From source code to test cases: A comprehensive benchmark for resource leak detection in Android apps

• Published in: Software, practice & experience Vol. 49; no. 3; pp. 540 - 548
• Main Authors: Riganelli, Oliviero, Micucci, Daniela, Mariani, Leonardo
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.03.2019; John Wiley and Sons Inc
• Subjects: Android app; Applications programs; benchmark; Benchmarks; bug detection; Global positioning systems; GPS; Leak detection; Leaks; Policies; Researchers; resource leak; Short Communication; Short Communications; Source code; Android app; bug detection; resource leak; benchmark
• ISSN: 0038-0644; 1097-024X; 1097-024X
• DOI: 10.1002/spe.2672

Summary Android apps share resources, such as sensors, cameras, and Global Positioning System, that are subject to specific usage policies whose correct implementation is left to programmers. Failing to satisfy these policies may cause resource leaks, that is, apps may acquire but never release resources. This might have different kinds of consequences, such as apps that are unable to use resources or resources that are unnecessarily active wasting battery. Researchers have proposed several techniques to detect and fix resource leaks. However, the unavailability of public benchmarks of faulty apps makes comparison between techniques difficult, if not impossible, and forces researchers to build their own data set to verify the effectiveness of their techniques (thus, making their work burdensome). The aim of our work is to define a public benchmark of Android apps affected by resource leaks. The resulting benchmark, called AppLeak, is publicly available on GitLab and includes faulty apps, versions with bug fixes (when available), test cases to automatically reproduce the leaks, and additional information that may help researchers in their tasks. Overall, the benchmark includes a body of 40 faults that can be exploited to evaluate and compare both static and dynamic analysis techniques for resource leak detection.