Smart contract comment generation has gained traction as a means to improve code comprehension and maintainability in blockchain systems. However, evaluating the quality of generated comments remains a challenge. Traditional metrics such as BLEU and ROUGE fail to capture domain-specific nuances, while human evaluation is costly and unscalable. In this paper, we present evalSmarT, a modular and extensible framework that leverages large language models (LLMs) as evaluators. The system supports over 400 evaluator configurations by combining approximately 40 LLMs with 10 prompting strategies. We demonstrate its application in benchmarking comment generation tools and selecting the most informative outputs. Our results show that prompt design significantly impacts alignment with human judgment, and that LLM-based evaluation offers a scalable and semantically rich alternative to existing methods.ResourcesVideo Demo: https://youtu.be/HXS_Yiszoz4Code and Data: https://anonymous.4open.science/r/SC_code_summarization-4653

This paper investigates the various technical and non-technical tools and techniques that software developers use to build and disseminate crypto mining apps on Android devices. Our study of 346 potential Android mining apps, collected between April 2019 and May 2022, has revealed the presence of more than ten mining apps on the Google Play Store, with at least half of those still available at the time of writing this (June 2022). We observed that many of those mining apps do not conceal their usage of the device's resource for mining which is considered a violation of the store's policies for developers. We estimate that more than ten thousand users have run mining apps downloaded directly from the Google Play Store, which puts the supposedly “stringent” vetting process into question. Furthermore, we prove that covert mining apps tend to be embedded into supposedly free versions of premium apps or pose as utility apps that provide valuable features to users. Finally, we empirically demonstrate that cryptojacking apps' resource consumption and malicious behavior could be insignificant. We presume that typical users, even though they might be running a mobile antivirus solution, could execute a mining app for an extended period without being alerted. We expect our results to inform the various actors involved in the security of Android devices against the lingering threat of cryptojacking and help them better assess the problem.