Abstract
Software fault prediction has become quite famous in the software engineering. If software faults are predicted earlier, it leads to good quality of software, and it reduces the resources and time required for testing, which ultimately leads to saving a considerable cost and effort that are used for testing purpose. In this literature study, we studied the major works done so far in the software defect prediction paradigm to find out the various benefit of predicting faults at initial phases of software product development. This paper aims to find answers to questions such as what are the merits and limitations of models developed so far, what are the possible areas of software fault prediction paradigm that are still open for research, what are the best suitable metrics used for predicting errors, and many more. From the literature survey, we analyzed that research conducted for predicting errors in object-oriented software so far was conducted on a small scale and using the limited metric suite. None of the studies gave a generalized model that could perform well for most of the datasets. They do not handle well the problem of imbalanced class distribution and noisy data. Many prediction models have already been developed so far, but most of them focus only on classification problem that is detecting faulty/not faulty classes.
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Joon, A., Tyagi, R.K., Chillar, K. (2021). Literature Review: Predicting Faults in Object-Oriented Software. In: Agrawal, R., Kishore Singh, C., Goyal, A. (eds) Advances in Smart Communication and Imaging Systems . Lecture Notes in Electrical Engineering, vol 721. Springer, Singapore. https://doi.org/10.1007/978-981-15-9938-5_30
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DOI: https://doi.org/10.1007/978-981-15-9938-5_30
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