Android is a fast-evolving operating system for mobile phones. The API specifications of the Android framework constantly change due to the release of new versions. As a result, compatibility issues arise when developers are unable to adapt their apps to such Android framework changes. Such compatibility issues have been recognized as a major challenge of app developers. To help resolve this challenge, in this thesis, we empirically investigated how the Android framework changes can induce various types of compatibility issues. Moreover, we propose a series of solutions to automatically detect and repair compatibility issues in Android apps. Specifically, this thesis focuses on the following two aspects:

(1) Compatibility issues in XML configuration files (Configuration Compatibility Is...[ Read more ]

Android is a fast-evolving operating system for mobile phones. The API specifications of the Android framework constantly change due to the release of new versions. As a result, compatibility issues arise when developers are unable to adapt their apps to such Android framework changes. Such compatibility issues have been recognized as a major challenge of app developers. To help resolve this challenge, in this thesis, we empirically investigated how the Android framework changes can induce various types of compatibility issues. Moreover, we propose a series of solutions to automatically detect and repair compatibility issues in Android apps. Specifically, this thesis focuses on the following two aspects:

(1) Compatibility issues in XML configuration files (Configuration Compatibility Issues). XML configuration files are widely used in Android apps to render user interfaces (UIs) and provide essential information for execution. However, configuration compatibility issues can be incurred when the processing of these configuration files is inconsistent across different Android API levels. From the perspective of app developers, the manual effort of issue detection can be non-trivial given the large number of XML configuration files in a typical Android app. Moreover, there is no universal solution available to resolve configuration compatibility issues and app developers need to diagnose the issues and resolve them case by case. To bridge such gaps, we conducted a systematic study by analyzing 196 real-world issues collected from 43 popular apps. Specifically, we first identified common patterns of Android framework code changes that induce such configuration compatibility issues. We propose ConfDroid, which automatically extract issue-detection rules from the Android framework changes. Furthermore, we empirically studied the common strategies adopted by app developers to repair configuration compatibility issues. With the empirical finding, we propose ConfFix, which is driven by the knowledge of how an XML configuration file is handled inconsistently in different versions of the Android framework and generates patches to eliminate such inconsistencies. The evaluation results show that all the above-proposed techniques outperform the state-of-the-art methods and receive positive feedback from app developers.

(2) Compatibility issues induced by Android framework APIs (API-induced Compatibility Issues). The implementation of Android apps relies on the Android framework APIs. The APIs on which Android apps depend may undergo changes, which result in API-Induced Compatibility Issues (AIC issues for short). In this thesis, we aim at studying automated detection and repair of AIC issues in Android apps. We first conduct research on callback compatibility issues, which are a major type of AIC issues. The Android apps respond to system events (e.g., click events) by overriding callback APIs in the Android framework. However, the timing of invoking the callback APIs may change with the update of the Android version, resulting in callback compatibility issues. Such issues mainly affect the apps’ control flow and data flow. In this thesis, we collected 100 real callback compatibility issues and then investigated how the Android framework changes can induce such issues. We further design CIDER, which facilitates automated issue detection with modelling the execution order of callback APIs across Android framework versions. The evaluation shows that CIDER outperforms the state-of-the-art approaches with high precision. For the AIC issue repair, existing tools are driven a set of issue-fixing templates refined from examples of other developers. However, these approaches are not applicable in practice as they require the code under repair to be syntactically similar to the issue-fixing templates. Recent advances in GPT models have shown their potential in repairing software bugs by leveraging knowledge from a large corpus of training dataset on the Internet. Motivated by this, we conducted an empirical study to evaluate the effectiveness and limitations of state-of-the-art GPT models in fixing AIC issues. The results show that GPT models can repair AIC issues to some extent, but there is still room for improvement in their repair capabilities. The results of our study also indicate the need for useful information from both the Android framwork and app context to improve the capabilities of GPT models in repairing AIC issues.