An approach to semantic search on technical documentation based on machine learning algorithm for customer request resolution automation
Abstract
The software sustaining phase is one of the essential stages of the software development life cycle. At this stage, customers can contact support and software engineers to request a resolution of any problem they meet during software utilization including questions of how to operate with software, where to find the information about particular functions and other relevant questions on software product. The work is devoted to research in the field of software sustainment automation. The distinctive feature of the article is the suggested semantic documentation search approach with the Doc2Vec machine learning algorithm, which allows automating automate customer requests resolution. Proposed semantic search is performed on documentation files, like PDFs, Microsoft Office documents, wiki pages and other text files with relevant information about the product. Documentation files, including page numbers, that have the closest semantic similarity to the textual description of an unresolved customer request, help the developer resolve the incoming request more efficiently and in a shorter time. The proposed approach is implemented in a software tool to automate the analysis of unresolved customer requests and provide recommendations to help in solving each of those requests. The results show the advantages of using the tool in the process of software product support.