fine-GRAPE: fine-grained APi usage extractor – an approach and dataset to investigate API usage

Abstract

An Application Programming Interface (API) provides a set of functionalities to a developer with the aim of enabling reuse. APIs have been investigated from different angles such as popularity usage and evolution to get a better understanding of their various characteristics. For such studies, software repositories are mined for API usage examples. However, many of the mining algorithms used for such purposes do not take type information into account. Thus making the results unreliable. In this paper, we aim to rectify this by introducing fine-GRAPE, an approach that produces fine-grained API usage information by taking advantage of type information while mining API method invocations and annotation. By means of fine-GRAPE, we investigate API usages from Java projects hosted on GitHub. We select five of the most popular APIs across GitHub Java projects and collect historical API usage information by mining both the release history of these APIs and the code history of every project that uses them. We perform two case studies on the resulting dataset. The first measures the lag time of each client. The second investigates the percentage of used API features. In the first case we find that for APIs that release more frequently clients are far less likely to upgrade to a more recent version of the API as opposed to clients of APIs that release infrequently. The second case study shows us that for most APIs there is a small number of features that is actually used and most of these features relate to those that have been introduced early in the APIs lifecycle.

On the Reaction to Deprecation of 25,357 Clients of 4+1 Popular Java APIs

“…Finally, the fourth uses the Eclipse JDT AST parser to mine type-resolved invocations from a source code file. We created a method, fine-GRAPE, based on the last approach [18], [19] that meets the following requirements: 3 (1) fine-GRAPE handles the largescale data in GitHub, (2) it does not depend on building the client code, (3) it results in a type-checked API usage dataset, (4) it collects explicit version usage information, and (5) it processes the whole history of each client.…”

“…This is reflected in the Java Language Specification(JLS). However, the standard Sun JDK 3 More details on fine-GRAPE can be found in our prior work [19].…”

On the reaction to deprecation of clients of 4 + 1 popular Java APIs and the JDK

“…APIs enable this: To cite a single example, we found at least 15,000 users of the Spring API (Sawant and Bacchelli 2016).…”

To react, or not to react: Patterns of reaction to API deprecation

“…To understand what features of an API consumers use, one can select from different proposed approaches that collect API usage data, e.g., MAPO by Xie and Pei (2006) and SOURCERER by Bajracharya et al (2006). We lean on the technique FINE-GRAPE developed by Sawant and Bacchelli (2017). This technique gives us three advantages: (1) it uses Maven-based Java projects, (2) it results in a type-checked API usage dataset, (2) it determines the API usages over the entire history of a given project.…”