Abstract
Bug report classification is an important phase of software engineering process. It falls under the testing and maintenance phase which is an important and time taking process. In today’s agile world it’s very important to deliver the software in less time without affecting the quality of software. It is the job of bug trigger to classify the bugs based on criticality. If bugs are classified incorrectly then it will induce a delay in the system as bugs with a high priority will not be dealt at the right time. This task done manually is prone to errors, thus there is a need for automatic classification of bugs to help the trigger. This paper proposed convolution neural network with L1 and L2 regularization compared with machine learning approach. Experimental analysis shows that all classes achieve significant improvement in results as compared to the previous approaches.
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Chauhan, A., Kumar, R. (2020). Bug Severity Classification Using Semantic Feature with Convolution Neural Network. In: Iyer, B., Deshpande, P., Sharma, S., Shiurkar, U. (eds) Computing in Engineering and Technology. Advances in Intelligent Systems and Computing, vol 1025. Springer, Singapore. https://doi.org/10.1007/978-981-32-9515-5_31
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DOI: https://doi.org/10.1007/978-981-32-9515-5_31
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