FY 1989 scientific and technical reports, articles, papers, and presentations

A compendium of bibliographic references to papers presented by Marshall Space Flight Center (MSFC) personnel and contractors during FY 1989 is provided. The papers include formal NASA technical reports, memoranda, papers which were published in technical journals, and presentations by MSFC personnel. The formal NASA technical reports and memoranda have abstracts included. Sources for obtaining these documents are also included

NCPV FY 1998 Annual Report

This report summarizes the in-house and subcontracted research and development (R and D) activities under the National Center for Photovoltaics (NCPV) from October 1, 1997 through September 30, 1998 (FY 1998). The NCPV is part of the U.S. Department of Energy's (DOE's) National Photovoltaics Program, as described in the DOE National Photovoltaics Program Plan for 1996-2000. The mission of the DOE National Photovoltaics Program is to make PV a significant part of the domestic economy--as an industry and as an energy resource. The two primary goals of the national program are to (1) maintain the U.S. industry's world leadership in research and technology development and (2) help the U.S. industry remain a major, profitable force in the world market. The NCPV provides leadership and support to the national program toward achieving its mission and goals

A User-aware Intelligent Refactoring for Discrete and Continuous Software Integration

Successful software products evolve through a process of continual change. However, this process may weaken the design of the software and make it unnecessarily complex, leading to significantly reduced productivity and increased fault-proneness. Refactoring improves the software design while preserving overall functionality and behavior, and is an important technique in managing the growing complexity of software systems. Most of the existing work on software refactoring uses either an entirely manual or a fully automated approach. Manual refactoring is time-consuming, error-prone and unsuitable for large-scale, radical refactoring. Furthermore, fully automated refactoring yields a static list of refactorings which, when applied, leads to a new and often hard to comprehend design. In addition, it is challenging to merge these refactorings with other changes performed in parallel by developers. In this thesis, we propose a refactoring recommendation approach that dynamically adapts and interactively suggests refactorings to developers and takes their feedback into consideration. Our approach uses Non-dominated Sorting Genetic Algorithm (NSGAII) to find a set of good refactoring solutions that improve software quality while minimizing the deviation from the initial design. These refactoring solutions are then analyzed to extract interesting common features between them such as the frequently occurring refactorings in the best non-dominated solutions. We combined our interactive approach and unsupervised learning to reduce the developer’s interaction effort when refactoring a system. The unsupervised learning algorithm clusters the different trade-off solutions, called the Pareto front, to guide the developers in selecting their region of interests and reduce the number of refactoring options to explore. To reduce the interaction effort, we propose an approach to convert multi-objective search into a mono-objective one after interacting with the developer to identify a good refactoring solution based on their preferences. Since developers may want to focus on specific code locations, the ”Decision Space” is also important. Therefore, our interactive approach enables developers to pinpoint their preference simultaneously in the objective (quality metrics) and decision (code location) spaces. Due to an urgent need for refactoring tools that can support continuous integration and some recent development processes such as DevOps that are based on rapid releases, we propose, for the first time, an intelligent software refactoring bot, called RefBot. Our bot continuously monitors the software repository and find the best sequence of refactorings to fix the quality issues in Continous Integration/Continous Development (CI/CD) environments as a set of pull-requests generated after mining previous code changes to understand the profile of developers. We quantitatively and qualitatively evaluated the performance and effectiveness of our proposed approaches via a set of studies conducted with experienced developers who used our tools on both open source and industry projects.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/154775/1/Vahid Alizadeh Final Dissertation.pdfDescription of Vahid Alizadeh Final Dissertation.pdf : Dissertatio

Example-based model refactoring using heuristic search

Software maintenance is considered the most expensive activity in software systems development: more than 80% of the resources are devoted to it. During the maintenance activities, software models are very rarely taken into account. The evolution of these models and the transformations that manipulate them are at the heart of model-driven engineering (MDE). However, as the source code, the model changes and tends to become increasingly complex. These changes generally have a negative impact on the quality of models and they cause damage to the software. In this context, refactoring is the most used technique to maintain an adequate quality of these models. The refactoring process is usually done in two steps: the detection of elements of the model to correct (design defects), then the correction of these elements. In this thesis, we propose two main contributions related to detection and correction of defects in class diagrams. The first contribution aims to automate the design defect detection. We propose to adapt genetic algorithms (e.g., genetic programming) to detect parts of the model that may correspond to design defects. The second contribution concerns the automation of the correction of these design defects. We propose to adapt three heuristic methods to suggest refactorings: 1. A single-objective optimization method based on structural similarities between a given model (i.e., the model to be refactored) and a set of examples of models (i.e., models that have undergone some refactorings); 2. An interactive single-objective optimization method based on structural similarity and the opinion of the designer; and 3. A multi-objective optimization method that maximizes both the structural and semantic similarities between the model under study and the models in the set of examples. All the proposed methods were implemented and evaluated on models generated from existing open-source projects and the obtained results confirm their efficiency

Information resources management, 1984-1989: A bibliography with indexes

This bibliography contains 768 annotated references to reports and journal articles entered into the NASA scientific and technical information database 1984 to 1989

Uma metodologia de elicitação de requisitos de software baseada na teoria da atividade

Orientador : Beatriz Mascia DaltriniTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoDoutorad