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Identification of escalations during product maintenance

Software defects have a major impact on the market acceptance and profitability of computer systems. Identification of bugs or issues that could lead to escalations during the maintenance phase of the products is vital and helps in increasing customer satisfaction. The objective of this paper is to identify the possible escalations and alert the engineering team to pay attention to these even before the impact of the issue is experienced. We analyze all the customer raised issues for a product by Hewlett-Packard Enterprise to identify potential escalations. We demonstrate that using machine learning techniques it is possible to identify these escalations in the product maintenance cycle with a 60% accuracy.