XDebloat: Towards Automated Feature-Oriented App Debloating

Existing programming practices for building Android apps mainly follow the “one-size-fits-all” strategy to include lots of functions and adapt to most types of devices. However, this strategy can result in software bloat and many serious issues, such as slow download speed, and large attack surfaces. Existing solutions cannot effectively debloat an app as they either lack flexibility or require human efforts. This work proposes a novel feature-oriented debloating approach and builds a prototype, named
XDebloat
, to automate this process in a flexible manner. First, We propose three feature location approaches to mine features in an app. XDebloat supports feature location approaches at a fine granularity. It also makes the feature location results editable. Second, XDebloat considers several Android-oriented issues (i.e., callbacks) to perform a more precise analysis. Third, XDebloat supports two major debloating strategies: pruning-based debloating and module-based debloating. We evaluate XDebloat with 200 open-source and 1,000 commercial apps. The results show that XDebloat can successfully remove components from apps or transform apps into on-demand modules within 10 minutes. For the
pruning-based
debloating strategy, on average, XDebloat can remove 32.1% code from an app. For the
module-based
debloating strategy, XDebloat can help developers build instant apps or app bundles automatically.