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Automated traceability techniques based on supervised machine learning algorithms can significantly reduce the cost and effort needed to create and maintain traceability links between requirements, architecture and source code. However, the upfront cost to train these algorithms is the main bottleneck for expanding, and validating these traceability techniques as well as applying them to complex industrial systems. In this tool demo, we present our Web-based tool named BUDGET, as a solution to automate creation of training data for the problem of tracing architectural concerns. BUDGET uses Automated Web-Mining, and Big-Data Analysis techniques to generate training data for supervised architecture-traceability techniques. It uses several sampling strategies and mines ultra-large scale code repositories to generate datasets of tactical code snippets. The BUDGET falls in the research tool category and supports researchers in the area of software architecture and requirements engineering.
Santos et al. (Fri,) studied this question.