NDLI: OASIS: prioritizing static analysis warnings for Android apps based on app user reviews

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OASIS: prioritizing static analysis warnings for Android apps based on app user reviews

Content Provider	ACM Digital Library
Author	Cheung, Shing-Chi Wei, Lili Liu, Yepang
Abstract	Lint is a widely-used static analyzer for detecting bugs/issues in Android apps. However, it can generate many false warnings. One existing solution to this problem is to leverage project history data (e.g., bug fixing statistics) for warning prioritization. Unfortunately, such techniques are biased toward a project’s archived warnings and can easily miss newissues. Anotherweakness is that developers cannot readily relate the warnings to the impacts perceivable by users. To overcome these weaknesses, in this paper, we propose a semantics-aware approach, OASIS, to prioritizing Lint warnings by leveraging app user reviews. OASIS combines program analysis and NLP techniques to recover the intrinsic links between the Lint warnings for a given app and the user complaints on the app problems caused by the issues of concern. OASIS leverages the strength of such links to prioritize warnings. We evaluated OASIS on six popular and large-scale open-source Android apps. The results show that OASIS can effectively prioritize Lint warnings and help identify new issues that are previously-unknown to app developers. .
Starting Page	672
Ending Page	682
Page Count	11
File Format	PDF
ISBN	9781450351058
DOI	10.1145/3106237.3106294
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Android lint App user reviews Warning prioritization Static analysis Concept graph Natural language processing
Content Type	Text
Resource Type	Article

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