Comparing host and target environments for distributed Ada programs

The Ada programming language provides a means of specifying logical concurrency by using multitasking. Extending the Ada multitasking concurrency mechanism into a physically concurrent distributed environment which imposes its own requirements can lead to incompatibilities. These problems are discussed. Using distributed Ada for a target system may be appropriate, but when using the Ada language in a host environment, a multiprocessing model may be more suitable than retargeting an Ada compiler for the distributed environment. The tradeoffs between multitasking on distributed targets and multiprocessing on distributed hosts are discussed. Comparisons of the multitasking and multiprocessing models indicate different areas of application

Knowledge Transfer and Quality Practices in the Implementation of a Sourcing Capability Model

This study adopts a knowledge transfer framework to examine the implementation and assimilation of a process improvement program for outsourcing service providers. Our theoretical model identifies the factors affecting knowledge transfer during both the initial implementation stage and the subsequent stage of full assimilation of improved outsourcing processes into organizational practice. We evaluate our theoretical model using detailed archival data collected on the implementation of an outsourcing capability model in the offshore delivery center of a large service provider. Findings indicate that knowledge transfer characteristics affect the time to implement the improved processes in the delivery center, but do not significantly relate to the likelihood of full assimilation. We also find an unexpected curvilinear relationship between implementation time and assimilation success such that processes with very low or very high implementation times are more likely to be fully assimilated

Insect Bio-inspired Neural Network Provides New Evidence on How Simple Feature Detectors Can Enable Complex Visual Generalization and Stimulus Location Invariance in the Miniature Brain of Honeybees

This work was supported by a Human Frontier Science Program Grant RGP0022/2014 to LC and Queen Mary University of London Scholarship to MR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Toward Quantitative Process Management With Exploratory Data Analysis

The Capability Maturity Model for Software is a model for building organizational capability that has been widely adopted in the software community and beyond. The Software CMM is a five-level model that prescribes process improvement priorities for software organizations. Level 4 in the CMM focuses on using quantitative techniques, particularly statistical techniques, for controlling the software process. In statistical process control terms, this means eliminating assignable (or special) causes of variation. Organizations beginning to use quantitative management typically begin by "informally stabilizing" their process. This paper describes typical questions and issues associated with the exploratory data analysis involved in initiating quantitative process management. Introduction The Capability Maturity Model (CMM) for Software [Paulk95], developed by the Software Engineering Institute (SEI) at Carnegie Mellon University, is a model for building organizational capability th..

Effective CMM-Based Process Improvement

The Capability Maturity Model SM for Software developed by the Software Engineering Institute has had a major influence on software process and quality improvement around the world. Although the CMM SM has been widely adopted, there remain many misunderstandings about how to use it effectively for business-driven software process improvement. This paper discusses how to use the CMM correctly and effectively. It also discusses aspects of successful process improvement efforts that are not explicitly addressed by the CMM, but which are critical to achieving business and process improvement goals