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Android malware has been in an increasing trend in recent years due to the pervasiveness of Android operating system. Android malware is installed and run on the smartphones without explicitly prompting the users or without the user's permission, and it poses great threats to users such as the leakage of personal information and advanced fraud. To address these threats, various techniques are proposed by researchers and practitioners. Static analysis is one of these techniques, which is widely applied to Android malware detection and can detect malware quickly and prohibit malware before installation. To provide a clarified overview of the latest work in Android malware detection using static analysis, we perform a systematic literature review by identifying 98 studies from January 2014 to March 2020. Based on the features of applications, we first divide static analysis in Android malware detection into four categories, which include Android characteristic-based method, opcode-based method, program graph-based method, and symbolic execution-based method. Then we assess the malware detection capability of static analysis, and we compare the performance of different models in Android malware detection by analyzing the results of empirical evidence. Finally, it is concluded that static analysis is effective to detect Android malware. Moreover, there is a preliminary result that neural network model outperforms the non-neural network model in Android malware detection. However, static analysis still faces many challenges. Thus, it is necessary to derive some novel techniques for improving Android malware detection based on the current research community. Moreover, it is essential to establish a unified platform that is used to evaluate the performance of a series of techniques in Android malware detection fairly. INDEX TERMS Android malware detection, static analysis, systematic literature review.

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In crowdsourced software testing, inspecting the large number of test reports is an overwhelming but inevitable software maintenance task. In recent years, to alleviate this task, many text-based test-report classification and prioritization techniques have been proposed. However in the mobile testing domain, test reports often consist of more screenshots and shorter descriptive text, and thus text-based techniques may be ineffective or inapplicable. The shortage and ambiguity of natural-language text information and the well defined screenshots of activity views within mobile applications motivate our novel technique based on using image understanding for multi-objective test-report prioritization. In this paper, by taking the similarity of screenshots into consideration, we present a multi-objective optimization-based prioritization technique to assist inspections of crowdsourced test reports. In our technique, we employ the Spatial Pyramid Matching (SPM) technique to measure the similarity of the screenshots, and apply the natural-language processing technique to measure the distance between the text of test reports. Furthermore, to validate our technique, an experiment with more than 600 test reports and 2500 images is conducted. The experimental results show that imageunderstanding techniques can provide benefit to test-report prioritization for most applications.

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