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Reuse of third-party libraries promises significant productivity improvements in software development. However, dependencies on external libraries and their APIs also introduce risks to a project and impact strategic decisions during development and maintenance. Informed decision making therefore requires a thorough understanding of the extent and nature of dependencies on external APIs. As realistically sized applications are often heavily entangled with various external APIs, gaining this understanding is infeasible with manual inspections only. To address this, we present an automated approach to analyze the dependencies of software projects on external APIs. The approach is supported by a static analysis tool featuring a visualization of the analysis results. We evaluate the approach as well as the tooling on multiple open source Java systems. [ABSTRACT FROM PUBLISHER]

Reuse recommendation systems support the developer by suggesting useful API methods, classes or code snippets based on code edited in the IDE. Existing systems based on structural information, such as type and method usage, are not effective in case of general purpose types such as String. To alleviate this, we propose a recommendation system based on identifiers that utilizes the developer's intention embodied in names of variables, types and methods. We investigate the impact of several variation points of our recommendation algorithm and evaluate the approach for recommending methods from the Java and Eclipse APIs in 9 open source systems. Furthermore, we compare our recommendations to those of a structure-based recommendation system and describe a metric for predicting the expected precision of a recommendation. Our findings indicate that our approach performs significantly better than the structure-based approach. [ABSTRACT FROM PUBLISHER]