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  • Influence
MAPO: Mining and Recommending API Usage Patterns
TLDR
The results show that using MAPO, programmers produce code with fewer bugs when facing relatively complex API usages, comparing with using the two state-of-the-art code search tools.
WHYPER: Towards Automating Risk Assessment of Mobile Applications
TLDR
WHYPER, a framework using Natural Language Processing (NLP) techniques to identify sentences that describe the need for a given permission in an application description, demonstrates great promise in using NLP techniques to bridge the semantic gap between user expectations and application functionality, further aiding the risk assessment of mobile applications.
Parseweb: a programmer assistant for reusing open source code on the web
TLDR
An approach that takes queries of the form "Source object type → Destination object type" as input, and suggests relevant method-invocation sequences that can serve as solutions that yield the destination object from the source object given in the query is developed.
An approach to detecting duplicate bug reports using natural language and execution information
TLDR
The experimental results show that the approach can detect 67%-93% of duplicate bug reports in the Firefox bug repository, compared to 43%-72% using natural language information alone.
Where do developers log? an empirical study on logging practices in industry
TLDR
This study systematically study the logging practices of developers in industry, with focus on where developers log, and demonstrates the high accuracy of up to 90% F-Score in predicting where to log.
AppContext: Differentiating Malicious and Benign Mobile App Behaviors Using Context
TLDR
This work introduces AppContext, an approach of static program analysis that extracts the contexts of security-sensitive behaviors to assist app analysis in differentiating between malicious and benign behaviors.
MAPO: mining API usages from open source repositories
TLDR
An API usage mining framework and its supporting tool called MAPO, which leverages the existing source code search engines to gather relevant source files and conducts data mining and the preliminary results show that the framework is practical for providing informative and succinct API usage patterns.
A Grey-Box Approach for Automated GUI-Model Generation of Mobile Applications
TLDR
This work presents a novel grey-box approach for automatically extracting a model of a given mobile app, which can efficiently extract compact yet reasonably comprehensive models of high quality for such apps.
Mining succinct and high-coverage API usage patterns from source code
TLDR
This paper proposes two quality metrics (succinctness and coverage) for mined usage patterns, and proposes a novel approach called Usage Pattern Miner (UP-Miner) that mines succinct and high-coverage usage patterns of API methods from source code.
Reliability Engineering
TLDR
The three articles in this special issue illustrate current trends in reliability engineering, with a focus on software engineering and the connected world.
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