When developing mobile apps, programmers rely heavily on standard API frameworks and libraries. However, learning and using those APIs is often challenging due to the fast-changing nature of API frameworks for mobile systems, the complexity of API us
ages, the insufficiency of documentation, and the unavailability of source code examples. In this paper, we propose a novel approach to learn API usages from bytecode of Android mobile apps. Our core contributions include: i) ARUS, a graph-based representation of API usage scenarios; ii) HAPI, a statistical, generative model of API usages; and iii) three algorithms to extract ARUS from apps bytecode, to train HAPI based on method call sequences extracted from ARUS, and to recommend method calls in code completion engines using the trained HAPI. Our empirical evaluation suggests that our approach can learn useful API usage models which can provide recommendations with higher levels of accuracy than the baseline n-gram model.
User reviews of mobile apps often contain complaints or suggestions which are valuable for app developers to improve user experience and satisfaction. However, due to the large volume and noisy-nature of those reviews, manually analyzing them for use
ful opinions is inherently challenging. To address this problem, we propose MARK, a keyword-based framework for semi-automated review analysis. MARK allows an analyst describing his interests in one or some mobile apps by a set of keywords. It then finds and lists the reviews most relevant to those keywords for further analysis. It can also draw the trends over time of those keywords and detect their sudden changes, which might indicate the occurrences of serious issues. To help analysts describe their interests more effectively, MARK can automatically extract keywords from raw reviews and rank them by their associations with negative reviews. In addition, based on a vector-based semantic representation of keywords, MARK can divide a large set of keywords into more cohesive subsets, or suggest keywords similar to the selected ones.
