Abstract
Nowadays, the challenge is how to exactly understand and apply various techniques to discover fault from the software module. Machine learning is the process of automatically discovering useful information in knowledgebase. It also provides capabilities to predict the outcome of future solutions. Case-based reasoning is a tool or method to predict error level with respect to LOC and development time in software module. This paper presents some new ideas about process and product metrics to improve software quality prediction. At the outset, it deals with the possibilities of using lines of code and development time from any language may be compared and be used as a uniform metric. The system predicts the error level with respect to LOC and development time, and both are responsible for checking the developer’s ability or efficiency of the developer. Prediction is based on the analogy. We have used different similarity measures to find the best method that increases the correctness. The present work is also credited through introduction of some new terms such as coefficient of efficiency, i.e., developer’s ability and normalizer. In order to obtain the result, we have used indigenous tool.
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Rashid, E., Patnaik, S., Usmani, A. (2015). Machine Learning and Its Application in Software Fault Prediction with Similarity Measures. In: Sethi, I. (eds) Computational Vision and Robotics. Advances in Intelligent Systems and Computing, vol 332. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2196-8_5
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DOI: https://doi.org/10.1007/978-81-322-2196-8_5
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