Software Maintainability Prediction Model for Object-Oriented Software Systems Based On Sensitivity-Based Linear Learning Method

This paper presented a new maintainability prediction model for an object-oriented (OO) software system based on the recently introduced learning algorithm called Sensitivity Based Linear Learning Method (SBLLM) for two-layer feedforward neural networks.. As the number of object-oriented software systems increases, it becomes more important for organizations to maintain those systems effectively. However, currently only a small number of maintainability prediction models are available for object oriented systems. In this work, we develop Sensitivity Based Linear Learning maintainability prediction model for an object-oriented software system. The model was constructed using popular object-oriented metric datasets, collected from different object-oriented systems. Prediction accuracy of the model was evaluated and compared with commonly used regression-based models and also with Bayesian network based model which was earlier developed using the same datasets. Empirical results from simulation show that our SBLLM based model produced promising results in term of prediction accuracy measures authorized in OO software maintainability literatures, better than most of the other earlier implemented models on the same datasets.