Y. Singh, B. Goel, and P. Chandra (India)
Corrective maintenance, SFFANNs, Software Measures.
This paper presents the application of neural networks in predicting corrective maintenance time. The paper aims to establish the viability of the usage of feedforward artificial neural network for predicting the time needed to correct errors associated with changes made to the software during maintenance. For this purpose a neural model using four software measures is proposed. The software measures used are complexity measures like cyclomatic complexity (ACC), readability of source code (RSC), documentation quality (DOQ) and understandability of software (UOS). The neural network used is Sigmoidal Feedforward Artificial Neural Network. It is found that the neural network of the type used can act as an efficient predictor of corrective maintenance time.
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