Assessing software maintainability in systems by leveraging fuzzy methods and linguistic analysis

2019-04-30Beyond the functional requirements of a system, software maintainability is essential for project success. While many metrics for software maintainability have been developed, the effective use of accuracy measures for these metrics has not been observed. Moreover, while there exists a large knowledge base of software maintainability in the forms of ontology and standards, this knowledge is rarely used. Especially in open source ecosystems, due to the large number of developers and inefficiency in identifying quality issues, it is extremely difficult to accurately measure and predict software maintainability. ❧ In this dissertation, a set of deep analysis of the effectiveness of the current metrics in the open source ecosystems has been reported. Based on the findings, a novel approach is introduced for better assessment of the overall software maintainability through fuzzy methods and linguistic analysis on issues summaries. Expert input and a large data set of over 60,000 issues summaries found in 22 well-established open source projects from three major open source ecosystems are used to build, validate and evaluate the approach. ❧ The results validate the generalizability of the proposed approach in correctly and automatically identifying software maintainability related issues. When compared to some state-of-the-art measurements, the proposed approach is a more suitable metric to reflect the effort needed to understand the existing source code and the changes between versions. Further analysis on these projects provides a means for identifying the trend of software maintainability changes as software evolves. This informs which areas should be focused on to ensure maintainability at different stages of the development and maintenance process. Furthermore, this analysis shows the differences of software maintainability in different classifications and the relationships among software qualities that contribute to software maintainability