Identifying Components from Object-Oriented APIs Based on Dynamic Analysis

TL;DR

This paper presents a dynamic analysis-based method for identifying reusable components in object-oriented APIs by examining real client interactions, overcoming static analysis limitations, and demonstrating high precision on Java APIs.

Contribution

It introduces a novel dynamic analysis approach that considers API co-usage dependencies to improve component identification accuracy.

Findings

High precision in component identification

Effective handling of dynamic features like polymorphism

Validated on multiple Java APIs with real client applications

Abstract

The reuse at the component level is generally more effective than the one at the object-oriented class level. This is due to the granularity level where components expose their functionalities at an abstract level compared to the fine-grained object-oriented classes. Moreover, components clearly define their dependencies through their provided and required interfaces in an explicit way that facilitates the understanding of how to reuse these components. Therefore, several component identification approaches have been proposed to identify components based on the analysis object-oriented software applications. Nevertheless, most of the existing component identification approaches did not consider co-usage dependencies between API classes to identify classes/methods that can be reused to implement a specific scenario. In this paper, we propose an approach to identify reusable software components in object-oriented APIs, based on the interactions between client applications and the targeted API. As we are dealing with actual clients using the API, dynamic analysis allows to better capture the instances of API usage. Approaches using static analysis are usually limited by the difficulty of handling dynamic features such as polymorphism and class loading. We evaluate our approach by applying it to three Java APIs with eight client applications from the DaCapo benchmark. DaCapo provides a set of pre-defined usage scenarios. The results show that our component identification approach has a very high precision.