Software maintainability prediction using soft computing

Abstract

Software maintainability is a critical component of the software development life cycle and is necessary for the efficient configuration and functioning of any software system. This thesis covers the important study on software maintainability prediction using neural networks, fuzzy logic, neuro-fuzzy, and deep learning approaches. Software maintainability can be predicted using a variety of variables, including complexity, reusability, dependability, usability, testability, understandability, quality, and others. Prompt estimation of software maintainability levels not only saves money and time, but also improves the caliber of software output. Furthermore, it is evident that soft computing techniques play a significant role in automatically and fairly reliably predicting software maintainability levels. newline