Frameworks and libraries offer reusable and customizable functionality through Application Programming Interfaces (APIs). Correctly using large and sophisticated APIs can represent a challenge due to hidden assumptions and requirements. Numerous approaches have been developed to infer properties of APIs, intended to guide their use by developers. With each approach come new definitions of API properties, new techniques for inferring these properties, and new ways to assess their correctness and usefulness. This paper provides a comprehensive survey of over a decade of research on automated property inference for APIs. Our survey provides a synthesis of this complex technical field along different dimensions of analysis: properties inferred, mining techniques, and empirical results. In particular, we derive a classification and organization of over 60 techniques into five different categories based on the type of API property inferred: unordered usage patterns, sequential usage patterns, behavioral specifications, migration mappings, and general information.

Numerous techniques involve mining change data captured in software archives to assist engineering efforts, for example to identify components that tend to evolve together. We observed that important changes to software artifacts are sometimes accompanied by numerous non-essential modifications, such as local variable refactorings, or textual differences induced as part of a rename refactoring. We developed a tool-supported technique for detecting non-essential code differences in the revision histories of software systems. We used our technique to investigate code changes in over 24,000 change sets gathered from the change histories of seven long-lived open-source systems. We found that up to 15.5% of a system's method updates were due solely to non-essential differences. We also report on numerous observations on the distribution of non-essential differences in change history and their potential impact on change-based analyses.

Software projects often rely on third-party libraries made accessible through Application Programming Interfaces (APIs). We have observed many cases where APIs are used in ways that are not the most effective. We developed a technique and tool support to automatically detect such patterns of API usage in software projects. The main hypothesis underlying our technique is that client code imitating the behavior of an API method without calling it may not be using the API effectively because it could instead call the method it imitates. Our technique involves analyzing software systems to detect cases of API method imitations. In addition to warning developers of potentially re-implemented API methods, we also indicate how to improve the use of the API. Applying our approach on 10 Java systems revealed over 400 actual cases of potentially suboptimal API usage, leading to many improvements to the quality of the code we studied.