Mobile app developers rely heavily on standard API frameworks and libraries. However, learning API usages is often challenging due to the fast-changing nature of API frameworks for mobile systems and the insufficiency of API documentation and 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 HAPI, a statistical model of API usages and three algorithms to extract method call sequences from apps' bytecode, to train HAPI based on those sequences, and to recommend method calls in code completion using the trained HAPIs. Our empirical evaluation shows that our prototype tool can effectively learn API usages from 200 thousand apps containing 350 million method sequences. It recommends next method calls with top-3 accuracy of 90% and outperforms baseline approaches on average 10--20%.

Mobile app reviews often contain useful user opinions like bug reports or suggestions. However, looking for those opinions manually in thousands of reviews is inefective and time- consuming. In this paper, we propose PUMA, an automated, phrase-based approach to extract user opinions in app reviews. Our approach includes a technique to extract phrases in reviews using part-of-speech (PoS) templates; a technique to cluster phrases having similar meanings (each cluster is considered as a major user opinion); and a technique to monitor phrase clusters with negative sentiments for their outbreaks over time. We used PUMA to study two popular apps and found that it can reveal severe problems of those apps reported in their user reviews.

Mobile apps often rely heavily on standard API frameworks and libraries. However, learning to use those APIs is often challenging due to the fast-changing nature of API frameworks and the insufficiency of documentation and code examples. This paper introduces DroidAssist, a recommendation tool for API usages of Android mobile apps. The core of DroidAssist is HAPI, a statistical, generative model of API usages based on Hidden Markov Model. With HAPIs trained from existing mobile apps, DroidAssist could perform code completion for method calls. It can also check existing call sequences to detect and repair suspicious (i.e. unpopular) API usages.

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 useful 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.

Mobile app reviews often contain useful user opinions for app developers. However, manual analysis of those reviews is challenging due to their large volume and noisynature. This paper introduces MARK, a supporting tool for review analysis of mobile apps. With MARK, an analyst can describe her interests of one or more apps via a set of keywords. MARK then lists the reviews most relevant to those keywords for further analyses. It can also draw the trends over time of the selected keywords, which might help the analyst to detect sudden changes in the related user reviews. To help the analyst describe her interests more effectively, MARK can automatically extract and rank the keywords by their associations with negative reviews, divide a large set of keywords into more cohesive subgroups, or expand a small set into a broader one.