverse engineering or by forward engineering approach, a system assessment was done from diverse perspectives: quality, business value, type of errors, etc. In this research, the changes required in the existing software components of the legacy system were identified using a supervised learning approach. New interfaces for the software components were redesigned according to the new requirements and/or type of errors. Software maintainability was measured by applying a machine learning technique, i.e., Naïve Bayes classifier. The dataset was designed based on the observations such as component state, successful or error type in the component, line of code of error that exists in the component, component business value, and changes required for the component or not. The results generated by the Waikato Environment for Knowledge Analysis (WEKA) software confirm the effectiveness of the introduced methodology with an accuracy of 97.18%.

Assessment of Software Maintainability Evolution Using C&K Metrics

2013 ◽  
Vol 11 (5) ◽  
pp. 1232-1237
Author(s):  
Nelson Barbosa ◽  
Kechi Hirama
Keyword(s):  
Software Maintainability
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