With the introduction of smart contacts, Ethereum has become one of the most popular blockchain networks. In the wake of its popularity, an increasing number of Ethereum-based software have been developed. However, the carbon emissions resulting from these software has been pointed out as a global issue. It is necessary to reduce the energy consumed by these software to reduce carbon emissions. Recently, most studies have focused on smart contracts and proposed energy-efficient methods for the development of carbon friendly Ethereum networks. However, in addition to smart contracts, the energy used by client software in Ethereum networks should also be reviewed. This is because the client software performs all functions occurring in the Ethereum network, including smart contracts. Therefore, energy bugs that waste energy in Ethereum client software should be investigated and solved. The first task to enable this is to build an energy bug benchmark of Ethereum client software. This study introduces ECench, an energy bug benchmark of Ethereum client software. ECench includes 507 energy buggy commits from 7 series of client software that are officially operated in the Ethereum network. We carefully collected and manually reviewed them for cleaner commits. A key strength of our benchmark is that it provides eight energy wastage categories, which can serve as a cornerstone for researchers to identify energy waste codes. ECench can provide a valuable starting point for studies on energy reduction and carbon reduction in Ethereum.

A growing interest in deep learning (DL) has instigated a concomitant rise in DL-related software (DLSW). Therefore, the importance of DLSW quality has emerged as a vital issue. Simultaneously, researchers have found DLSW more complicated than traditional SW and more difficult to debug owing to the black-box nature of DL. These studies indicate the necessity of automatic debugging techniques for DLSW. Although several validated debugging techniques exist for general SW, no such techniques exist for DLSW. There is no standard bug benchmark to validate these automatic debugging techniques. In this study, we introduce a novel bug benchmark for DLSW, Denchmark, consisting of 4,577 bug reports from 193 popular DLSW projects, collected through a systematic dataset construction process. These DLSW projects are further classified into eight categories: framework, platform, engine, compiler, tool, library, DL-based application, and others. All bug reports in Denchmark contain rich textual information and links with bug-fixing commits, as well as three levels of buggy entities, such as files, methods, and lines. Our dataset aims to provide an invaluable starting point for the automatic debugging techniques of DLSW.

Cloud computing allows users to use only a Web browser to receive computing services via the Internet. Users only need to pay for the services they actually use. It appears that a wide adoption of cloud computing in the foreseeable future is inevitable, and its adoption will bring about a sea change in the pricing and distribution practices for both software and hardware. There are, however, various issues that will impede adoption of cloud computing. Most of them can be solved. We discuss the status of cloud computing today and various adoption issues. We also provide a market prognosis.