Abstract
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%.
Highlights
Waikato Environment for Knowledge Analysis (WEKA) consists of algorithms and visualization tools which are used for predictive modeling and data analysis [37]
Reverse engineering can only be applied if there are fewer errors or defects in the software
The business value of the software increases if there is smaller number of errors or defects
Summary
Software engineering consists of process, product, and resources entities. Processes are related to software activities, e.g., specification, design, or testing [1]. Products are correlated to documents which result from the activity of the process, e.g., specification, design document, deliverables, artifacts, etc. Resources are related to entities that are considered by the process activity, e.g., personnel, CASE tools, or hardware [3]. The entities consist of external and internal attributes. External attributes describe the entity behavior while internal attributes are those that portray the entity itself. In literature [4], machine learning applications are incorporated with software engineering tasks for: (i) predicting external or internal attributes of resources, product, or process, (ii) recycling of processes or product, (iii) improving the processes by retrieving the specification, etc
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