Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

A goal model for crowdsourced software engineering

Crowdsourced Software Engineering (CSE) is the act of undertaking any external software engineering tasks by an undefined, potentially large group of online workers in an open call format. Using an open call, CSE recruits global online labor to work on various types of software engineering tasks, such as requirements extraction, design, coding and testing. The field is rising rapidly and touches various aspects of software engineering. CSE has grown significance in both academy and industry. Despite of the enormous usage and significance of CSE, there are many open challenges reported by various researchers. In order to overcome the challenges and realizing the full potential of CSE, it is highly important to understand the concrete advantages and goals of CSE. In this paper, we present a goal model for CSE, to understand the real environment of CSE, and to explore the aspects that can somehow overcome the aforementioned challenges. The model is designed using RiSD, a method for building Strategic Dependency (SD) models in the i* notation, applied in this work using iStar2.0. This work can be considered useful for CSE stakeholders (Requesters, Workers, Platform owners and CSE organizations).Peer ReviewedPostprint (published version

GTTC Future of Ground Testing Meta-Analysis of 20 Documents

National research, development, test, and evaluation ground testing capabilities in the United States are at risk. There is a lack of vision and consensus on what is and will be needed, contributing to a significant threat that ground test capabilities may not be able to meet the national security and industrial needs of the future. To support future decisions, the AIAA Ground Testing Technical Committees (GTTC) Future of Ground Test (FoGT) Working Group selected and reviewed 20 seminal documents related to the application and direction of ground testing. Each document was reviewed, with the content main points collected and organized into sections in the form of a gap analysis current state, future state, major challenges/gaps, and recommendations. This paper includes key findings and selected commentary by an editing team

Principles in Patterns (PiP) : User Acceptance Testing of Course and Class Approval Online Pilot (C-CAP)

The PiP Evaluation Plan documents four distinct evaluative strands, the first of which entails an evaluation of the PiP system pilot (WP7:37 – Systems & tool evaluation). Phase 1 of this evaluative strand focused on the heuristic evaluation of the PiP Course and Class Approval Online Pilot system (C-CAP) and was completed in December 2011. Phase 2 of the evaluation is broadly concerned with "user acceptance testing". This entails exploring the extent to which C-CAP functionality meets users' expectations within specific curriculum design tasks, as well as eliciting data on C-CAP's overall usability and its ability to support academics in improving the quality of curricula. The general evaluative approach adopted therefore employs a combination of standard Human-Computer Interaction (HCI) approaches and specially designed data collection instruments, including protocol analysis, stimulated recall and pre- and post-test questionnaire instruments. This brief report summarises the methodology deployed, presents the results of the evaluation and discusses their implications for the further development of C-CAP

Optimization of fuzzy analogy in software cost estimation using linguistic variables

One of the most important objectives of software engineering community has been the increase of useful models that beneficially explain the development of life cycle and precisely calculate the effort of software cost estimation. In analogy concept, there is deficiency in handling the datasets containing categorical variables though there are innumerable methods to estimate the cost. Due to the nature of software engineering domain, generally project attributes are often measured in terms of linguistic values such as very low, low, high and very high. The imprecise nature of such value represents the uncertainty and vagueness in their elucidation. However, there is no efficient method that can directly deal with the categorical variables and tolerate such imprecision and uncertainty without taking the classical intervals and numeric value approaches. In this paper, a new approach for optimization based on fuzzy logic, linguistic quantifiers and analogy based reasoning is proposed to improve the performance of the effort in software project when they are described in either numerical or categorical data. The performance of this proposed method exemplifies a pragmatic validation based on the historical NASA dataset. The results were analyzed using the prediction criterion and indicates that the proposed method can produce more explainable results than other machine learning methods.Comment: 14 pages, 8 figures; Journal of Systems and Software, 2011. arXiv admin note: text overlap with arXiv:1112.3877 by other author