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The Amazon Alexa marketplace is the largest Voice Personal Assistant (VPA)platform with over 100,000 voice applications (i.e., skills) published to theskills store. In an effort to maintain the quality and trustworthiness ofvoice-apps, Amazon Alexa has implemented a set of policy requirements to beadhered to by third-party skill developers. However, recent works reveal theprevalence of policy-violating skills in the current skills store. Tounderstand the causes of policy violations in skills, we first conduct a userstudy with 34 third-party skill developers focusing on whether they are awareof the various policy requirements defined by the Amazon Alexa platform. Ouruser study results show that there is a notable gap between VPA's policyrequirements and skill developers' practices. As a result, it is inevitablethat policy-violating skills will be published.  To prevent the inflow of new policy-breaking skills to the skills store fromthe source, it is critical to identify potential policy violations at thedevelopment phase. In this work, we design and develop SkillScanner, anefficient static code analysis tool to facilitate third-party developers todetect policy violations early in the skill development lifecycle. To evaluatethe performance of SkillScanner, we conducted an empirical study on 2,451 opensource skills collected from GitHub. SkillScanner effectively identified 1,328different policy violations from 786 skills. Our results suggest that 32% ofthese policy violations are introduced through code duplication (i.e., codecopy and paste). In particular, we found that 42 skill code examples frompotential Alexa's official accounts (e.g., "alexa" and "alexa-samples" onGitHub) contain policy violations, which lead to 81 policy violations in otherskills due to the copy-pasted code snippets from these Alexa's code examples.

SkillScanner: Detecting Policy-Violating Voice Applications Through Static Analysis at the Development Phase