Lili Wei

Julian Harty, Haonan Zhang, Lili Wei et al.

Software logs are of great value in both industrial and open-source projects. Mobile analytics logging enables developers to collect logs remotely from their apps running on end user devices at the cost of recording and transmitting logs across the Internet to a centralised infrastructure. This paper makes a first step in characterising logging practices with a widely adopted mobile analytics logging library, namely Firebase Analytics. We provide an empirical evaluation of the use of Firebase Analytics in 57 open-source Android applications by studying the evolution of code-bases to understand: a) the needs-in-common that push practitioners to adopt logging practices on mobile devices, and b) the differences in the ways developers use local and remote logging. Our results indicate mobile analytics logs are less pervasive and less maintained than traditional logging code. Based on our analysis, we believe logging using mobile analytics is more user centered compared to traditional logging, where the latter is mainly used to record information for debugging purposes.

Yepang Liu, Lili Wei, Chang Xu et al.

Resource leak bugs in Android apps are pervasive and can cause serious performance degradation and system crashes. In recent years, several resource leak detection techniques have been proposed to assist Android developers in correctly managing system resources. Yet, there exist no common bug benchmarks for effectively and reliably comparing such techniques and quantitatively evaluating their strengths and weaknesses. This paper describes our initial contribution towards constructing such a benchmark. To locate real resource leak bugs, we mined 124,215 code revisions of 34 large-scale open-source Android apps. We successfully found 298 fixed resource leaks, which cover a diverse set of resource classes, from 32 out of the 34 apps. To understand the characteristics of these bugs, we conducted an empirical study, which revealed the root causes of frequent resource leaks in Android apps and common patterns of faults made by developers. With our findings, we further implemented a static checker to detect a common pattern of resource leaks in Android apps. Experiments showed that the checker can effectively locate real resource leaks in popular Android apps, confirming the usefulness of our work.

Wuqi Zhang, Lili Wei, Shuqing Li et al.

Since the emergence of Ethereum, blockchain-based decentralized applications (DApps) have become increasingly popular and important. To balance the security, performance, and costs, a DApp typically consists of two layers: an on-chain layer to execute transactions and store crucial data on the blockchain and an off-chain layer to interact with users. A DApp needs to synchronize its off-chain layer with the on-chain layer proactively. Otherwise, the inconsistent data in the off-chain layer could mislead users and cause undesirable consequences, e.g., loss of transaction fees. However, transactions sent to the blockchain are not guaranteed to be executed and could even be reversed after execution due to chain reorganization. Such non-determinism in the transaction execution is unique to blockchain. DApp developers may fail to perform the on-chain-off-chain synchronization accurately due to their lack of familiarity with the complex transaction lifecycle. In this work, we investigate the challenges of synchronizing on-chain and off-chain data in Ethereum-based DApps. We present two types of bugs that could result in inconsistencies between the on-chain and off-chain layers. To help detect such on-chain-off-chain synchronization bugs, we introduce a state transition model to guide the testing of DApps and propose two effective oracles to facilitate the automatic identification of bugs. We build the first testing framework, DArcher, to detect on-chain-off-chain synchronization bugs in DApps. We have evaluated DArcher on 11 popular real-world DApps. DArcher achieves high precision (99.3%), recall (87.6%), and accuracy (89.4%) in bug detection and significantly outperforms the baseline methods. It has found 15 real bugs in the 11 DApps. So far, six of the 15 bugs have been confirmed by the developers, and three have been fixed. These promising results demonstrate the usefulness of DArcher.