Many organizations develop software systems using open source software (OSS), which is risky due to the high possibility of losing support. Contributors are critical for the survival of OSS projects, but very few new contributors remain with OSS projects to become long-time contributors (LTCs). Identification of factors that contribute to become an LTC can help OSS project owners utilize limited resources to retain new contributors. In this paper, we investigate whether we can effectively predict new contributors to OSS repositories becoming long time contributors based on repository and contributor meta-data collected from GitHub. We construct a dataset containing 70,899 observations from 888 most popular repositories with 56,766 contributors. Each observation represents a contributor who joined the repository and is categorized as either an LTC or a non-LTC, depending on whether their project tenure is longer than 3 years. Each observation has 31 features that are calculated using the information of the new contributor and the repository when a new contributor joins the project. We build several machine learning models, including naive Bayes, k-nearest neighbor, logistic regression, decision tree, and random forest to predict LTC validated using 10-fold cross-validation. We compare our best model with state of the art model in terms of precision, recall, F1-score, Matthews correlation coefficient (MCC), and area under the curve (AUC). In 10-fold cross-validation, the precision, recall, F1-score, MCC, and AUC of our best model (random forest) are 0.695, 0.079, 0.140, 0.226, and 0.913, respectively. These values are 27.29%, 92.68%, 86.67%, 56.94%, and 0.55%, respectively better than the best baseline state of the art model (random forest). Compared to state of the art models, the models built using our approach use less than 50% features (31 vs 63), have no wait time of one month after the contributor joins to predict future LTC status, and produce better results.

The development of complex self-adaptive systems has accelerated rapidly over the past decade, led by the Department of Defense, which has sought to develop and field military systems, such as unmanned aerial vehicles and unmanned ground vehicles, with elevated levels of autonomy to accomplish their mission with reduced funding and manpower. As their role increases, such systems must be able to adapt and learn, and make nondeterministic decisions. To field such systems requires extensive testing, evaluation, verification, and validation-a challenging task. To address this, we apply a novel systems perspective to develop a framework to predict future system performance based on the complexity of the operating environment using newly introduced complexity measures and learned costs. In this paper, we consider an autonomous military ground robot navigating in complex off-road environments. Using our model and experimental data from Defense Advanced Research Projects Agency-led experiments, we demonstrate the accuracy with which our model can predict system performance and then validate our model against other experimental results.

Minimizing the attack surface of a system and introducing diversity into a system are two effective ways to improve system security. However, determining how to include diversity in a system without increasing the attack surface more than necessary is a difficult problem, requiring knowledge about the system characteristics, operating environment, and available permutations that is generally not available prior to system deployment. We propose viewing a system's components, interfaces, and communication channels as a set of states and actions that can be analyzed using a sequential decision making process, and using a multi-objective reinforcement learning algorithm to learn a set of policies that minimize a system's attack surface and execute those policies to obtain configuration diversity while a system is operating. We describe a methodology for designing a system such that its components and behaviors can be translated into a multi-objective Markov Decision Process, demonstrate the use of multi-objective reinforcement learning to learn a set of optimal policies using three different multi-objective reinforcement learning algorithms in the context of an online file sharing application, and show that our multi-objective temporal difference afterstate algorithm outperforms the alternatives for the example problem.

The Advanced Telecommunications Computing Architecture (AdvancedTCA) standard for information technology (IT) equipment has been proposed as ideal for use in the extreme environments experienced by military units, designed specifically to meet the extremes of high temperatures. Despite the fact that AdvancedTCA components are designed specifically to provide an overall availability (AO) in excess of 0.99999 and delivered ready-made to meet requirements for modularity and scalability under the Modular Open Systems Approach (MOSA), the Department of Defense has been fairly slow to adopt AdvancedTCA as a standard for its IT programs. At least one of the reasons for this slow adoption is that AdvancedTCA servers generally perform slower than commodity IT servers, which is thought to result in poor system performance. After modeling an enterprise IT environment and simulating network traffic using the OPNET Modeler tool, the authors demonstrated that there is no performance degradation in using AdvancedTCA servers for the consolidation effort in comparison to commodity IT equipment. The authors used analysis of variance and Kruskal-Wallis multivariate data analysis techniques to show that there is no significant degradation in the overall performance of the system when using AdvancedTCA servers. In order to conduct this comparison, the authors developed a system-level performance benchmark (system goodput GS) to show that individual component benchmarks are not accurate predictors of system-level performance. The benefits of AdvancedTCA in terms of ruggedization, quality, cost savings, MOSA compliance, and increased AOfar outpace any perceived performance gap.

This paper aims to explore the premise that without proper identification of the engineering generational knowledge transfer boundaries, there will be a significant loss of engineering knowledge. In order to share or transfer pertinent engineering knowledge at a pace equivalent to the increase in engineering retirements, the engineering generational knowledge transfer boundary must be clearly identified and defined. Identifying these boundaries will enable the strategic transfer of engineering and architectural knowledge (STEAK) across generational boundaries. Existing knowledge transfer boundary frameworks, models and methodologies will be explored to clearly identify the engineering generational knowledge transfer boundaries and create a STEAK Model. Relevant knowledge management methodologies can also be incorporated and used to categorize a set of core techniques to be used in this knowledge management process. The methodologies used to develop the STEAK Model will be aimed at bringing knowledge management benefits to an engineering organization. This model will be used to identify and illustrate a knowledge transfer structure that integrates engineering generational knowledge. This “inherent approach” will also be used to evaluate the implications associated with the loss of required knowledge held by exiting engineers if this knowledge is not imparted to incoming inexperienced engineers.

Purpose This research aims to examine the relationship between information security strategy and organization performance, with organizational capabilities as important factors influencing successful implementation of information security strategy and organization performance. Design/methodology/approach Based on existing literature in strategic management and information security, a theoretical model was proposed and validated. A self‐administered survey instrument was developed to collect empirical data. Structural equation modeling was used to test hypotheses and to fit the theoretical model. Findings Evidence suggests that organizational capabilities, encompassing the ability to develop high‐quality situational awareness of the current and future threat environment, the ability to possess appropriate means, and the ability to orchestrate the means to respond to information security threats, are positively associated with effective implementation of information security strategy, which in turn positively affects organization performance. However, there is no significant relationship between decision making and information security strategy implementation success. Research limitations/implications The study provides a starting point for further research on the role of decision‐making in information security. Practical implications Findings are expected to yield practical value for business leaders in understanding the viable predisposition of organizational capabilities in the context of information security, thus enabling firms to focus on acquiring the ones indispensable for improving organization performance. Originality/value This study provides the body of knowledge with an empirical analysis of organization's information security capabilities as an aggregation of sense making, decision‐making, asset availability, and operations management constructs.

Recursive Markov-modulated Poisson process (MMPP) parameter estimation is performed by adapting an approach for hidden Markov model estimation developed by Krishnamurthy and Moore. Explicit expressions are developed for functions used in the recursion. The resulting approach is compared to a recursive MMPP estimation algorithm developed by Lindgren and Holst. Numerical results are provided which demonstrate the applicability of the approach for estimation of interrupted Poisson processes.