24/7: Drone Operations and the Distributed Work of War

How does waging war effectively fade into the background for most Americans, even as it is one of the most defining aspects of the United States’ actions and priorities, both domestically and internationally? This dissertation takes up one dimension of this question by ethnographically engaging with a particular mode of contemporary US war making that involves the deployment of drones, large and high-altitude aerial vehicles, remotely controlled from within the United States. Based on fieldwork conducted over fourteen months between 2010 and 2015 within the US with communities involved in the deployment, planning, or assessment of Air Force drone operations, a primary contribution of the dissertation is to refocus critical discourses around drones through the lens of labor and the work entailed in war. By examining the divisions of labor implicated in ongoing drone warfare, a wider set of questions and implications takes shape about the nature of contemporary American war and where different kinds of responsibilities and modes of normalization lie. The dissertation begins by arguing that the distributions of action and control that characterize drone operations are neither obvious nor necessary, but rather have taken hold only in the context of specific historical conditions of possibility. These conditions are what enable drone operations to be seen as an effective and ideal form of US military engagement, and involve interwoven developments in post-World War II military command and control theory, digital data, global information networks, and a reliance on legal frameworks that render state violence justified. The dissertation also examines the discrepancies between the imagined capacities of “unmanned” and “autonomous” drones and the current practices that constitute and maintain these technologies, which must be continually managed and constructed as effective and legitimate actors through professionalized military discourses and practices. The second half of the dissertation, more ethnographic in focus, examines how drone operations are implicated in changing conceptions of military service and military-civilian distinctions. Through an examination of the tensions and controversies that have arisen around drone pilots, the dissertation presents how Air Force pilots and commanders involved in drone operations construct and position the value of drone operations as meaningful and honorable military service. The analysis demonstrates that while officers put forward the value of their work as professional and altruistic service, at the same time, an irreconcilable tension exists because the military labor of drone operations bears increasing similarity to other forms of contemporary civilian work, characterized by the language of compensation, flexibility, and in/security. The dissertation concludes by proposing the concept of the warzone as a way to encompass all the places in which war occurs, its consequences on the battlefield, but also its sites of execution and the range of people, places, and practices that are implicated in the ongoing conduct of war. The dissertation demonstrates that the increasing deployment of drone operations is a contributing factor to the seeming invisible state of war for the majority of Americans. However, this is not necessarily because war is being conducted at a distance, as most journalists and scholars propose. Rather it is because war is being conducted, sometimes literally, in Americans’ backyards, close-by in the United States, in ways that are obfuscated or rendered merely mundane

Software defect prediction: do different classifiers find the same defects?

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio