Hardware-assisted virtualization represents a pillar technology for large-scale clusters and cloud-based applications. Hardware faults are still frequent as technology advances, potentially resulting in serious reliability concerns. This paper introduces COSMOS, a fault injection framework tailored for testing hardware-assisted hypervisors. By exploiting nested virtualization, COSMOS does not require instrumentation of the target and enables the assessment of multiple hypervisors. We performed an extensive fault injection campaign to assess popular hardware-assisted hypervisors like KVM, Xen, and Jailhouse. The results show a non-negligible percentage of non–fail-stop behaviors, with notable differences in hypervisors’ ability to log failures and prevent fault propagation with a timely recovery.

Several techniques have been proposed to accurately predict software defects. These techniques generally exploit characteristics of the code artefacts (e.g., size, complexity, etc.) and/or of the process adopted during their development and maintenance (e.g., the number of developers working on a component) to spot out components likely containing bugs. While these bug prediction models achieve good levels of accuracy, they mostly ignore the major role played by human-related factors in the introduction of bugs. Previous studies have demonstrated that focused developers are less prone to introduce defects than non-focused developers. According to this observation, software components changed by focused developers should also be less error prone than components changed by less focused developers. We capture this observation by measuring the scattering of changes performed by developers working on a component and use this information to build a bug prediction model. Such a model has been evaluated on 26 systems and compared with four competitive techniques. The achieved results show the superiority of our model, and its high complementarity with respect to predictors commonly used in the literature. Based on this result, we also show the results of a “hybrid” prediction model combining our predictors with the existing ones.

Developers search source code frequently during their daily tasks, to find pieces of code to reuse, to find where to implement changes, etc. Code search based on text retrieval (TR) techniques has been widely used in the software engineering community during the past decade. The accuracy of the TR-based search results depends largely on the quality of the query used. We introduce Refoqus, an Eclipse plugin which is able to automatically detect the quality of a text retrieval query and to propose reformulations for it, when needed, in order to improve the results of TR-based code search. A video of Refoqus is found online at http://www.youtube.com/watch?v=UQlWGiauyk4.