Motivation: In modern IT systems, the increasing demand for computational power is tightly coupled with ever higher energy consumption. Traditionally, energy efficiency research has focused on reducing energy consumption at the hardware level. Nevertheless, the software itself provides numerous opportunities for improving energy efficiency. Goal: Given that energy efficiency for IT systems is a rising concern, we investigate existing work in the area of energy-aware software development and identify open research challenges. Our goal is to reveal limitations, features, and tradeoffs regarding energy-performance for software development and provide insights on existing approaches, tools, and techniques for energy-efficient programming. Method: We analyze and categorize research work mostly extracted from top-tier conferences and journals concerning energy efficiency across the software development lifecycle phases. Results: Our analysis shows that related work in this area has focused mainly on the implementation and verification phases of the software development lifecycle. Existing work shows that the use of parallel and approximate programming, source code analyzers, efficient data structures, coding practices, and specific programming languages can significantly increase energy efficiency. Moreover, the utilization of energy monitoring tools and benchmarks can provide insights for the software practitioners and raise energy-awareness during the development phase.

Context: Databases are an integral element of enterprise applications. Similarly to code, database schemas are also prone to smells - best practice violations. Objective: We aim to explore database schema quality, associated characteristics and their relationships with other software artifacts. Method: We present a catalog of 13 database schema smells and elicit developers' perspective through a survey. We extract embedded sql statements and identify database schema smells by employing the DbDeo tool which we developed. We analyze 2925 production-quality systems (357 industrial and 2568 well-engineered open-source projects) and empirically study quality characteristics of their database schemas. In total, we analyze 629 million lines of code containing more than 393 thousand sql statements. Results: We find that the index abuse smell occurs most frequently in database code, that the use of an orm framework doesn't immune the application from database smells, and that some database smells, such as adjacency list, are more prone to occur in industrial projects compared to open-source projects. Our co-occurrence analysis shows that whenever the clone table smell in industrial projects and the values in attribute definition smell in open-source projects get spotted, it is very likely to find other database smells in the project. Conclusion: The awareness and knowledge of database smells are crucial for developing high-quality software systems and can be enhanced by the adoption of better tools helping developers to identify database smells early.

Cloud computing relies on virtualization technologies to provide computer resource elasticity and scalability. Despite its benefits, virtualization technologies come with serious concerns in terms of security. Although existing work focuses on specific vulnerabilities and attack models related to virtualization, a systematic analysis of known vulnerabilities for different virtualization models, including hypervisor-based and container-based solutions is not present in the literature. In this paper, we present an overview of the existing known vulnerabilities for hypervisor and container solutions reported in the CVE database and classified under CWE categories. Given the vulnerability identification and categorization, we analyze our results with respect to different virtualization models and license schemes (open source/commercial). Our findings show among others that hypervisors and containers share common weaknesses with most of their vulnerabilities reported in the category of security features.