Abstract
Software developers in large projects work in complex information landscapes, and staying on top of all relevant software artifacts is challenging. As software systems often evolve for years, a high number of issue reports is typically managed during the lifetime of a system. Efficient management of incoming issue requires successful navigation of the information landscape. In our work, we address two important work tasks involved in issue management: Issue Assignment (IA) and Change Impact Analysis (CIA). IA is the early task of allocating an issue report to a development team. CIA deals with identifying how source code changes affect the software system, a fundamental activity in safety-critical development. Our solution approach is to support navigation, both among development teams and software artifacts, based on information available in historical issue reports. We present how we apply techniques from machine learning and information retrieval to develop recommendation systems. Finally, we report intermediate results from two controlled experiments and an industrial case study.
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